Efficient implementation of rank-only OS-CFAR with dedicated noise estimation
D. Köhler, Frank Meinl, Holger Blume · IET conference proceedings. · 2023
Differentiating targets from background noise is an essential task of radar signal processing. Typically, constant false alarm rate (CFAR) detectors, which estimate local noise characteristics to determine an adaptive threshold, are employed for this purpose. A commonly used variant for automotive radar applications is the ordered-statistic CFAR (OS-CFAR) due to its good performance in multi-target scenarios and near clutter edges. However, obtaining the order statistics is associated with computationally intensive sorting of the CFAR training data.With the rank-only implementation, an efficient OS-CFAR algorithm is given, which does not require to calculate the order statistics explicitly and thus removes the necessity of sorting. This has the drawback, that the local noise estimates are not calculated, which may be required in some applications, e.g. to compute the signal-to-noise ratio.