Common target tracking algorithms, such as the Kalman Filter, assume Gaussian estimates of process and measurement noises. This Gaussian assumption does not fully support practical maneuvering target tracking. Rather, when target motion is highly dynamic, sudden maneuvers are better described by non-Gaussian noise distributions. A Kalman-Levy filter has been proposed as an improvement to the maneuvering target tracking problem. This filter models process and measurement noises using Levy distributions. While an improvement in maneuver estimation is demonstrated with the Kalman-Levy filter, it requires significant computation time and occasionally provides poor estimates of simple, linear maneuvers that the Kalman filter can otherwise provide. This paper seeks to improve maneuvering target tracking without sacrificing computation time by proposing the use of a moving-average filter in the tracking process. A Moving-Average filter is used to track the position root-mean-square error (RMSE) and switch from the Kalman filter to the Kalman-Levy filter when this error becomes large. The Kalman filter, the Kalman-Levy filter, and the switching algorithm based on the Moving-Average filter are demonstrated on two tracking problems. Simulation results show that switching between the filters improves maneuvering target state estimation accuracy while being computationally efficient.
Target tracking has become more complicated as radar operating environments have become increasingly congested with clutter, interference, and countermeasures for jamming of radio frequency (RF) signals. As a result, the accuracy and performance of target tracking radars are further degraded. Three configurations of radar systems, namely, monostatic radar, bistatic radar, and passive radar, are commonly used today. The most conventional one is the monostatic radar defined by a co-located transmitter and receiver. In the bistatic radar system, the radar transmitter and receiver are physically separated by a large distance. A passive radar system is a derivative of the bistatic radar system, wherein radar functions are performed without the use of one’s own transmitter. Instead, existing signals-of-opportunity (SOP) within the RF environment are opportunistically exploited to perform the radar functions. This paper presents a concept based on metacognition which entails dynamically switching the mode of operation between the three radar configurations to optimize target tracking accuracy. The paper provides an overview of the three radar configurations followed by the description of an approach for switching radar configuration among the three radars. Modeling and simulation of a passive radar system for target tracking in MATLAB is presented and followed by analysis and discussion of its performance.
There is growing research interest to merge the idea of a metacognitive radar with that of a tracking radar. The concept of metacognition can be broadly summarized as the process of learning about learning. In a metacognitive tracking radar, the system uses cognitive processes to detect and track a target in a dynamic environment. The radar then applies metacognitive techniques to select the cognitive process that yields the most accurate target track. In the context of target tracking, cognitive processes are various tracking algorithms. Currently, metacognitive tracking radar systems have only been demonstrated on targets of known trajectories. Their performance in the case of a randomly maneuvering target has not been explored. This paper presents an initial approach to this problem. First, an algorithm to generate random target trajectories is presented. Then, these trajectories are estimated using two estimation algorithms: the Extended Kalman Filter (EKF) and the Interacting Multiple Model (IMM) estimator. Finally, the performances of these two algorithms are compared.
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