Maximum Likelihood Probabilistic Data Association Multi-Hypothesis Tracker Applied to Multistatic Sonar Data Sets

摘要

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 applyML-PMHT to twomultistatic 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.