基于Kalman的室内伪卫星定位算法研究

Research on indoor pseudo satellite positioning algorithm based on Kalman

针对普通抗差卡尔曼滤波算法在用于伪卫星室内定位时难以保证算法阈值最优性等问题,对基于抗差卡尔曼滤波的室内伪卫星定位算法进行了进一步研究,将遍历算法的思想与普通抗差卡尔曼率波模型相结合,提出了一种基于遍历抗差卡尔曼滤波的优化算法以用于提高伪卫星室内定位的精度。该算法可在给定范围内自主进行寻找比较,得到最适合该测量环境的算法阈值,从而确保算法模型中阈值的最优性,提高最终优化结果的精度。研究表明,相较于滤波前,X轴、Y轴方向平均标准差数值分别降低了39.8%与40.2%,X-Y平面平均标准差数值降低了39%,与普通卡尔曼模型的滤波性能进行比较,遍历抗差卡尔曼模型有着更好的优化效果。

Aiming at the problem that the common robust Kalman filter algorithm is difficult to ensure the threshold optimality of ordinary robust Kalman filter algorithm when used for indoor positioning of pseudopatellite, this paper studies the indoor positioning algorithm of pseudopatellite based on robust Kalman filter. Combining the idea of ergodic algorithm with the ordinary robust Kalman filter, an optimization algorithm is is proposed. The algorithm can find and compare, independently within a given range, the algorithm threshold most suitable for the measurement environment. It insures optimality, and improves the accuracy of final optimization results. The research shows that compared with before filtering, the average standard deviation of X and Y axes is reduced by 39.8% and 40.2% respectively, and the average standard deviation of X-Y plane is reduced by 39%. Compared with the filtering performance of the ordinary Kalman model, an ergodic robust Kalman model has better optimization effects.