Electronic Information Engineering Department of Naval Aeronautical and Astronautical University, Yantai, China

---

Abstract—In this paper, the new nonlinear filter method Cubature Kalman Filter (CKF) is improved to solve the passive location problem. Firstly, the Empirical Mode Decomposition (EMD) algorithm is used to estimate the new measurement noise covariance in the filter process; And then the new covariance of the noise is brought into the circle; Meanwhile, the location process is improved by the way of square root to keep its stability and positivity, and the results of track with moving angle-measured sensors’ measurements by Extend SCKF are compared with the results by Unscented Kalman Filter (UKF) in the paper; By the tracking results to the velocity of the target, Extend SCKF algorithm can not only track the target with unknown measurement noise but also improve the passive position precision remarkably with the same complexity of UKF algorithm.

Index Terms—Cubature Kalman Filter, Sensor, Empirical Mode Decomposition, Square-Root Cubature Kalman Filter, Unscented Kalman Filter

[PDF]

Cite: Ruiguo Sheng, Yang Zhang, and Jun Miao, " An Improved Location Algorithm by Extend Square-root Cubature Kalman Filter," Journal of Computers vol. 8, no. 2, pp. 471-477, 2013.