A high-performance target may accelerate at non-uniform rates, complete sharp turns within short time periods, thrust, roll, and pitch; which may not follow a linear model. Even though the interacting multiple model (IMM) can be considered as a multimodal approach, it still requires prior knowledge about the target model. To overcome this weakness, a fuzzy logic particle filter (FLPF) is used. It is comprised of single-input single-output; which is presented by fuzzy relational equations. A canonical-rule based form is used to express each of these fuzzy relational equations. The dynamics of the high-performance target are modeled by multiple switching (jump Markov) systems. The target may follow one-out of-seven dynamic behavior model at any time in the observation period under assumption of coordinate turn model. The FLPF has the advantage that it does not require any prior knowledge of statistical models of
process as in IMM. Moreover, it does not need any maneuver detector even when tracking a high performance target; which results in less computational complexities. By using an appropriate fuzzy overlap set, only a subset of the total number of models need to be evaluated, and these will be conditioned on acceleration values close to the estimate. This reduces the computational load compared to the fuzzy IMM (FIMM) algorithm. To achieve the whole range of maneuver variables, more models can be added without increasing the computational load as the number of models evaluated is determined only by the overlap. An example is included for visualizing the effectiveness of the proposed algorithm. Simulation results showed that the FLPF has good tracking performance and less computational load compared to the FIMM when applied to systems characterized by large scan periods.
Tracking a maneuvering target weakens the performance of predictive-model-based Bayesian state estimators (Kalman Filter). Therefore, the particle is used to track maneuverable targets instead of Kalman filter and its extensions. The particle filter proved more efficiency compared to Kalman filter and its extensions, e.g. Extended Kalman Filter (EKF) and Interacting Multiple Model (IMM). Unfortunately, due to the highly uncertainty and incompleteness of the information in a highly-maneuverable target-tracking problem, the advantage of the particle filter is weakened. Both auxiliary and smoothing particle filter were proposed to overcome this problem. In this paper, we compare the performance of both auxiliary and smoothing particle filter in tracking a highly maneuverable target. We applied both algorithms to track a maneuverable target in a multiple-sensors network. Monte Carlo simulation showed that the smoothing particle filter has a better performance when compared to auxiliary particle filter in tracking a maneuvering target.