Partitioning algorithms in underwater passive target tracking

Abstract

Abshnnct -The area of underwater passive target tracking has received considerable attention in the past decades, due to both its theoretical interest and its practical importance in several applications. Many powerful tools fhm the fields of signal processing, image processing, and estimation theory have been brought to bear for the solution of the passive target tracking problem. Among the latter, techniques based on Kalman filtering and techniques based on partitioning filters have been successfully used. The approaches based on Kalman filtering do not usually perform adequately when facing a maneuvering target, whereas the techuiques based on partitioning filters perform very satisfactorily in the same case. In this paper, four approaches to the problem of underwater passive target tracking, based on the partitioning theory are reviewed and discussed. Their performance is also checkd against that of Kalman filtering-based approaches in both maneuvering and nonmaneuvering targets scenaria.

A Survey of Manoeuvring Target Tracking Methods

“…A comparison of input/onset-time estimation with the interacting multiple model algorithm appeared in [13] (with follow-up comments contained in [52] and [65]), demonstrating the comparable performance of the IMM algorithm at lower computational complexity. Several methods based on partitioning (multiple model) filters have been reviewed in the context of bearings-only tracking for non-manoeuvring and manoeuvring targets in [71], and their performance compared with that of a single Kalman filter technique.…”