A comparative analysis of QADA-KF with JPDAF for multitarget tracking in clutter

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.

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