Labeled Multi-Bernoulli Filter for Track-Before-Detect Bearing-Only Tracking using an Autonomous Underwater Vehicle

This paper considers an autonomous underwater vehicle (AUV) equipped with a sensor array to detect the presence of moving targets and estimate their relative locations. We propose a track-before-detect labeled multi-Bernoulli filter, which directly utilizes the signals received from the sensor array as the measurement in the form of a sample-covariance matrix (SCM). Using the unthresholded sensor data avoids creating bearing measurements and thus alleviates the challenges in the presence of multiple targets and high levels of background noise. The SCM measurement is affected by multiple targets in an additive fashion, which causes multi-dimensional integrals in measurement updating. We employ a change of variables and complex inverse-Wish art approximations to derive the closed form solutions, which ensures the filter is computationally tractable. Moreover, sequential Monte Carlo implementation is provided, complemented by an adaptive birth procedure based on the current SCM measurement. Simulation results demonstrate the improved performance of the proposed filter in challenging scenarios of low signal-to-noise ratio, small number of snapshots, trajectory crossing, and fluctuating received signal power.