A SVD Based SRUKF Algorithm of Single Observer Passive Location

A square root unscented kalman filter algorithm based on singular valued decomposition is presented to enhance the robustness in the single observer passive location. The Cholesky decomposition or update is replaced by singular value decomposition, so the new method solves the unstable problem of SRUKF (Square Root unscented kalman filter) which is caused by covariance matrix morbidity in strong nonlinear cases. The simulation results show that the filtering algorithm of SVD-SRUKF proposed in this paper has higher stability and accuracy than any other similar algorithm.