Long-Short Forward-Reverse Cubature Kalman Filtering for Underwater Moving Target Element Estimation

Bearings-Only Target Motion Analysis (BOTMA) is a core technology for Unmanned Underwater Vehicles (UUVs) to perform covert underwater missions. Recursive Bayesian filtering algorithms, as the mainstream solution for BOTMA, exhibit inherent limitations—particularly excessive dependence on prior target information. In practical underwater operations, prior information about targets is often scarce due to the requirement of covertness for observation platforms, leading to large initial estimation errors in nonlinear Kalman filtering algorithms and consequently degraded accuracy of target motion parameter calculation. To address this issue, this study proposes an optimization approach based on the Cubature Kalman Filter (CKF) algorithm, incorporating forward-backward filtering. First, data obtained from the forward recursion of the CKF algorithm is back-propagated to the initial time to optimize the filtering initial values. Then, an appropriate stride is selected to perform filtering using an “alternating forward-reverse” strategy. This approach effectively resolves the issue of poor target motion parameter calculation accuracy caused by insufficient prior information. Experimental results validate the reliability and effectiveness of this algorithm.