ML–PDA and ML–PMHT: Comparing Multistatic Sonar Trackers for VLO Targets Using a New Multitarget Implementation

Abstract

The maximum-likelihood probabilistic data association (ML-PDA) tracker and the maximum-likelihood probabilistic multihypothesis (ML-PMHT) tracker are tested in their capacity as algorithms for very low observable (VLO) targets (meaning 6-dB postsignal processing or even less) and are then applied to five synthetic benchmark multistatic active sonar scenarios featuring multiple targets, multiple sources, and multiple receivers. Both methods end up performing well in situations where there is a single target or widely spaced targets. However, ML-PMHT has an inherent advantage over ML-PDA in that its likelihood ratio (LR) has a simple multitarget formulation, which allows it to be implemented as a true multitarget tracker. This formulation, presented here for the first time, gives ML-PMHT superior performance for instances where multiple targets are closely spaced with similar motion dynamics.Index Terms-Bistatic, expectation maximization (EM), low observable, maximum likelihood, maximum-likelihood probabilistic data association (ML-PDA), maximum-likelihood probabilistic multihypothesis (ML-PMHT), multistatic, multitarget, multitarget ML-PMHT, sonar, tracking.

Distributed compressed sensing based joint detection and tracking for multistatic radar system

“…JOMP and OSGA algorithms, in reconstructing a sparse vector representing the state space with high resolution. Secondly, the proposed distributed compressed sensing based joint detection and tracking algorithm is compared to the TBD algorithm [34,43], the maximum-likelihood probabilistic data association (ML-PDA) algorithm [20,10], and the maximumlikelihood probabilistic multihypothesis (ML-PMHT) algorithm [35], in tracking multiple targets (including prominent and weak targets) in a 3D Cartesian coordinate system.…”

“…The performance of the proposed distributed compressed sensing based joint detection and tracking (DCS-JDT) algorithm is further compared to the TBD algorithm [34,43], the ML-PDA algorithm [20,10], and the ML-PMHT algorithm [35]. The following metrics are evaluated to verify the performance of the algorithms: the global root mean square error (RMSE) in position and the execution time (ET).…”

Consensus-based distributed adaptive target tracking in camera networks using Integrated Probabilistic Data Association

“…For tracking in clutter, conventional state estimation techniques cannot be used because several measurements are available at every scan, and at most, one measurement can arise from the target. The elegant Probabilistic Data Association (PDA) methodology has been successfully applied in this case, including a variety of very successful applications to radars, sonars, and electro-optic systems, e.g., [9][10][11][20][21][22][23][24][25]. This methodology is based on the implicit strong assumption that the target is always perceivable.…”

“…In this sense, it is straightforward to construct adaptive algorithms based, e.g., on the extended Kalman filter (like in [8]), on the unscented Kalman filter, and particle filters. It is also possible to apply the proposed methodology within the framework of the ML-PDA method applicable in real time [20][21][22][23][24][25]. The adaptation strategy should again cope mainly with the limited FoVs (or sensing ranges).…”

“…Regarding the computational requirements, complexity of all the methods is, in general, O n 3 . However, the algorithms IPDA-KCF and IPDA-ICF require computation of matrix inversions (size n × n, see (25) and (26), respectively), which is not the case with the IPDA-ACF algorithm. Therefore, in addition to favorable communication requirements (which are of crucial importance for the efficiency of large sensor networks), the proposed algorithm exhibits also the lowest computational demands.…”