Moving Target Indicator Sensor Research Articles

Performance Measures and MHT for Tracking Move-Stop-Move Targets with MTI Sensors

We consider the problem of tracking ground-based vehicles with moving target indicator (MTI) sensors. MTI sensors can only detect a target if the magnitude of the range-rate exceeds the minimum detectable velocity, and as a result targets typically exhibit evasive move-stop-move (MSM) behavior in order to avoid detection. Further complexity is added by the fact that the environment is cluttered, resulting in both missed detections and spurious false measurements. A key problem is then to distinguish between a missed detection of a moving target and a lack of a detection due to the target stopping (or moving at low velocity). In this paper, we provide a novel framework for calculating performance measures (which are not necessarily bounds) for this problem. Our approach unifies state-of-the-art posterior Cramer-Rao lower bound (PCRLB) approaches for dealing with manoeuvring targets (namely, the best-fitting Gaussian approach) and cluttered environments (the measurement sequence conditioning approach). Our approach is also able to exploit the correlation between the number of measurements at each sampling time and the target motion model. Furthermore, we are able to show that established PCRLB methodologies are special cases of this unifying approach. We therefore provide a general technique for calculating performance bounds/measures for target tracking that can be applied to a broad range of problems. We also introduce a multiple hypothesis tracker (MHT) implementation for this problem. In simulations, the MHT is shown to accurately track the target, and provided that the probability of detection is close to unity, the new performance measure is an extremely accurate predictor of the localization performance of the MHT. If the probability of detection is lower, and except when employing a short scanback, the MHT performance is significantly better than the measure. In such cases the true limit of performance is the measure calculated by assuming the correct motion model, and data association hypotheses are known. The MHT filter is also shown to maintain track of the target in a high percentage of simulations, even with a scanback of just a few time steps. Therefore if track maintenance is the most important requirement, the employment of long scanbacks is not essential. We conclude that our PCRLB-like measure and MHT implementation provide effective approaches for performance prediction and target tracking, respectively, in the challenging MTI domain.