37. V. Cevher, P. Boufounos, R. G. Baraniuk, A. C. Gilbert, and M. J. Strauss, “Near-optimal Bayesian localization via incoherence and sparsity,” IPSN 2009, San Francisco, CA, 13-16 April 2009.
    Abstract      BiBTeX      Paper: PDF

    Source localization using a network of sensors is a classical problem with applications in tracking, habitat monitoring, etc. A solution to this estimation problem must satisfy a number of competing resource constraints, such as estimation accuracy, communication and energy costs, signal sampling requirements and computational complexity. This paper exploits recent developments in sparse approximation and compressive sensing to efficiently perform localization in a sensor network. We introduce a Bayesian framework for the localization problem and provide sparse approximations to its optimal solution. By exploiting the spatial sparsity of the posterior density, we demonstrate that the optimal solution can be computed using fast sparse approximation algorithms. We show that exploiting the signal sparsity can reduce the sensing and computational cost on the sensors, as well as the communication bandwidth. We further illustrate that the sparsity of the source locations can be exploited to decentralize the computation of the source locations and reduce the sensor communications even further. We also discuss how recent results in 1-bit compressive sensing can impact the sensor communications by transmitting only the timing information relevant to the problem. Finally, we develop a computationally efficient algorithm for bearing estimation using a network of sensors with provable guarantees.

    @inproceedings{IEEE_IPSN09_Cevher,
    author = "V. Cevher and P. Boufounos and R. G. Baraniuk and A. C. Gilbert and M. J. Strauss",
    title = "Near-optimal Bayesian localization via incoherence and sparsity",
    booktitle = "IPSN",
    address= "San Francisco, CA",
    year = "13--16 April 2009",}
36. C. Hegde, M. F. Duarte, and V. Cevher, “Compressive sensing recovery of spike trains using a structured sparsity model,” SPARS'09, Saint-Malo, France, 6-9 April 2009.  
    Abstract      BiBTeX      Paper: PDF

    The theory of Compressive Sensing (CS) exploits a well-known concept used in signal compression – sparsity – to design new, efficient techniques for signal acquisition. CS theory states that for a length-N signal x with sparsity level K, M = O(K log(N/K)) random linear projections of x are sufficient to robustly recover x in polynomial time. However, richer models are often applicable in real-world settings that impose additional structure on the sparse nonzero coefficients of x. Many such models can be succinctly described as a union of K-dimensional subspaces. In recent work, we have developed a general approach for the design and analysis of robust, efficient CS recovery algorithms that exploit such signal models with structured sparsity.

    We apply our framework to a new signal model which is motivated by neuronal spike trains. We model the firing process of a single Poisson neuron with absolute refractoriness using a union of subspaces. We then derive a bound on the number of random projections M needed for stable embedding of this signal model, and develop a algorithm that provably recovers any neuronal spike train from M measurements. Numerical experimental results demonstrate the benefits of our model-based approach compared to conventional CS recovery techniques.

    @inproceedings{SPARS09_Hegde,
    author = "C. Hegde, M. F. Duarte, and V. Cevher",
    title = "Compressive sensing recovery of spike trains using a structured sparsity model",
    booktitle = "SPARS'09",
    address= "Saint-Malo, France",
    year = "13--16 April 2009",}
35. M. F. Duarte, C. Hegde, V. Cevher, and Richard G. Baraniuk “Recovery of Compressible Signals in Unions of Subspaces,” CISS, Baltimore, MD, 18-20 March 2009.  
    Abstract      BiBTeX      Paper: PDF

    Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquisition of sparse or compressible signals; instead of taking periodic samples, we measure inner products with M < N random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. Initial research has shown that by leveraging stronger signal models than standard sparsity, the number of measurements required for recovery of an structured sparse signal can be much lower than that of standard recovery. In this paper, we introduce a new framework for structured compressible signals based on the unions of subspaces signal model, along with a new sufficient condition for their recovery that we dub the restricted amplification property (RAmP). The RAmP is the natural counterpart to the restricted isometry property (RIP) of conventional CS. Numerical simulations demonstrate the validity and applicability of our new framework using wavelet-tree compressible signals as an example.

    @inproceedings{CISS09_Duarte,
    author = "M. F. Duarte, C. Hegde, V. Cevher, and Richard G. Baraniuk",
    title = "Recovery of Compressible Signals in Unions of Subspaces",
    booktitle = "CISS",
    address= "Baltimore, MD",
    year = "18--20 March 2009",}
34. V. Cevher, M. F. Duarte, C. Hegde, and R. G. Baraniuk,  “Sparse signal recovery using Markov random fields,” NIPS, Vancouver, B.C., Canada, 8-11 December 2008.
    Abstract      BiBTeX      Paper: PDF

    Compressive Sensing (CS) combines sampling and compression into a single sub-Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphical model. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based reconstruction algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.

    @inproceedings{IEEE_NIPS08_Cevher,
    author = "V. Cevher and and M. F. Duarte and C. Hegde and R. G. Baraniuk",
    title = "Sparse signal recovery using Markov random fields",
    booktitle = "NIPS",
    address= "Vancouver, B.C., Canada",
    year = "8--11 December 2008",}
33. V. Cevher, A. C. Sankaranarayanan, M. F. Duarte, D. Reddy, R. G. Baraniuk, and R. Chellappa, “Compressive sensing for background subtraction,” ECCV, Marseille, France, 12-18 October 2008.
    Abstract      BiBTeX      Paper: PDF

