Modified CA-CFAR Detector for Oversampled Sensors

To ensure less quantization error and sampling loss, oversampling is very common in radar, sonar, communications and other applications. The cell averaging constant false alarm rate (CA-CFAR) is widely used in target detection due to its ability to adaptively adjust the detection threshold based on varying background noise levels and maintain a constant false alarm rate. However, a high oversampling ratio (OSR) will increase the correlation degree of the sample data, which will distort the maximum likelihood estimation (MLE) of the background noise power obtained by the traditional CA-CFAR detector and consequently weaken its detection performance. To solve this problem, a modified CA-CFAR detector is proposed in this paper. Utilizing the colored filter matrix, the MLE of the covariance matrix of the data in the reference window is obtained, based on which the modified test statistics are derived via the generalized likelihood ratio test. The simulation results show that the modified CA-CFAR detector can achieve the upper performance bound for different OSRs.