Abstract
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.
Highlights
Multiple-input multiple-output (MIMO) radar system is a multistatic architecture composed of multiple transmitters and receivers, which seeks to exploit the spatial diversity of radar backscatter
In order to deal with the Multi target tracking (MTT) data association issues, we found in literature several methods are classified into Bayesian and other non-Bayesian filters, has been applied to address different scenarios, such as, Markov Chain Monte Carlo Data Association (MCMCDA) was proposed in [7] as a solution to replace the conventional method as known by The Multiple Hypothesis Tracking (MHT), to handle the low Signal-to-Noise Ratio (SNR) in the pre-processing phase
This algorithm contributes to improving the state estimation of two crossing target in 2D using two separated sensors in a MIMO radar system
Summary
Multiple-input multiple-output (MIMO) radar system is a multistatic architecture composed of multiple transmitters and receivers, which seeks to exploit the spatial diversity of radar backscatter. In MIMO radar system, the objective of MTT is to estimate jointly at each scan the number of targets continuously moving in a given region and estimates their trajectories from noisy sensor measurements [2]. Confocal Laser Scanning Microscopy the number of transmitters and receivers in MIMO radar system needs to implement new intelligent algorithms leads to increased tracking performances, these depend on the specific and intelligent tracker employed [3, 4]. Multiple target tracking (MTT) in radar system is extremely challenging, due to a lot of constraints such as the low performance of the sensor, the nature and the number of the target illuminated, the real time processing and the uncertainty of data association at that time the crossing path phenomenon is appear [5, 6], some targets may go undetected and lead to loss their trajectories during the tracking interval
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