基于Rayleigh分布杂波模型的动态目标检测算法

Moving Target Detection Method Based On The Rayleigh Distribution Clutter Mode

为了解决传统的动态目标检测算法在功率波动明显的非均匀复杂背景噪声下,检测性能严重下降的问题,基于杂波分布模型和恒虚警理论,提出一种基于Rayleigh分布杂波模型的单元最大最小平均恒虚警(CMMA-CFAR)检测算法,通过均值和均方差估计噪声平均功率并动态调整检测门限参数,在保持较低虚警率的前提下,提高目标的检测率.采用了数字滤波器组降低旁瓣功率,抑制强杂波干扰.经过与其它算法仿真对比,该算法具有最优的检测性能,检测率大于95.00%.该方法已应用于车辆开门防撞预警系统,在奇瑞G5车型上进行了标定测试,针对典型接近目标:自行车、摩托车和轿车,平均预警率大于97.50%,误警率小于3.00%.结果表明,该算法在杂波边缘干扰和非均匀噪声背景下,仍具有良好的检测性能.

In order to solve the problem that the detection performances of the current moving target detection method decreases badly in non-uniform and complex background noise,which fluctuated significantly in power,a cell maximum and minimum average-CFAR（ CMMA-CFAR） algorithm was proposed to maintain higher detection rate and low false detection rate by adjustment threshold in time based on the noise intensity based on clutter distribution model and CFAR theory. Digital filter bank was used to suppress noise effectively by lowering the digital signal sidelobe power. Compared with the simulation and analysis results of other algorithms,the algorithm had the best detection performance,whose detection rate was up to 95. 00%. The algorithm was applied to a vehicle door open collision warning system,which was calibrated and tested on the Chery G5 car.For three representative closing targets： bicycles,motorcycles and motor vehicles,the average early warning rate was up to 95. 50% and false alarm rate was down to 3. 00%. The results show that the algorithm still has a good detection performance in clutter edge interference and non-uniform background noise.