This paper describes methods that have been developed for using multiple hypothesis tracking (MHT) for an agile beam radar in the presence of range gate pull off (RGPO) electronic countermeasures (ECM). The paper shows how the agile beam radar allocation logic can be extended to include uncertainty in target position due to data association uncertainty. It also shows how the MHT track score can be modified to reflect target offset from the commanded radar antenna position and how measured SNR is included in the track score. Results from the second Benchmark tracking study are presented. These results show MHT-based allocation to ge highly efficient. The results also show that the system satisfies stringent track maintenance requirements in the presence of RGPO and coincident target maneuvers.
This paper discusses the potential application of Interacting Multiple Model (IMM) filtering to the multi-radar Air Defense System application. This application includes a wide variety of potential target and radar characteristics. The paper begins with a discussion of the three IMM filter models that have been chosen and the choice of Markov transition matrix under the condition of variable update rate filtering. A comparison is given of the track prediction performance of the IMM method with that of a single model filtering system that uses maneuver detection for gain and covariance adjustment. Results show the IMM approach to be uniformly better than a conventional filter that has been designed for the Air Defense System application. However, the relative improvement of the IMM approach is a strong function of the quality of the radar measurement data. Since data association is a key tracking issue, IMM filtering will be adapted for use as part of a Multiple Hypothesis Tracking System. For this purpose, IMM gating and track scoring expressions are discussed and the methods validated through simulation.
This paper describes how optical sensor signal processing and data association methods that have been developed for Aerospace applications can be applied to the traffic monitoring function of Advanced Traffic Management Systems (ATMS). It first discusses techniques that have been developed for background estimation and detection of vehicles on a roadway. Then, the transformation to tracking coordinates and the multiple target tracking (MTT) algorithm that produces traffic flow observation data are outlined. An extended Kalman filter that takes observed flow data from multiple sensor sites and produces flow estimates for an entire roadway is described and its application to incident detection discussed. Preliminary results using simulated and actual freeway data are presented. Finally, techniques for presenting this data to the user and the manner in which these signal and data processing techniques relate to an overall ATMS design are outlined.