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文中提出了一种利用时延信息的基于归一化约束总体最小二乘法(regularized CTLS)的定位算法,将时延量测的非线性方程转换成目标状态量的线性方程,将真实时延信息方程利用一阶Taylor展开转化为量测时延信息加上线性的噪声信息方程,从而可以利用RCTLS的方法对定位方程求解.分析了定位误差,并根据MSE最小原则,进而选取最优的加权因子,使得该算法具有更佳的定位性能,计算机仿真结果验证了该算法的可行性与有效性.最后是附录,给出了定位的CRB界与定位的GDOP图.
In this paper, we propose a localization algorithm based on normalized constrained global least squares (LSLS) using time-delay information, which converts the nonlinear equation of delay measurement into a linear equation of target state quantity. The real-time delay information The equation is transformed into the measurement delay information plus the linear noise information equation by the first-order Taylor expansion, so that the location equation can be solved by using the RCTLS method. The positioning error is analyzed and the optimal weighting factor is selected according to the MSE minimum principle , Which makes the algorithm have better localization performance.Computer simulation results verify the feasibility and effectiveness of the algorithm.At last, an appendix shows the GDOP map of the location CRB boundary and location.