Fuzzy c ‐means‐based intelligent tracking algorithm for an underwater manoeuvring target

Abstract

The effort to detect a target conflicts with the covertness in anti-submarine warfare. The underwater detection rate is extremely low and sound waves are the only detection method unlike the airborne operational environment. The data of an underwater target based on noise levels are more difficult to interpret when the acceleration inputs are included. Hence, the authors propose a complementary algorithm for the tracking problem in an active sonar system. The bearing-range data of the target yield the measurement residuals, and the residuals are used as inputs to the clustering method. The clustering method estimates the acceleration and the noise level. The estimates aid in positional compensation and enhance the tracking performance. The fuzzy c-means clustering is utilised for the clustering and maintains the filter sustainable. Finally, some examples are provided to show the effectiveness of the proposed method.

Smart tracking algorithm for multi‐static sonar based on expectation maximisation

“…Third, the measurement should be processed adaptively when unknown external inputs are included in the manoeuvres of the target [18–20]. In particular, the acceleration inputs should be separated from the noise term and should be offset appropriately to improve the tracking performance [21–23]. Fourth, the dispersion of the measurement residuals should be analysed to enhance the tracking performance.…”