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.

No abstract

Many acoustic channels suffer from interference which is neither narrowband nor impulsive. This relatively long duration partial band interference can be particularly detrimental to system performance. In operational networks, many "dropped" messages are lost due to partial band interference which corrupts different portions of the received signal depending on the relative position of the interferers, information source and receivers due to the slow speed of propagation. We survey recent work in interference mitigation as background motivation to develop a spatial diversity receiver for use in underwater networks and compare this multi-receiver interference mitigation strategy with a recently developed single receiver interference mitigation algorithm using experimental data collected from the underwater acoustic network at the Atlantic Underwater Test and Evaluation Center. The results indicate that both mitigation strategies are effective: parameterized interference cancellation is most effective at moderate SIRs whereas spatial diversity reconstruction is effective and realizes the most gain at high SIRs. We also apply the parametized interference cancellation to the problem of estimating mutually interfering waveforms when it is desired to know both time domain signals and find that it effectively extracts both mutually interfering linear frequency modulated (LFM) and orthogonal frequency division multiplexing (OFDM) waveforms.

The Maximum Likelihood Probabilistic Multi-Hypothesis tracker (ML-PMHT) is an algorithm that works well against low-SNR targets in an active multistatic framework with multiple transmitters and multiple receivers. The ML-PMHT likelihood ratio formulation allows for multiple targets as well as multiple returns from any given target in a single scan, which is realistic in a multi-receiver environment where data from different receivers is combined together. Additionally, the likelihood ratio can be optimized very easily and rapidly with the expectation-maximization (EM) algorithm. Here, we apply ML-PMHT to two multistatic data sets: the TNO blind 2008 data set and the Metron 2009 data set. Results are compared with previous work that employed the Maximum Likelihood Probabilistic Data Assocation (ML-PDA) tracker, an algorithm with a different assignment algorithm and as a result a different likelihood ratio formulation.

No abstract