Passive Target Motion Analysis With Own-Ship Location Uncertainty in the Presence of Non-Gaussian Sensor Noise

Passive target motion analysis (TMA) is traditionally performed using angle-only measurements, which requires the own-ship to execute a manoeuvre to make the tracking system observable. These manoeuvres are burdensome for the naval community. In contrast, this work explores underwater TMA by incorporating time delay and Doppler frequency measurements along with angle data, eliminating the need for own-ship manoeuvre and improving estimation accuracy. Measurement noises are assumed to follow a non-Gaussian distribution, and maximum correntropy (MC)-based Bayesian filtering framework is adopted to solve the problem. Furthermore, the own-ship's position is inherently uncertain due to navigation errors, and this work addresses the uncertainty by modifying the measurement noise covariance matrix within the estimation framework. Simulation results demonstrate that the proposed methodology achieves improved tracking performance in terms of root mean square error (RMSE) and $\%$ track loss compared to existing state-of-the-art MC Kalman filtering approaches.