    Compressive sensing (CS) is an emerging field that provides a framework for image recovery using sub-Nyquist sampling rates. The CS theory shows that a signal can be reconstructed from a small set of random projections, provided that the signal is sparse in some basis, e.g., wavelets. In this paper, we describe a method to directly recover background subtracted images using CS and discuss its applications in some communication constrained multi-camera computer vision problems. We show how to apply the CS theory to recover object silhouettes (binary background subtracted images) when the objects of interest occupy a small portion of the camera view, i.e., when they are sparse in the spatial domain. We cast the background subtraction as a sparse approximation problem and provide different solutions based on convex optimization and total variation. In our method, as opposed to learning the background, we learn and adapt a low dimensional compressed representation of it, which is sufficient to determine spatial innovations; object silhouettes are then estimated directly using the compressive samples without any auxiliary image reconstruction. We also discuss simultaneous appearance recovery of the objects using compressive measurements. In this case, we show that it may be necessary to reconstruct one auxiliary image. To demonstrate the performance of the proposed algorithm, we provide results on data captured using a compressive single-pixel camera. We also illustrate that our approach is suitable for image coding in communication constrained problems by using data captured by multiple conventional cameras to provide 2D tracking and 3D shape reconstruction results with compressive measurements.

    @inproceedings{IEEE_ECCV08_Cevher,
    author = "V. Cevher and A. C. Sankaranarayanan and M. F. Duarte and D. Reddy and R. G. Baraniuk and R. Chellappa",
    title = "Compressive Sensing for Background Subtraction",
    booktitle = "ECCV",
    address= "Marseille, France",
    year = "12--18 October 2008",}
32. V. Cevher, M. F. Duarte, and R. G. Baraniuk, “Distributed target localization via spatial sparsity,” EUSIPCO 2008, Lausanne, Switzerland, 25-29 August 2008.  
    Abstract      BiBTeX      Paper: PDF

    We propose an approximation framework for distributed target localization in sensor networks. We represent the unknown target positions on a location grid as a sparse vector, whose support encodes the multiple target locations. The location vector is linearly related to multiple sensor measurements through a sensing matrix, which can be locally estimated at each sensor. We show that we can successfully determine multiple target locations by using linear dimensionality-reducing projections of sensor measurements. The overall communication bandwidth requirement per sensor is logarithmic in the number of grid points and linear in the number of targets, ameliorating the communication requirements. Simulations results demonstrate the performance of the proposed framework.

    @inproceedings{IEEE_EUSIPCO08_Cevher,
    author = "V. Cevher and M. F. Duarte and R. G. Baraniuk",
    title = "Distributed target localization via spatial sparsity",
    booktitle = "EUSIPCO",
    address= "Lausanne, Switzerland",
    year = "25--29 August 2008",}
31. V. Cevher, and L. Kaplan, “Pareto frontiers of sensor networks for localization,” IPSN 2008, St. Louis, MO, 22-24 April 2008.
    Abstract      BiBTeX      Paper: PDF

    We develop a theory to predict the localization performance of randomly distributed sensor networks consisting of various sensor modalities when only a constant active subset of sensors that minimize localization error is used for estimation. The characteristics of the modalities include measurement type (bearing or range) and error, sensor reliability, FOV, sensing range, and mobility. We show that the localization performance of a sensor network is a function of a weighted sum of the total number of each sensor modality. We also show that optimization of this weighted sum is independent of how the sensor management strategy chooses the active sensors. We combine the utility objective with other objectives, such as lifetime, coverage and reliability to determine the best mix of sensors for an optimal sensor network design. The Pareto efficient frontier of the multi objectives are obtained with a dynamic program, which also accommodates additional convex constraints.

    @inproceedings{IEEE_IPSN08_Cevher,
    author = "V. Cevher and L. Kaplan",
    title = "Pareto frontiers of sensor networks for localization",
    booktitle = "IPSN",
    address= "St. Louis, MO",
    year = "22--24 April 2008",}
30. V. Cevher, and R. G. Baraniuk, “Compressive sensing for sensor calibration,” SAM 2008 Workshop, Darmstadt, Germany, 21-23 July 2008.
    Abstract      BiBTeX      Paper: PDF

    We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an ℓ1-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.

    @inproceedings{IEEE_SAM08_Cevher,
    author = "V. Cevher and R. G. Baraniuk",
    title = "Compressive sensing for sensor calibration",
    booktitle = "SAM 2008 Workshop",
    address= "Darmstadt, Germany",
    year = "21--23 July 2008",}
29. D. Reddy, A. C. Sankaranarayanan, V. Cevher, and R. Chellappa, “Compressed sensing for multi-view tracking and 3-D voxel reconstruction,” ICIP 2008, San Diego, CA, 12-15 October 2008.
    Abstract      BiBTeX      Paper: PDF

    Compressed sensing(CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach in various multi-view estimation problems. The sparsity of the silhouette images corresponds to sparsity of object parameters (location, volume etc.) in the scene. We use random projections (compressed measurements) of the silhouette images for directly recovering object parameters in the scene coordinates. To keep the computational requirements of this recovery procedure reasonable, we tessellate the scene into a bunch of non-overlapping lines and perform estimation on each of these lines. Our method is scalable in the number of cameras and utilizes very few measurements for transmission among cameras. We illustrate the usefulness of our approach for multi-view tracking and 3-D voxel reconstruction problems.

