Algorithm Analysis And Simulation Research Of Passive Bearing-Only Location For Maneuvering Targets

Passive location technology takes more and more important role in the modern electronic warfare. It has some unique advantages that the high concealment, the far action distances, the strong anti-jamming ability, and so on. But in the information obtained from sensor, errors always exist, comparing to the real states of targets. Therefore, how to analyze the precision of passive location algorithms has become a major issue at present. Only through the complete analysis of precision, improving the precision of passive location system can be possible. Around this issue, many key points need in-depth careful study, which mainly include high-precision parameters measure of passive location, the choice of positioning system, high-precision tracking and filter algorithms.The main positioning systems of passive location are: Bearing-Only algorithm (BO), time difference of arrival algorithm (TDOA), Doppler-frequency algorithm, and so on. This paper mainly researches the Bearing-Only technology. Bearing-Only technology has been one of the most common positioning technologies, which only needs the angle information of target to locate the target, but the Bearing-Only technology has a defect that it can't achieve the distance information of target. In order to improve the precision of Bearing-Only technology, this paper analyzes and researches the tracking and filter algorithms of Bearing-Only technology.This paper is mainly around Bearing-Only technology, which adopts three kind of Kalman Filter: classic Kalman filter, extended Kalman filter, pseudo-linear Kalman filter. And theoretical analysis and computer simulation are adopted on each of the Kalman filter algorithm. First, establishing the various distributed models of sites, for different site's distributions, to analyze the different geometric functions. It researches the impacts of every measurement parameter and every algorithm on the precision of Bearing-Only technology, and draws the error simulation curves. By analyzing the simulation results, it compares the advantages and disadvantages among these three Kalman filter algorithms at last.