Benchmark Problem with a Multisensor Framework for Radar Resource Allocation and the Tracking of Highly Maneuvering Targets, Closely Spaced Targets, and Targets in the Presence of Sea-Surface4nduced Multipath (CD-ROM)

Abstract : ELECTRONIC FILE CHARACTERISTICS: 34 files; MATLAB v.5.2 files, Postscript and MS Word documents. PHYSICAL DESCRIPTION: 1 computer laser optical disc (CD-ROM); 4 3/4 in.; 8.76MB. SYSTEMS DETAIL NOTE: Postscript requires viewer or conversion. PS file identical to hard copy. ABSTRACT: This report presents a benchmark problem with multisensor framework for target tracking. The benchmark problem involves beam pointing control of a phased array radar against highly maneuvering targets, closely spaced targets, and targets in the presence of sea-surface-induced multipath. The simulation testbed includes target trajectory scenarios to address these difficult tracking issues. The framework for the multisensor problem exists only since generic IRST and PESM sensor models are currently available. These generic sensors can be replaced with more realistic sensor models. The use of additional sensors can aid the tracking and improve the radar beam-pointing control. The benchmark problem includes the effects of target amplitude fluctuations, beamshape, missed detections, false alarms, finite sensor resolution, target maneuvers, and track loss. This benchmark problem also includes multiple waveforms and frequencies so that the radar energy can be coordinated with the tracking algorithm. Advanced monopulse processing techniques must be implemented by the researcher. The "best" tracking algorithm as determined by the performance criteria is the one that minimizes a weighted average of the radar energy and radar time, while satisfying a 4-percent lost track rate. The report presents the radar model, the target scenarios, and the performance criteria for the benchmark problem. A computer program testbed that simulates the multisensor, adaptive tracking of targets is provided so that researchers can implement and evaluate their algorithms with minimal effort.