    @inproceedings{IEEE_ICIP08-Reddy,
    author = "D. Reddy and A. C. Sankaranarayanan and V. Cevher and R. Chellappa",
    title = "Compressed sensing for multi-view tracking and 3-D voxel reconstruction",
    booktitle = "ICIP 2008",
    address= "San Diego, CA",
    year = "12--15 October 2008",}
28. V. Cevher, A. C. Sankaranarayanan, and R. Chellappa, “Factorized variational approximations for acoustic multi source localization,” ICASSP 2008, Las Vegas, NV, 30 March-4 April 2008.
    Abstract      BiBTeX      Paper: PDF

    Estimation based on received signal strength (RSS) is crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present a Gaussian approximation of the Chi distribution that is applicable to general RSS source localization problems in sensor networks. Using our Gaussian approximation, we provide a factorized variational Bayes (VB) approximation to the location and power posterior of multiple sources using a sensor network. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model. In turn, our factorized VB approximations decrease the computational complexity and provide computational robustness as the number of targets increases. Simulations are provided to demonstrate the effectiveness of the proposed approximations.

    @inproceedings{IEEE_ICASSP08-Cevher2,
    author = "V. Cevher and A. C. Sankaranarayanan and R. Chellappa",
    title = "Factorized variational approximations for acoustic multi source localization",
    booktitle = "ICASSP 2008",
    address= "Las Vegas, NV",
    year = "30 March--4 April 2008",}
27. V. Cevher, A. C. Gurbuz, J. H. McClellan, and R. Chellappa, “Compressive wireless arrays for bearing estimation of sparse sources in angle domain,” ICASSP 2008, Las Vegas, NV, 30 March-4 April 2008.
    Abstract      BiBTeX      Paper: PDF

    Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the sum of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, creating a disadvantage for their deployment and also limiting their aperture size. In this paper, we develop the signal processing algorithms for randomly deployable wireless sensor arrays that are severely constrained in communication bandwidth. We focus on the acoustic bearing estimation problem and show that when the target bearings are modeled as a sparse vector in the angle space, functions of the low dimensional random projections of the microphone signals can be used to determine multiple source bearings as a solution of an ℓ1-norm minimization problem. Field data results are shown where only 10bits of information is passed from each microphone to estimate multiple target bearings.

    @inproceedings{IEEE_ICASSP08-Cevher1,
    author = "V. Cevher and A. C. Gurbuz and J. H. McClellan and R. Chellappa",
    title = "Compressive wireless arrays for bearing estimation of sparse sources in angle domain",
    booktitle = "ICASSP 2008",
    address= "Las Vegas, NV",
    year = "30 March--4 April 2008",}
26. A. C. Gurbuz, V. Cevher, and J. H. McClellan, “A compressive beamforming method,” ICASSP 2008, Las Vegas, NV, 30 March-4 April 2008.
    Abstract      BiBTeX      Paper: PDF

    Compressive Sensing (CS) is an emerging area which uses a relatively small number of non-traditional samples in the form of randomized projections to reconstruct sparse or compressible signals. This paper considers the direction-of-arrival (DOA) estimation problem with an array of sensors using CS. We show that by using random projections of the sensor data, along with a full waveform recording on one reference sensor, a sparse angle space scenario can be reconstructed, giving the number of sources and their DOA’s. The number of projections can be very small, proportional to the number sources. We provide simulations to demonstrate the performance and the advantages of our compressive beamformer algorithm.

    @inproceedings{IEEE_ICASSP08-Gurbuz,
    author = "A. C. Gurbuz and V. Cevher and J. H. McClellan",
    title = "A compressive beamforming method",
    booktitle = "ICASSP 2008",
    address= "Las Vegas, NV",
    year = "30 March--4 April 2008",}
25. V. Cevher, R. Chellappa, and J. H. McClellan, “Gaussian approximations for energy-based detection and localization in sensor networks,” IEEE Statistical Signal Processing Workshop, Madison, WI, 26-29 August 2007.  
    Abstract      BiBTeX      Paper: PDF

    Energy-based detection and estimation are crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present novel Gaussian approximations that are applicable to general energy-based source detection and localization problems in sensor networks. Using our approximations, (i) we derive receiver operating characteristics curves and Cramer-Rao bounds, and (ii) we provide a factorized variational Bayes approximation to the location and source energy posterior for centralized or decentralized estimation. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model, which results in smaller estimation biases than the other Gaussian models typically used in the literature. We also prove that additive Gaussian noise models result in negatively biased speed estimates under the same signal assumptions, which can be circumvented by the proposed approximations.

    @inproceedings{IEEE_SSP07-Cevher,
    author = "V. Cevher and R. Chellappa and J. H. McClellan",
    title = "Gaussian approximations for energy-based detection and localization in sensor networks",
    booktitle = "IEEE Statistical Signal Processing Workshop",
    address= "Madison, WI",
    year = "26--29 August 2007",}
24. R. Velmurugan, V. Cevher, and J. H. McClellan, “Implementation of batch-based particle filters for multi-sensor tracking,” IEEE CAMSAP 2007, U.S. Virgin Islands, 12-14 December 2007.   
    Abstract      BiBTeX      Paper: PDF

    Energy-based detection and estimation are crucial in sensor networks for sensor localization, target tracking, etc. In this paper, we present novel Gaussian approximations that are applicable to general energy-based source detection and localization problems in sensor networks. Using our approximations, (i) we derive receiver operating characteristics curves and Cramer-Rao bounds, and (ii) we provide a factorized variational Bayes approximation to the location and source energy posterior for centralized or decentralized estimation. When the source signal and the sensor noise have uncorrelated Gaussian distributions, we demonstrate that the envelope of the sensor output can be accurately modeled with a multiplicative Gaussian noise model, which results in smaller estimation biases than the other Gaussian models typically used in the literature. We also prove that additive Gaussian noise models result in negatively biased speed estimates under the same signal assumptions, which can be circumvented by the proposed approximations.

