Research on Vehicle Radar Data Processing with Improved Interactive Kalman Filter
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摘要: 为了减少车载毫米波雷达数据中的噪声影响，本文采用了改进的交互式卡尔曼滤波算法对采集数据进行了处理，得到了目标运动状态的最优值。依据目标车辆的运行轨迹构建了运动状态方程，确定了不同状态下的状态矩阵和观测矩阵，同时设计了交互式多模型滤波器，借助于dSPACE场景仿真软件建立了虚拟交通场景，利用硬件在环技术实现了运动目标的数据采集，分析计算了雷达数据噪声，在滤波过程中，利用遗传算法对过程噪声和量测噪声进行在线优化，得到噪声的最优组合。通过激光雷达对目标的探测结果对算法的滤波性能进行了验证，滤波算法求得的数据平均误差小于0.1 m，对数据的噪声起到一定的抑制作用，提高了对目标车辆的定位与追踪能力。
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关键词:
- 车载毫米波雷达 /
- 改进的交互式卡尔曼滤波 /
- 硬件在环 /
- 遗传算法
Abstract: In order to reduce the influence of noise on millimeter wave radar data, an interactive Kalman filter algorithm is used to process the collected data and the optimal value of the data is obtained. The motion state equation of the target vehicle was constructed according to its motion trajectory, and the state matrix and observation matrix in different states were determined. Interactive multi-model filter was designed. The virtual traffic scene was built with dSPACE scene simulation software. The data acquisition of moving targets was realized with hardware-in-the-loop technology, and the radar data noise was analyzed and calculated. During the filtering process, the genetic algorithm was used to continuously optimize the process noise and measurement noise. The filtering performance of the algorithm was verified by the set target motion trajectory. The average error of the radar data obtained by the filtering algorithm was less than 0.1 m. The noise of the radar data was reduced, the positioning and tracking capabilities of the target vehicle were improved. -
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