On the Performance Gain of the FFT Filter-Bank-Based Summation and Majority CFAR Detectors

The constant false alarm rate (CFAR) detector based on the fast Fourier transform (FFT) filter bank is computationally efficient and widely used for the detection and frequency estimation of narrowband signals embedded in noise. Depending on the expected bandwidth of the signals of interest, techniques involving the processing of multiple input data blocks, with or without overlap, may be used to optimize the detection performance. These include the predetection summation (averaging) of power spectral estimates or the use of a two-stage processing strategy where the final detection decision is based on the majority vote of detection decisions obtained for the individual input data blocks. This paper shows that the actual performance gain obtained by doubling the number of input data blocks is dependent on the number of data blocks and is always less than 3 dB.