Abstract
In this paper, a fuzzy logic-based recursive least squares filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random characteristics. In the proposed filter, fuzzy logic is applied in the standard recursive least squares filter (RLSF) by the design of a set of fuzzy if-then rules. Given the observation residual and the heading change in the current prediction, these rules are used to determine the magnitude of the fading factor of RLSF. The proposed filter has an advantage in which the restrictive assumptions of statistical models for process noise, measurement noise, and motion models are relaxed. Moreover, it does not need a maneuver detector when tracking a maneuvering target. The performance of FLRLSF is evaluated by using a simulation and real test experiment, and it is found to be better than those of the traditional RLSF, the fuzzy adaptive α-β filter (FAα-βF), and the hybrid Kalman filter in tracking accuracy.
Highlights
Maneuvering target tracking (MTT) is always a critical problem in target tracking area [1,2,3,4,5]
5 Experimental results and analysis A stimulation experiment and a real test experiment have been carried out to evaluate the performance of the fuzzy logic-based recursive least squares filter (FLRLSF) method in comparison with the other three existing methods, the traditional recursive least squares filter (RLSF) [18], fuzzy adaptive α-β filter (FAα-βF) [16], and hybrid Kalman filter (HKF) [14] for MTT
6 Conclusion In this paper, considering the properties and drawbacks of the traditional adaptive filters, FLRLSF is proposed for MTT in the situation of observations with unknown random characteristics
Summary
Maneuvering target tracking (MTT) is always a critical problem in target tracking area [1,2,3,4,5]. In the literature on MTT in sensor network, its survey primarily consists of target dynamic models, observation models and techniques, decision-based methods, multiple-model methods, and nonlinear filtering methods. Structured adaptive filters, usually with more computation, require sufficient prior knowledge such as all possible motion models of a moving target. They are less suitable for real situations with limited prior knowledge. Parametric adaptive filters, generally with less computation, describe maneuver characteristics as unknown random parameters with certain probability distribution functions (pdf) and estimate maneuver models jointly by both the parameters and the target
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