2-D CFAR Procedure of Multiple Target Detection for Automotive Radar

In Advanced Driver Assistant System (ADAS), the automotive radar is used to detect targets or obstacles around the vehicle. The procedure of Constant False Alarm Rate (CFAR) plays an important role in adaptive targets detection in noise or clutter environment. But in practical applications, the noise or clutter power is absolutely unknown and varies over the change of range, time and angle. The well-known cell averaging (CA) CFAR detector has a good detection performance in homogeneous environment but suffers from masking effect in multi-target environment. The ordered statistic (OS) CFAR is more robust in multi-target environment but needs a high computation power. Therefore, in this paper, a new two-dimension CFAR procedure based on a combination of Generalized Order Statistic (GOS) and CA CFAR named GOS-CA CFAR is proposed. Besides, the Linear Frequency Modulation Continuous Wave (LFMCW) radar simulation system is built to produce a series of rapid chirp signals. Then the echo signals are converted into a two-dimensional Range-Doppler matrix (RDM), which contains information about the targets as well as background clutter and noise, through twice Fast Fourier Transform (FFT).The simulation experimental results show that compared to the two-dimensional OS-CA CFAR, the new 2-D GOS-CA CFAR can enhance the detection performance and robustness in the actual multi-target environment with lower computational complexity.