    @inproceedings{IEEE_CAMSAP07-Velmurugan,
    author = "R. Velmurugan and V. Cevher and J. H. McClellan",
    title = "Implementation of batch-based particle filters for multi-sensor tracking",
    booktitle = "IEEE CAMSAP",
    address= "U.S. Virgin Islands",
    year = "12--14 December 2007",}
23. R. Velmurugan, S. Subramanian, V. Cevher, J. H. McClellan, and D. V. Anderson “Mixed-mode implementation of particle filters,” IEEE PACRIM 2007, Victoria, B.C., CA, 22-24 August 2007.
    Abstract      BiBTeX      Paper: PDF

    In this paper, we develop new mixed-mode implementations for particle filters and compare them to a digital implementation. The motivation for the mixed-mode implementation is to achieve low-power implementation of particle filters. The specific application considered is a bearings-only, single-target tracking algorithm. Specifically, we develop mixed-mode implementations that use analog components to realize nonlinear functions in the particle filter algorithm. The analog implementation of nonlinear functions use multiple-input translinear element (MITE) networks. MITEs operate in the subthreshold region and hence dissipate low-power. Simulation results for onmixed-mode implementation of the bearings-only tracker are presented. We show that of the two mixed-mode implementations, one approach dissipates almost same power as that of a digital implementation. The second approach has nearly twenty times less power dissipation, but requires careful analog design.

    @inproceedings{IEEE_PACRIM07-Velmurugan,
    author = "R. Velmurugan and S. Subramanian and V. Cevher and J. H. McClellan and D. V. Anderson",
    title = "Mixed-mode implementation of particle filters",
    booktitle = "IEEE PACRIM",
    address= "Victoria, B.C., Canada",
    year = "22-24 August 2007",}
22. V. Cevher, R. Chellappa, and J. H. McClellan, “Joint acoustic-video fingerprinting of vehicles, part I,” ICASSP 2007, Honolulu, Hawaii, 16-20 April 2007.
    Abstract      BiBTeX      Paper: PDF

    We address vehicle classification and mensuration problems using acoustic and video sensors. In this paper, we show how to estimate a vehicle’s speed, width, and length by jointly estimating its acoustic wave-pattern using a single passive acoustic sensor that records the vehicle’s drive-by noise. The acoustic wave-pattern is approximated using three envelope shape (ES) components, which approximate the shape of the received signal’s power envelope. We incorporate the parameters of the ES components along with estimates of the vehicle engine RPM and number of cylinders to create a vehicle profile vector that forms an intuitive discriminatory feature space. In the companion paper, we discuss vehicle classification and mensuration based on silhouette extraction and wheel detection, using a video sensor. Vehicle speed estimation and classification results are provided using field data.

    @inproceedings{IEEE_ICASSP07-Cevher1,
    author = "V. Cevher and R. Chellappa and J. H. McClellan",
    title = "Joint acoustic-video fingerprinting of vehicles, Part I",
    booktitle = "ICASSP 2007",
    address= "Honolulu, Hawaii",
    year = "15--20 April 2007",}
21. V. Cevher, F. Guo, A. C. Sankaranarayanan, and R. Chellappa, “Joint acoustic-video fingerprinting of vehicles, part II,” ICASSP 2007, Honolulu, Hawaii, 16-20 April 2007.
    Abstract      BiBTeX      Paper: PDF

    In this second paper, we first show how to estimate the wheelbase length of a vehicle using line metrology in video. We then address the vehicle fingerprinting problem using vehicle silhouettes and color invariants. We combine the acoustic metrology and classification results discussed in Part I with the video results to improve estimation performance and robustness. The acoustic video fusion is achieved in a Bayesian framework by assuming conditional independence of the observations of each modality. For the metrology density functions, Laplacian approximations are used for computational efficiency. Experimental results are given using field data.

    @inproceedings{IEEE_ICASSP07-Cevher2,
    author = "V. Cevher and F. Guo and A. C. Sankaranarayanan and R. Chellappa and J. H. McClellan",
    title = "Joint acoustic-video fingerprinting of vehicles, Part II",
    booktitle = "ICASSP 2007",
    address= "Honolulu, Hawaii",
    year = "15--20 April 2007",}
20. V. Cevher, F. Shah, R. Velmurugan, and J. H. McClellan, “An acoustic multi-target tracking system using random sampling consensus,” 2007 IEEE Aerospace Conference, Big Sky, Montana, 3-10 March 2007.  
    Abstract      BiBTeX      Paper: PDF

    In this paper, we present an acoustic direction-of-arrival (DOA) tracking system to track multiple maneuvering targets using a state space approach. The system consists of three blocks: beamformer, random sampling, and particle filter. The beamformer block processes the received acoustic data to output bearing batches as point statistics. The random sampling block determines temporal clustering of the bearings in a batch to determine region-of-interests (ROIs). Based on the track-before-detect approach, each ROI indicates the presence of a possible target. We describe three random sampling algorithms called RANSAC, MSAC, and NAPSAC to use in the random sampling block. The particle filter then tracks the targets via its interactions with the beamformer and the random sampling blocks. We present a computational analysis of the random sampling blocks and show tracking results with field data.

    @Inproceedings{IEEE_AESCONF07_Cevher1,
    author = "V. Cevher and R. Chellappa and F. Shah and R. Velmurugan and J. H. McClellan",
    title = "An acoustic multi-target tracking system using random sampling consensus",
    booktitle = "2007 IEEE Aerospace Conference",
    address= "Big Sky, Montana",
    year = "3--10 March 2007",}
19. M. Borkar, V. Cevher, and J. H. McClellan, “A Monte-Carlo approach for tracking mobile personnel,” 2007 IEEE Aerospace Conference, Big Sky, Montana, 3-10 March 2007.  
    Abstract      BiBTeX      Paper: PDF

    In this paper, we propose a Monte-Carlo method based on the particle filter framework to track footfall locations generated by mobile personnel using seismic arrays. While the particle proposal function follows a simple bootstrap approach, the novelty in our algorithm comes from a unique weighting strategy that takes into account the sparse nature of the seismic footfall signal and is robust against missed detections and clutter which could appear in the form of other impulsive sources or other walkers. Our weighting strategy automatically makes use of the wavefront shape, either planar or circular, and assigns weights in x-y space. Data association is built into the system, eliminating the need to explicitly associate the received footfall impulses with different walkers. Hence our algorithm is ideal for tracking multiple mobile personnel. We also demonstrate the fusion of our system with range information available by means of radar. Fusion with radar improves x-y tracking when range resolution is lost due to a large distance between the target and the seismic array leading to planar wavefronts.

