基于粒子群优化的三维测向交叉定位算法

Algorithm for 3D orientation cross bearing based on PSO

首先分析了三维测向交叉定位的基本原理,针对测角信息冗余这一问题,研究了2种测向交叉定位算法,并仿真出了定位精度的几何稀释(GDOP)图。结论表明:存在相同的测角误差和布站误差时,基于迭代的泰勒级数法定位精度较好,但是迭代初值如何选取仍然没有得到很好的解决。提出了一种基于迭代搜索求最优解的自适应权重的粒子群算法,最后通过设定不同的测角误差值仿真分析了该算法。仿真结果表明:在测角误差为0.5°或者1°时,粒子群优化算法在高度估计上比传统算法更准确,并且新算法有更好的定位精度,有一定的实际应用价值。

Basic theory of three-dimensional （3D）orientation cross bearing is analyzed, aiming at problem of information redundancy in angle measurement, two algorithms are researched, GDOP figures is obtained by simulation. It is found that Taylor series method based on iteration has better position precision than the others when angular measurement and station distribution error are the same, but how to chose the initial iteration value is still a problem. So an algorithm based on iterative searching to obtain the optimal solution is presented, named adaptive weight particle swarm optimization algorithm ,finally, the algorithm is simulated and analyzed carried out by setting different values of angle measurement error. Simulation resul~ show that when angle measurement error is 0.5°or I ° ,the PSO algorithm is more accurate than traditional algorithms in height estimation, at the same time, the new algorithm has better positioning precision, and has certain practical value.