Research On Passive Position Technology Within NLOS Environment

Passive location is widely used in wireless target detection,wireless interference source investigation,and other areas because of its countless advantages,such as strong invis ibility,simple equipment,and good flexibility.Among the methods that was used before,moving single station bearing-only position algorithm is one that most commonly used because of its strong invis ibility and easy to implement.However,wireless signals are easy to be affected by complex environments,and the NLOS phenomenon is widespread in communication process,which will greatly reduce the stability and accuracy in location algorithms.Based on previous studies,this paper analyzed the features of NLOS propagation and the advantages and disadvantages of existing filtering algorithms,and proposed a combined algorithm,which is suitable for NLOS environments.We firstly suppress the influence of the error by using Weighted Kalman Interest Smoothing(WKIS)algorithm,and then increase the positioning accuracy by using BP Neural Networks-based Improved Modified Gain Extended Kalman Filter(BPNN-MGEKF)algorithm.The combination of these two algorithms can greatly improve the ability on anti-NLOS error.The Weighted Kalman Interest Smoothing(WKIS)algorithm run weighted smoothing process according to signal strength,thus fixed the problem caused by traditional smoothing algorithm,and avoid mis judgment when one entered the LOS environments from NLOS environments.The fixed MGEK algorithm reduced errors by modify Kalman gain.However,the traditional gain-correction ways will bring other errors we use measured values instead of actual values.To solve this problem,we proposed the BP-Neural Network based Modified Gain Extended Kalman Filter(BPNN-MGEKF)algorithm,which use the relationships between fitted measured values in BP-Neural Networks,measurement error variance and the correction value of real gain.By using the measured values,the correct value will be achieved to suppress additional error,and improve positioning accuracy.We deployed our algorithm in the scene of the moving s ingle station bearing-only passive location and compared our results with existing algorithms.The result shows that the integrated algorithm is strong enough to supperss NLOS error and is more stable.