    @Inproceedings{IEEE_AESCONF07_Borkar1,
    author = "M. Borkar and V. Cevher and J. H. McClellan",
    title = "A Monte Carlo approach for tracking mobile personnel",
    booktitle = "2007 IEEE Aerospace Conference",
    address= "Big Sky, Montana",
    year = "3--10 March 2007",}
18. L. Kaplan and V. Cevher, “Design considerations for heterogeneous network of bearings-only sensors using sensor management,” 2007 IEEE Aerospace Conference, Big Sky, Montana, 3-10 March 2007.
    Abstract      BiBTeX       Paper: PDF

    This paper presents the design characterization of a heterogeneous sensor network with the goal of geolocation accuracy. It is assumed that the network exploits sensor management to conserve node usage. Each available node modality is a bearings-only sensor of varying capability. The optimal mixture of modalities is discussed under the constraint of the overall network cost. Finally, simulations verify the theory and demonstrate design choices for a network consisting of two modes analogous to acoustic arrays and cameras.

    @Inproceedings{IEEE_AESCONF07_Kaplan1,
    author = "L. Kaplan and V. Cevher",
    title = "Design considerations for heterogeneous network of bearings-only sensors using sensor management",
    booktitle = "2007 IEEE Aerospace Conference",
    address= "Big Sky, Montana",
    year = "3--10 March 2007",}
17. S. Ozgur, V. Cevher, D. B. Williams, and J. H. McClellan, “Convergence Analysis for Sequential Monte Carlo Receivers in Communications Applications,” IEEE DSP Workshop 2006, Grand Teton National Park, Wyoming, 24-27 September 2006.
    Abstract      BiBTeX      Paper: PDF

    Recently, sequential Monte Carlo methods have been applied in the telecommunications field finding application in receiver design. These receivers do not require channel state information or training, making them bandwidth efficient as no communication bandwidth needs to be spent on training. The receivers are optimal in the sense that they achieve minimum symbol error rate regardless of the noise distribution, nonlinearities in the system, and distribution of the transmitted symbols. Moreover, these receivers are capable of producing soft-information outputs, which enables the designer to utilize iterative receiver architectures for near-optimal performance. In this work we investigate the convergence properties of these algorithms when utilized in various types of receivers and we quantify the convergence rate. We describe how various parameters (e.g., noise power, number of particles) and factors (e.g., dimensionality of the problem) affect the convergence rate and point out factors that should be improved first to gain speed and accuracy in the convergence.

    @inproceedings{IEEE_DSP06_Ozgur,
    author = "S. Ozgur and V. Cevher and D. B. Williams and J. H. McClellan",
    title = "Convergence Analysis for Sequential Monte Carlo Receivers in Communications Applications",
    booktitle = "IEEE DSP Workshop 2006",
    address= "Grand Teton National Park, Wyoming",
    year = "24--27 September 2006",}
16. V. Cevher, M. Borkar, and J. H. McClellan, “A joint radar-acoustic particle filter tracker with acoustic propagation delay compensation,” EUSIPCO 2006, Florence, Italy, 4-8 September 2006.   
    Abstract      BiBTeX      Paper: PDF

    In this paper, a novel particle filter tracker is presented for target tracking using collocated radar and acoustic sensors. Real-time tracking of the target's position and velocity in Cartesian coordinates is performed using batches of range and direction-of-arrival estimates. For robustness, the filter aligns the radar and acoustic data streams to account for acoustic propagation delays. The filter proposal function uses a Gaussian approximation to the full tracking posterior for improved efficiency. To incorporate the aligned acoustic data into the tracker, a two-stage weighting strategy is proposed. Computer simulations are provided to demonstrate the effectiveness of the algorithm.

    @inproceedings{EUSIPCO06-Cevher1,
    author = "V. Cevher and M. Borkar and J. H. McClellan",
    title = "A joint radar-acoustic particle filter tracker with acoustic propagation delay compensation",
    booktitle = "EUSIPCO 2006",
    address= "Florence, Italy",
    year = "4--8 September 2006",}
15. R. Velmurugan, S. Subramanian, V. Cevher, D. Abramson, K. M. Odame, J. D. Gray, H.-J. Lo, J. H. McClellan, and D. V. Anderson “On low-power analog implementations of particle filters for target tracking,” EUSIPCO 2006, Florence, Italy, 4-8 September 2006.  
    Abstract      BiBTeX      Paper: PDF

    We propose a low-power, analog and mixed-mode, implementation of particle filters. Low-power analog implementation of nonlinear functions such as exponential and arctangent functions is done using multiple-input translinear element (MITE) networks. These nonlinear functions are used to calculate the probability densities in the particle filter. A bearings-only tracking problem is simulated to present the proposed low-power implementation of the particle filter algorithm.

