More Base Radar Time Difference Positioning Technology Research

Multistatic radar system is a type of radar system which consists of transmitters and receivers distributed in different locations. Because the monostatic radar is limited in azimuth measurement accuracy while the target is far from the radar,and multistatic radar has a higher positioning accuracy for long-range goal compared with monostatic radar because the different architecture.The article research the principle of multistatic radar location, location algorithm and the distinguishment of multiple target. Mainly include the following:1. Introduce the concept of multistatic radar and two positioning methods.For convenice we analyze the typical structure of one transmitter-multiple receiver multistatic radar system which positioning aerial target.Then give an accurate model of the target positioning base on it.Because linear approximation may cause large error when we solving the nonlinear location equations,we transfer the nonlinear equations to a objective function base on minimum mean square error criterion. Then the targeting problem can be transformed to the minimum of objective function.2. Research the principles of the steepest descent method and genetic algorithm and their application in the multistatic radar location. Then proposed a step selection method based on interpolation because the ideal steepest descent method is difficult to get the best step. The simulation analysis of the impact of the layout of radar transmitter and receiver and algorithm parameters on the speed and convergence of the algorithm,and give an reasonable parameter range.3.Propose a fast neighborhood-based search algorithm,which speed is much faster than steepest descent method and genetic algorithm. Then analysis of the impact of the layout of radar transmitter and receiver on the speed and convergence of the algorithm. Use it to improve the genetic algorithm,which can get a higher accuracy. The simulation result show that the algorithm can guarantee convergence and much faster than the steepest descent method and genetic algorithm. genetic algorithm is much faster after improved. Then compare the performance of algorithms above.4. Research the location error in a noisy environment,analysis of the relationship between the location error and the number of receivers.5. Proposed a multi-target distinguishment method between each of the target echo, proposing a combining method which using possible solution set and statistical desition to distinguish the various target echo.This method greatly reduce the computation compared with brute-force method and take full advantage of the echo information,it is a best practice in probabilistic sense.