Abstract
A localization and tracking algorithm for an early-warning tracking system based on the information fusion of Infrared (IR) sensor and Laser Detection and Ranging (LADAR) is proposed. The proposed Kalman filter scheme incorporates Out-of-Sequence Measurements (OOSMs) to address long-range, high-speed incoming targets to be tracked by networked Remote Observation Sites (ROS) in cluttered environments. The Rauch–Tung–Striebel (RTS) fixed lag smoothing algorithm is employed in the proposed technique to further improve tracking accuracy, which, in turn, is used for target profiling and efficient filter initialization at the targeted platform. This efficient initialization increases the probability of target engagement by increasing the distance at which it can be effectively engaged. The increased target engagement range also reduces risk of any damage from debris of the engaged target. Performance of the proposed target localization algorithm with OOSM and RTS smoothing is evaluated in terms of root mean square error (RMSE) for both position and velocity, which accurately depicts the improved performance of the proposed algorithm in comparison with existing retrodiction-based OOSM filtering algorithms. The effects of assisted target state initialization at the targeted platform are also evaluated in terms of Time to Impact (TTI) and true track retention, which also depict the advantage of the proposed strategy.
Highlights
Detection and tracking antiship supersonic targets, such as sea-skimming missiles, are challenging problems for any countermeasure and surveillance system [1]
This paper presents the additional benefits of smoothing for the purpose of target profiling from several networked Remote Observation Sites (ROSs) along with launch point estimation of high-velocity projectiles
This situation leads to the arrival of IR measurements at the fusion center after the target trajectory state is updated with the measurements observed from
Summary
Detection and tracking antiship supersonic targets, such as sea-skimming missiles, are challenging problems for any countermeasure and surveillance system [1]. Two general techniques to update the current state in a globally optimal and suboptimal manner for solving single- and multiple-lag OOSM problems are developed in Reference [17]. In Reference [18], an algorithm is proposed to estimate target position by incorporating arbitrary lag OOSMs using grey relational analysis, which is a computationally intensive technique and involves more CPU time in case of multilag delayed observations. An optimal retrodiction-based OOSM filtering algorithm called A1 algorithm is presented in Reference [8] This technique is optimal and offers an exact solution only for a scenario where the OOSM arrives between the previous two in sequence and consecutive measurements. In this paper, existing optimal Al1 and suboptimal Bl1 OOSM filtering algorithms based on retrodiction have been proposed along with the smoothing framework to incorporate single- and multiple-lag OOSMs in a cluttered environment.
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