Track-Before-Detect Bearings-Only Localization Performance in Complex Passive Sonar Scenarios: A Case Study

Track-before-detect (TkBD) algorithms have been shown to greatly abate measurement-to-track association (MTA) challenges. These simplifications are aptly relevant for reducing operator workload in deployed sonar systems that require a human in the loop. This paper presents a case study of a passive bearings-only target motion analysis TkBD algorithm using two complex data sets: one simulated and one obtained at sea, which are representative of scenario characteristics typically encountered with advanced operational sonar systems. The simulated data set exhibits crossing contacts of differing signal-to-noise ratios and short data gaps. The at-sea data, obtained from an open ocean exercise, are used to assess performance under realistic conditions that exhibit high levels of clutter, varying background noise levels, fading contacts, a high bearing-rate contact, and large data gaps. The TkBD algorithm is designed for the general complexities encountered in the examined data sets and employs a particle filter that defines its likelihood function as the accumulation of raster bin values from the output of the sonar beamformer conditioned on the particle's state trajectory. For the scenarios examined the localization performance of the algorithm is encouraging. The paper briefly reviews the chief motivation for using a TkBD approach, which is to circumvent MTA.