This article deals with the multi-target tracking problem (MTT) in MIMO radar systems. As a result, this problem is now seen as a new technological challenge. Thus, in different tracking scenarios, measurements from sensors are usually subject to a complex data association issue. The MTT data association problem of assigning measurements-to-target or target-state-estimates becomes more complex in MIMO radar system, once the crossing target tracking scenario arises, hence the interference phenomenon may interrupt the received signal and miss the state estimation process. To avoid most of these problems, we have improved a new hybrid algorithm based on particle filter called “Monte Carlo” associated to Joint Probabilistic data Association filter (JPDAF), the whole approach named MC-JPDAF algorithm has been proposed to replace the traditional method as is known by the Extended KALMAN filter (EKF) combined with JPDAF method, such as EKF-JPDAF algorithm. The obtained experimental results showed a challenging remediation. Where, the MC-JPDAF converges towards the accurate state estimation. Thus, more efficient than EKF-JPDAF. The simulation results prove that the designed system meets the objectives set for MC-JPDA by referring to an experimental database using the MATLAB Software Development Framework.

This paper addresses the multi-target tracking problem in a multiple-input multiple-output radar system. Different tracking sensor measurements often encounter complex data as well correlation problems. Our goal is to solve this dilemma, which appears to be a new technical challenge; fix object-tracking scenarios while avoiding divergence. Once the cross-path phenomenon occurs, the target measurement assignment process in MIMO radar systems becomes more complicated. Therefore, this interference phenomenon can disturb the received signal and miss the state estimation process. To avoid all the mentioned problems, we have improved a new hybrid algorithm based on the particle filter; Adaptive Monte Carlo has been used in conjunction with the Joint Probabilistic Data Association Filter to substitute the classical extended KALMAN filter combined with JPDAF known as EKF-JPDAF. Experimental results of our MIMO-FMCW radar system with AMC-JPDAF converge to a more accurate state estimate while avoiding divergence. Using MATLAB software development framework, the designed system meets the goals originally set by AMC-JPDAF by referring to the experimental database obtained from the MIMO-FMCW 8x16 radar system.

The state estimation and tracking of random target is an attractive research problem in radar system. The information received in the radar receiver was reflected by the target, that it is received with many white and Gaussian noise due to the characteristics of the transmission channel and the radar environment. After detection and location scenarios, the radar system must track the target in real time. We aim to improve the state estimation process for too random target at the given instant in order to converge to the true target state and smooth their true path for a long time, it simplifies the process of real-time tracking. In this framework, we propose a new approach based on the numerical methods presented by MONTE CARLO (MC) counterpart the method conventionally used named Extended KALMAN Filter (EKF), we showed that the first are more successful. Keywords: Radar, Monte Carlo, Extended KALMAN Filter, Tracking, PF, Random target.