基于Kalman滤波和奇偶矢量法的优化RAIM算法

针对传统RAIM算法很难检测微小伪距偏差的问题,可以通过对多个历元的统计检验量进行归一化处理,增大统计检验量的非中心化参数,提高对微小故障的检测率.Kalman新息检测法可以对卫星的故障进行独立检测,具有运算量小、在少星情况时仍能进行故障检测和识别的优点,但是对微小伪距偏差不敏感.针对两种方法的优点提出了基于Kalman滤波和奇偶矢量法的综合RAIM算法.仿真结果表明,该方法不仅可以提高对微小伪距偏差的检测率,同时减少了对可见卫星数的要求,验证了该算法应用于接收机自主完好性检测的可行性和正确性.

An Optimized RAIM Algorithm Based on Kalman Filter and Parity Vector

As traditional RAIM algorithm is very difficult to detect small pseudorange deviation, an algorithm is proposed to increase the decentralization parameters and improved the monitoring rate of tiny fault by normalization processing of statistical tests with multiple epochs. The fault of each satellite can be detected by Kalman innovation covariance independently and it has the advantage of monitoring and identification when the visible satellites are less, but Kalman innovation algorithm is not sensitive to tiny pseudorange fault. A comprehensive RAIM algorithm is proposed biased on the advantages of Kalman innovation covariance and parity vector algorithm. The simulation results show that the new algorithm can not only improve the monitoring rate of small pseudorange deviation, but also reduce the requirements of visible satellites, the result verifies the algorithm is applied to the receiver autonomous integrity test feasibility and correctly