The Positioning And Tracking Method Of Passive Radar Based On Compass Satellite Signals

With the development of technology, the military technology of many countries is constantly updated. The once-popular traditional radar now is facing four threats(the first is the threat of electronic interference and electronic surveillance; the second is the threat of low-altitude, super-low altitude aircraft and cruise missiles; the third is the threat of invisible aircraft; the forth is the threat of the high-speed autonomous anti-radiation missiles).The technological development makes the traditional radar exposing these weaknesses, which is gradually threatening the survival of its own. After entering the new century, more and more countries began to develop new kinds of radar to overcome the four threats. The new kind radar is named passive radar, such as the Czech Republic’s "Vera," America’s "silent sentinels",Russian’s "Kaerqiuta" and China’s "anti-invisible Sentinel", which has been put to use in the army. In this passage, it begins from the emitter signals of passive radar, and gives a brief introduction of characteristics and working principles. It is aimed at the source of enemy radiation, the ground source and air radiation of the third party. After considering the operational safety and affected by terrain clutter interference and small actual situation, it selects the Signals of Compass Satellite as the radiation source. And then we studied the location and tracking of passive radar.After analyzing the characteristics of the Signals of Compass Satellite which has I branch and Q branch. Kasami code of I branch is used for civilian. We have computer simulation for its ambiguity function. Simulation results show that the Signals of Compass Satellite as a third party, its signal ambiguity function which is just like a "thumbtack". It has a good resolution about the distance and pacing, which is more suitable for the usage as a radar signal. Through the location method of the relative time difference of arriving at four stations, at three-dimensional space. Its positioning accuracy has also been Computer simulation, and the result is ideal.We will track the target steadily after positioning, which is called state estimation of the target. Taking it into account that Kalman filter is only suitable for Gaussian white noise, we estimate the effect of the state using extended Kalman filter algorithm, unscented Kalman filter algorithm and particle filter algorithm.And then we compare them with Computer Simulation. Considered from the effect of state estimation and the condition of applicable algorithm, we ultimately decided to use the particle filter for tracking target. We respectively make computer simulation of target tracking under the condition of the Gaussian noise environments and different intensities of flicker noise environments.