基于改进粒子群‒牛顿算法级联的双星定位方法

Dual-satellite positioning method based on cascaded IPSO‒Newton algorithm

摘要:

双星时差(TDoA)/频差(FDoA)定位因其只需利用2颗卫星便可对辐射源进行定位，成本和实现难度低于多卫星定位体制，实时性优于单卫星定位，更适合实际应用。为解决时频差方程解算过程中伴有的非线性最优化问题，提出一种改进粒子群算法(IPSO)；针对双星时频差定位系统采用牛顿算法定位精确度较高，但在实际定位中采用该算法存在目标位置初值设置的收敛盲区问题，提出基于改进粒子群-牛顿(IPSO-Newton)级联定位方法。利用IPSO算法获得可靠收敛的初定位值，并以此作为迭代初值；级联使用牛顿法进行定位，提高定位精确度并避免初值不收敛问题。仿真实验表明，所提算法比Newton法有效初始点选取成功率提高了48.15%，节省了计算量，提高了算法的计算效率。

Abstract:

Dual-satellite Time-Difference of Arrival(TDoA) and Frequency-Difference of Arrival (FDoA) positionings utilize just two satellites to locate the emitter, bearing lower cost and difficulty than multi-satellite positioning, and better real-time performance than single-satellite positioning system, more suitable for practical applications. In order to solve the nonlinear optimization problem in the process of solving the TDoA/FDoA equation, an Improved Particle Swarm Optimization(IPSO) algorithm is put forward. Dual-satellite TDoA/FDoA location systems use Newton method with high precision, but there exists unsolvable blind areas of initial value of iteration. To address this issue, an cascaded localization method of IPSO and Newton iteration is proposed, in which the IPSO algorithm gives coarse localization result with rapid speed and reliable convergence, and this coarse result is used as the initial value of Newton iteration, so as to reduce the positioning error and avoid non-convergence. By analyzing the simulation results, the proposed algorithm not only increases the success rate of effective initial point selection by 48.15% compared to the Newton method, but also cuts the computation and improves the computational speed.