SYSTOLIC ARCHITECTURES FOR RADAR CFAR DETECTORS

Radar constant false alarm rate (CFAR) detectors are adaptive threshold detectors, used to compensate for unknown noise environments. To normalize mean shifts in this noise level, the system estimates the background noise level in any under-test data sample based on nearby reference data samples, often the surrounding samples. We discuss several advances in the evolution of radar CFAR detectors, from the classical mean-level detector to more recent designs using order statistics, or sorted data values. These algorithms can be implemented by modifying the existing running window order statistics filtering techniques widely used in signal/image processing. Although the signal processing theory of CFAR detection is well advanced, practical applications lag because of the high throughput required in radar. This intensive computational requirement is likely to be met by further advances in VLSI technology alone; it must result from parallel processing techniques. Systolic array architectures are proposed for several important CFAR detectors. Techniques for improving the processor utilization efficiency of the proposed array architectures are also discussed.