    @inproceedings{EUSIPCO06-Velmurugan1,
    author = "R. Velmurugan and S. Subramanian and V. Cevher and D. Abramson and K. M. Odame and J. D. Gray and H.-J. Lo and J. H. McClellan and D. V. Anderson",
    title = "On low-power analog implementations of particle filters for target tracking",
    booktitle = "EUSIPCO 2006",
    address= "Florence, Italy",
    year = "4--8 September 2006",}
14. V.Cevher, R. Velmurugan, and J. H. McClellan, “A particle filter range tracker,” ICASSP 2006, Toulouse, France, 15-19 May 2006.
    Abstract      BiBTeX      Paper: PDF

    We propose a particle filter tracker to track multiple maneuvering targets using a batch of range measurements. The state update is formulated through a locally linear motion model and the observability of the state vector is proved using geometrical arguments. The data likelihood treats the range observations as an image using template models derived from the state update equation, and incorporates the possibility of missing data as well as spurious range observations. The particle filter handles multiple targets, using a partitioned state-vector approach. The filter proposal function uses a Gaussian approximation of the full-posterior to cope with target maneuvers for improved efficiency. By treating the range measurements as images and using smoothness constraints, the particle filter is able to avoid the data association problems. Computer simulations demonstrate the performance of the tracking algorithm.

    @inproceedings{IEEE_ICASSP06-Cevher1,
    author = "V. Cevher and R. Velmurugan and J. H. McClellan",
    title = "A particle filter range tracker",
    booktitle = "ICASSP 2006",
    address= "Toulouse, France",
    year = "15--19 May 2006",}
13. M. Alam, V. Cevher, and J. H. McClellan, “Optimal experiments with seismic sensors,” ICASSP 2006, Toulouse, France, 15-19 May 2006.
    Abstract      BiBTeX      Paper: PDF

    In this paper, we consider the problem of detecting and locating buried land mines and subsurface objects by using seismic waves. We demonstrate an adaptive seismic system that maneuvers an array of receivers, according to an optimal positioning algorithm based on the theory of optimal experiments, to minimize the number of distinct measurements to localize the mine. The adaptive localization algorithm is tested using numerical model data as well as laboratory measurements performed in a facility at Georgia Tech. It is envisioned that the future systems should be able to incorporate this new method into portable mobile mine location systems.

    @inproceedings{IEEE_ICASSP06-Alam1,
    author = "M. Alam and V. Cevher and J. H. McClellan",
    title = "Optimal experiments with seismic sensors",
    booktitle = "ICASSP 2006",
    address= "Toulouse, France",
    year = "15--19 May 2006",}
12. M. Borkar, V. Cevher, and J. H. McClellan, “A Monte-Carlo method for initializing distributed tracking algorithms,” ICASSP 2006, Toulouse, France, 15-19 May 2006.
    Abstract      BiBTeX      Paper: PDF

    Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor networks. In this paper, we determine an initial probability distribution of multiple target states in a distributed manner to initialize distributed trackers. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a weighted set of discrete state realizations. The filter state vector is the target positions and velocities on the 2D plane. Our approach can determine the state vector distribution even if the individual sensors are not capable of observing it. The only condition is that the network as a whole can observe the state vector. A robust weighting strategy is formulated to account for missed detections and clutter. To demonstrate the effectiveness of the algorithm, we simulate a network with direction-of-arrival nodes and range-Doppler nodes.

    @inproceedings{IEEE_ICASSP06-Borkar1,
    author = "M. Borkar and V. Cevher and J. H. McClellan",
    title = "A Monte-Carlo method for initializing distributed tracking algorithms",
    booktitle = "ICASSP 2006",
    address= "Toulouse, France",
    year = "15--19 May 2006",}
11. V. Cevher, R. Velmurugan, and J. H. McClellan, “Multi target direction-of-arrival tracking using road priors,” 2006 IEEE Aerospace Conference, Big Sky, Montana, 4-11 March 2006.  
    Abstract      BiBTeX      Paper: PDF

    In this paper, we present a multi target particle filter DOA tracker that can incorporate road prior information at a single array node. The filter uses a batch of DOA’s to determine the state vector, based on an image template matching idea. The filter likelihood is derived with the joint probability density association principles so that no DOA measurement is associated to more than one target. The filter state update has the target DOA, the target velocity over range ratio, and the target heading parameters. We present two approaches for incorporating the road information. In the first approach, the road prior is injected at the weighting stage of the tracker, where a raised mixture Gaussian distribution, derived from the road headings at the target DOA, constraints the particles. The second  approach is based on modifying the state update function with a compound model, where a mixture of the constant velocity model and the road information is used. In this case, the filter uses an online EM algorithm to update the state vector along with the mixture components. Computer simulations demonstrate the performance of the approaches.

    @inproceedings{IEEE_AESCONF06_Cevher,
    author = "V. Cevher and R. Velmurugan and J. H. McClellan",
    title = "Multi target direction-of-arrival tracking using road priors",
    booktitle = "2006 IEEE Aerospace Conference",
    address= "Big Sky, Montana",
    year = "4--11 March 2006",}
10. V. Cevher and J. H. McClellan, “An acoustic multiple target tracker,” 2005 IEEE Statistical Signal Processing Conference, Bordeaux, France, 17-20 July 2005.
    Abstract      BiBTeX      Paper: PDF

    We propose a particle filter acoustic tracker to track multiple maneuvering targets using a state space formulation. The state update is formulated through a locally linear motion model. The observations are a batch of direction-of-arrival (DOA) estimates at various frequencies. The data likelihood incorporates the possibility of missing data as well as spurious DOA observations. By imposing smoothness constraints on the target motion, the particle filter is able to avoid the data association problems. To make the filter computationally efficient, a proposal strategy based on approximating the full posterior is employed. Computer simulations are presented to show the performance of the algorithm.

