Measurement Tracking of Bearing Time Records in Low SNR Environments

The marine environment can impede accurate detection due to various noises. These combined impediments lower the signal-to-noise ratio (SNR) of the bearing measurement and increase the likelihood of undetected target intervals over entire signal processing time. When performing Target Motion Analysis (TMA) by using these bearing measurements, noises can cause discontinuities in the target trajectory, thereby degrade TMA performance. In this paper, we propose a technique for estimating the bearing measurement trajectory on a Bearing Time Records (BTR) diagram in low SNR environments. The proposed method called the integrated highest probability data association - amplitude information (IHPDA-AI) algorithm applies probabilistic distributions of signal amplitudes of the target and clutter to evaluate the data association probability calculation unlike the existing HPDA (Highest Probability Data Association) algorithm. The performance of the proposed algorithm is validated through Monte Carlo simulations. The proposed technique is assessed by comparing the performance of the integrated probabilistic strongest neighbor filter with m-validated measurement (IPSNF-m) algorithm and the cumulative IPSNF-m (CPSNF-m) algorithm using simulated BTR diagrams in various SNR environments.