Shifted Rayleigh filter: a novel estimation filtering algorithm for pervasive underwater passive target tracking for computation in 3D by bearing and elevation measurements

Abstract

Purpose Marine exploration is becoming an important element of pervasive computing underwater target tracking. Many pervasive techniques are found in current literature, but only scant research has been conducted on their effectiveness in target tracking. Design/methodology/approach This research paper, introduces a Shifted Rayleigh Filter (SHRF) for three-dimensional (3 D) underwater target tracking. A comparison is drawn between the SHRF and previously proven method Unscented Kalman Filter (UKF). Findings SHRF is especially suitable for long-range scenarios to track a target with less solution convergence compared to UKF. In this analysis, the problem of determining the target location and speed from noise corrupted measurements of bearing, elevation by a single moving target is considered. SHRF is generated and its performance is evaluated for the target motion analysis approach. Originality/value The proposed filter performs better than UKF, especially for long-range scenarios. Experimental results from Monte Carlo are provided using MATLAB and the enhancements achieved by the SHRF techniques are evident.