    @inproceedings{IEEE_SSP05-Cevher,
    author = "V. Cevher and J. H. McClellan",
    title = "An acoustic multiple target tracker",
    booktitle = "IEEE Statistical Signal Processing Conference",
    address= "Bordeaux, France",
    year = "17--20 July 2005",}
9. M. Borkar, V. Cevher, and J. H. McClellan, “Estimating target state distributions in a distributed sensor network using a Monte-Carlo approach,” 2005 IEEE Workshop on Machine Learning for Signal Processing, Mystic, Connecticut, 28-30 September 2005.
   Abstract      BiBTeX      Paper: PDF

   Distributed processing algorithms are attractive alternatives to centralized algorithms for target tracking applications in sensor net works. In this paper, we address the issue of determining an initial probability distribution of multiple target states in a distributed manner to initialize distributed trackers. Our approach is based on Monte-Carlo methods, where the state distributions are represented as a discrete set of weighted particles. The target state vector is the target positions and velocities in the 2D plane. Our approach can determine the state vector distribution even if the individual sensors are not capable of observing it. The only condition is that the network as a whole can observe the state vector. A robust weighting strategy is formulated to account for mis-detections and clutter. To demonstrate the effectiveness of the algorithm, we use direction-of-arrival nodes and range-Doppler nodes.

   @inproceedings{IEEE_MLSP05-Borkar,
   author = "M. Borkar and V. Cevher and J. H. McClellan",
   title = "Estimating target state distributions in a distributed sensor network using a Monte-Carlo approach",
   booktitle = "IEEE MLSP 2005",
   address= "Mystic, Connecticut",
   year = "28--30 September 2005",}
8. G. Qian, V. Cevher, A. Sankaranarayanan, J. H. McClellan, and R. Chellappa, “Vehicle tracking using acoustic and video sensors,” in Army Science Conference 2004, Orlando, 29 November-2 December 2004.
   Abstract      BiBTeX      Paper: PDF

   In target tracking, fusing multi-modal sensor data under a power-performance trade-off is becoming increasingly important. Proper fusion of multiple modalities can help in achieving better tracking performance while decreasing the total power consumption. In this paper, we present a framework for tracking a target given joint acoustic and video observations from a co-located acoustic array and a video camera. We demonstrate on field data that tracking of the direction-of-arrival of a target improves significantly when the video information is incorporated at time instants when the acoustic signal-to-noise ratio is low.

   @inproceedings{ASC2004-Qian,
   author = "G. Qian and V. Cevher and A. Sankaranarayanan and J. H. McClellan and R. Chellappa",
   title = "On low-power analog implementations of particle filters for target tracking",
   booktitle = "EUSIPCO 2006",
   address= "Florence, Italy",
   year = "4--8 September 2006",}
7. V. Cevher and J. H. McClellan, “Proposal strategies for joint state-space tracking with particle filters,” ICASSP 2005, Philadelphia, PA, 18-23 March 2005.
   Abstract      BiBTeX      Paper: PDF

   A proposal function determines the random particle support of a particle filter. When this support is distributed close to the true target density, filter’s estimation performance increases for a given number of particles. In this paper, a proposal strategy for joint state-space tracking using particle filters is given. The state-spaces are assumed Markovian and not-exact; however, each state-space is assumed to sufficiently describe the underlying phenomenon. The joint tracking is achieved by carefully placing the random support of the joint filter to where the final posterior is likely to lie. Computer simulations demonstrate improved performance and robustness of the joint state-space through the proposed strategy.

   @inproceedings{IEEE_ICASSP05-Cevher,
   author = "V. Cevher  and J. H. McClellan",
   title = "Proposal strategies for joint state-space tracking with particle filters",
   booktitle = "ICASSP 2005",
   address= "Philadelphia, PA",
   year = "18--23 March 2005",}
6. V. Cevher and J. H. McClellan, “Acoustic node calibration using helicopter sounds and Monte Carlo Markov chain methods,” IEEE DSP Workshop, Taos Ski Valley, NM, 1-4 August 2004.
   Abstract      BiBTeX      Paper: PDF

   A Monte-Carlo method is used to calibrate a randomly placed sensor node using helicopter sounds. The calibration is based on using the GPS information from the helicopter and the estimated DOA’s at the node. The related Cramer-Rao lower bound is derived and the effects of the GPS errors on the position estimates are derived. Issues related to the processing of the field data, e.g., time synchronization and data non-stationarity are discussed. The effects of the GPS errors are shown to be negligible under certain conditions. Finally, the results of the calibration on field data are given.

   @inproceedings{IEEE_DSP04_Cevher,
   author = "V. Cevher and J. H. McClellan",
   title = "Acoustic node calibration using helicopter sounds and Monte Carlo Markov chain methods",
   booktitle = "IEEE DSP Workshop",
   address= "Taos Ski Valley, NM",
   year = "1--4 August 2004",}
5. V. Cevher and J. H. McClellan, “Fast initialization of particle filters using a modified Metropolis-Hastings algorithm: Mode-Hungry approach,” ICASSP 2004, Montreal, CA, 17-21 May 2004.
   Abstract      BiBTeX      Paper: PDF

   As a recursive algorithm, the particle filter requires initial samples to track a state vector. These initial samples must be generated from the received data and usually obey a complicated distribution. The Metropolis-Hastings (M-H) algorithm is used for sampling from intractable multivariate target distributions and is well suited for the initialization problem. Asymptotically, the M-H scheme creates samples drawn from the exact distribution. For the particle filter to track the state, the initial samples need to cover only the region around its current state. This region is marked by the presence of modes. Since the particle filter only needs samples around the mode, we modify the M-H algorithm to generate samples distributed around the modes of the target posterior. By simulations, we show that this ”mode hungry” algorithm converges an order of magnitude faster than the original M-H scheme for both unimodal and multi-modal distributions.

