Abstract
We propose a novel algorithm, state propagation based dynamic compressed sensing (SP-DCS), that uses a target dynamic model in dynamic compressed sensing (DCS) to track a fixed number of targets. To track a time-varying number of targets using raw measurements from a Doppler radar, we also propose a novel hybrid particle filter based dynamic compressed sensing (HPF-DCS) algorithm. We calculate the support set in a Bayesian framework and a particle filter approximates the posterior probability mass function (pmf) of the support set. HPF-DCS is a combination of random and deterministic sampling. In random sampling, a number of predicted existing sub-particles are sampled from the prior pmf of the existing support set to handle the scenario when targets disappear randomly at a scan time. In deterministic sampling, the new support set corresponding to newly appearing targets is calculated by solving a sparsity promoting optimization problem. Our simulation results show that the proposed algorithm can track a time-varying number of targets successfully. It also outperforms the sequential Monte Carlo based probability hypothesis density (SMC-PHD) filter, as well as the multi-mode, multi-target track before detect (MM-MT-TBD) filter.
Highlights
Most tracking algorithms use measurements obtained by thresholding raw measurements
For the first scenario we propose a novel state propagation based dynamic compressed sensing (SP-DCS) algorithm, which introduces state evolution into DCS
We propose a novel hybrid sampling algorithm, hybrid particle filter (PF) based dynamic compressed sensing (HPF-DCS), which is a combination of random and deterministic sampling
Summary
Most tracking algorithms use measurements obtained by thresholding raw measurements Examples of such algorithms are the multiple hypotheses tracking (MHT) [1]–[4]; the joint probabilistic data association filter (JPDAF) [5], [6]; the probability hypothesis density (PHD) filter [4], [7]; the cardinalized PHD (CPHD) filter [8], [9]; and the generalized labeled multi-Bernoulli (GLMB) tracker [9]. These algorithms are not suitable for tracking targets with low signal-to-noise ratio (SNR). Target detection and tracking using a high resolution radar requires wide bandwidth transmission during a short observation time, which demands expensive hardware systems
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