Sparse Approximate Solutions to Linear Systems
SIAM Journal on Computing
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
State space initiation for blind mobile terminal position tracking
EURASIP Journal on Advances in Signal Processing
High-resolution radar via compressed sensing
IEEE Transactions on Signal Processing
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
Sparse Bayesian learning for basis selection
IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Sparsity-Based Multi-Target Tracking Using OFDM Radar
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Information theory and radar waveform design
IEEE Transactions on Information Theory
Target Estimation Using Sparse Modeling for Distributed MIMO Radar
IEEE Transactions on Signal Processing
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We propose a novel sparsity-based algorithm for multiple-target tracking in a time-varying multipath environment. We develop a sparse measurement model for the received signal, by considering a finite dimensional representation of the time-varying system function which characterizes the transmission channel. The measurement model allows us to exploit the joint delay-Doppler diversity offered by the environment. We reformulate the problem of multiple-target tracking as a block support recovery problem and we derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. Using this bound, we prove that spread-spectrum waveforms are ideal candidates for signaling. We also prove that under spread-spectrum signaling, the dictionary of the sparse measurement model exhibits a special structure. We exploit this structure to develop a computationally inexpensive support recovery algorithm by projecting the received signal on to the row space of the dictionary. Numerical simulations show that tracking using proposed algorithm for support recovery performs better when compared to tracking using other sparse reconstruction algorithms and tracking using a particle filter. The proposed algorithm takes significantly less time when compared to the time taken by other methods.