   @inproceedings{IEEE_ICASSP04-Cevher,
   author = "V. Cevher  and J. H. McClellan",
   title = "Fast initialization of particle filters using a modified Metropolis-Hastings algorithm: Mode-Hungry approach",
   booktitle = "ICASSP 2004",
   address= "Montreal, CA",
   year = "17--21 May 2004",}
4. V. Cevher and J. H. McClellan, “Tracking of multiple wideband targets using passive sensor arrays and particle filters,” IEEE DSP Workshop, Callaway Gardens, GA, 13-16 October 2002.
   Abstract      BiBTeX      Paper: PDF

   In this paper, we present a way to track multiple maneuvering targets with varying time-frequency signatures. A particle filter is used to track targets that have constant speeds with changing heading directions. The target motion dynamics help the particle filter achieve an angular resolution otherwise not possible by the conventional beamforming techniques. Moreover, the particle filter has a built-in target association that eliminates the need for heuristic techniques commonly used in the multiple target tracking problems. Reference priors are used to derive the probability distribution function of the acoustic array outputs given the state of the multiple target states (MTS’s). Local linearization is used to approximate the importance function used in the particle filter by a Gaussian pdf. Finally, computer simulations are used to demonstrate the performance of the algorithm with synthetic data.

   @inproceedings{IEEE_DSP02_Cevher,
   author = "V. Cevher and J. H. McClellan",
   title = "Tracking of multiple wideband targets using passive sensor arrays and particle filters",
   booktitle = "IEEE DSP Workshop",
   address= "Callaway Gardens, GA",
   year = "13--16 October 2002",}
3. V. Cevher and J. H. McClellan, “2-D sensor perturbation analysis: equivalence to AWGN on array outputs,” SAM 2002 Workshop, WDC, 4-6 August 2002.
   Abstract      BiBTeX      Paper: PDF

   In this paper, the performance of a subspace beamformer, namely the multiple signal classification algorithm (MUSIC), is scrutinized in the presence of sensor position errors. Based on a perturbation model, a relationship between the array autocorrelation matrix and the source autocorrelation matrix is established. It is shown that under certain assumptions on the source signals, the Gaussian sensor perturbation errors can be modeled as additive white Gaussian noise (AWGN) for an array where sensor positions are known perfectly. This correspondence can be used to equate position errors to an equivalent signal-to-noise ratio (SNR) for AWGN in performance evaluation. Finally, Cramer-Rao bound for the position perturbations that can be computed using the Cramer-Rao bound relations for the additive Gaussian noise case at high SNR’s.

   @inproceedings{SAM02-Cevher,
   author = "V. Cevher and J. H. McClellan",
   title = "2-D sensor perturbation analysis: equivalence to AWGN on array outputs",
   booktitle = "SAM 2002",
   address= "Washington, DC",
   year = "4--6 August 2002",}
2. V. Cevher and J. H. McClellan, “Sensor array calibration via tracking with the extended Kalman filter,” ICASSP 2001, vol. 5, pp. 2817-2820, Salt Lake City, UT, May 2001.
   Abstract      BiBTeX      Paper: PDF

   Starting with a randomly distributed sensor array with unknown sensor orientations, array calibration is needed before target localization and tracking can be performed using classical triangulation methods. In this paper, we assume that the sensors are only capable of accurate direction of arrival (DOA) estimation. The calibration problem cannot be completely solved given the DOA estimates alone, since the problem is not only rotationally symmetric but also includes a range ambiguity. Our approach to calibration is based on tracking a single target moving at a constant velocity. In this case, the sensor array can be calibrated from target tracks generated by an extended Kalman filter (EKF) at each sensor. A simple algorithm based on geometrical matching of similar triangles will align the separate tracks and determine the sensor positions and orientations relative to a reference sensor. Computer simulations show that the algorithm performs well even with noisy DOA estimates at the sensors.

   @inproceedings{IEEE_ICASSP01-Cevher,
   author = "V. Cevher  and J. H. McClellan",
   title = "Sensor array calibration via tracking with the extended Kalman filter",
   booktitle = "ICASSP 2001,
   address= "Salt Lake City, UT",
   year = "May 2001",}
1. R. M. Dansereau, W. Kinsner, and V. Cevher, “Wavelet packet best basis search using generalized Renyi entropy,” IEEE CCECE 2002, vol. 2, pp. 1005-1008.
   Abstract      BiBTeX   

   This paper introduces an approach to wavelet packet best basis searches using the generalized Renyi entropy. The approach extends work by R.R. Coifman and M.V. Wickerhauser who showed how Shannon entropy can be used as an additive cost function in the wavelet packet best basis selection (see IEEE Trans. on Inform. Theory, vol.38, no.2, p.713-18, 1992). This paper also extends the idea of an additive cost function to an arithmetic mean. These extensions allow for a redefinition of additive cost functions as arithmetic means in a way consistent with the approach of Coifman and Wickerhauser. The approach using an arithmetic mean is then extended to include the geometric mean. This extension to geometric means allows us to introduce the Renyi generalized entropy as a cost function in the best basis search. These two extensions also allow the use of incomplete probability distributions, whereas Coifman and Wickerhauser's entropy based cost function is limited to complete probability distributions.

   @inproceedings{IEEE_CCECE02_Dansereau,
   author = "R. M. Dansereau and W. Kinsner and V. Cevher",
   title = "Wavelet packet best basis search using generalized Renyi entropy",
   booktitle = "IEEE CCECE 2002",}

Thesis:

V. Cevher

 A Bayesian framework for target tracking using acoustic
and image measurements,

Ph.D. thesis, Georgia Institute of Technology, 2005.