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Multi-Input and Output Radar System (MIMO) is a new type of radar system that has been the subject of much research in recent years due to its many advantages. Meanwhile, the issue of target tracking in MIMO radar systems is of great importance, and providing an efficient solution for it remains an unresolved issue. In this paper, target tracking in MIMO radar systems using optimizing Kalman interactive filter is investigated. The proposed method in this research is the MIMO radar system for the simultaneous tracking of multiple targets. In the assumed system model, the interactive multiple model (IMM) based on Extended Kalman Filter (EKF) is used to understand the effective tracking of the target. Also, the optimization of the multi-objective tracking model in MIMO radar systems has been done by the proposed method using the particle swarm optimization (PSO) algorithm. This optimization algorithm estimates the fit of each studied response based on the number of energy resources and time consumed. The efficiency of the proposed method in a simulated environment has been evaluated and its performance in tracking multiple targets has been investigated in terms of different criteria. Based on the results of these experiments, the proposed method, in addition to reducing the amount of tracking error, can be effective in reducing energy consumption and reducing the sampling period. Thus, the proposed method will reduce resource consumption in the multi-objective tracking system. [ABSTRACT FROM AUTHOR]

One of the most important parts of infrared search and tracking systems is image processing, which detects the targets in infrared images. In this paper, a new infrared small target detection algorithm is proposed. The proposed method uses heuristic optimization algorithms to find small targets in infrared images. In this way, the particle swarm optimization algorithm is used as one of the best heuristic optimization algorithms. The performance of the proposed algorithm is evaluated using real and simulated infrared images. These images include a variety of false response sources like high-intensity edges, cloudy background, complex sea-sky background, and a target close to high-intensity background clutter. The experimental results are compared with the four common methods of infrared small target detection. The experimental results show the effectiveness and performance of the proposed algorithm. Also, the runtime of the proposed method is comparable to other commonly used methods, and this makes it usable in real-time programs. [ABSTRACT FROM AUTHOR]