Design and experimental validation of a robust CFAR distributed multifrequency radar data fusion system

A robust constant false alarm rate (CFAR) distributed detection system that operates in heavy clutter with unknown distribution is presented. The system is designed to provide CFARness under clutter power fluctuations and robustness under unknown clutter and noise distributions. The system is also designed to operate successfully under unbalanced power distributions among sensors, and exhibits fault-tolerance in the presence of sensor power fluctuations. The test statistic at each sensor is a robust (in terms of signal-to-noise ratio distribution across sensors) CFAR t-statistic. In addition to the primary binary decisions, confidence levels are generated with each decision and used in the fusion logic to robustify the fusion performance and eliminate weaknesses of the Boolean fusion logic. The test statistic and the fusion logic are analyzed theoretically for Weibull and lognormal clutter. The theoretical performance is compared against Monte-Carlo simulations that verify that the system exhibits the desired characteristics of CFARness, robustness, insensitivity to power fluctuations, and fault- tolerance. The system is tested with experimental target-in-clear and target-in-clutter data. The experimental performance agrees with the theoretically predicted behavior when the target is visible by all three radars. When the target is not visible in two out of the three radars, due to a possible undetected misalignment, the fusion performance is compromised. Robustification of the fusion performance against unpredictable and undetectable degradation of data quality in the majority of the sensors is then achieved using geometric filtering. Geometrical filtering is accomplished by using the Hough transform and additional information in the fusion design about the shape of the target trajectory(ies).