目前绝大部分的定位算法只适用于特定的定位场景，而基于Taylor 级数迭代的定
位算法适用于任意定位体制. 为此，针对具有一般普适意义的定位方程，给出一种系统分析
Taylor 级数迭代定位算法的理论框架，分别在校正源状态无误差(情况a) 和校正源状态有误
差(情况b)条件下推导了基于Taylor级数展开的定位方法及其相关性能. 对于情况(a)，给出
了相应的克拉美罗界. 对于情况(b)，首先给出其克拉美罗界，并与情况(a)进行比较. 其次推
导出忽略校正源状态误差时的均方误差理论值，并与克拉美罗界进行比较，发现忽略校正源
状态误差无法达到最优定位精度. 针对上述不足，提出了一种基于Taylor 级数迭代的两步最
优融合算法，并证明其精度可达到克拉美罗界. 最后，利用仿真实验场景验证该算法的性能以
及理论分析的正确性.
While most passive localization algorithms are only applicable to certain applications,
the Taylor series iteration (TSI) algorithm is suitable for arbitrary settings. For
this reason, a theoretical framework for deriving localization performance of TSI is discussed.
Two cases, with and without calibration state error, are considered. In the former
case, the corresponding Cramér-Rao lower bound (CRLB) is derived. In the latter case,
the corresponding CRLB is derived and compared with the former case. Mean square error
(MSE) is derived theoretically. Comparison between the theoretical MSE with the CRLB
indicates that accuracy of the source location cannot reach the optimal accuracy without
considering the calibration state error. To overcome the shortcoming, a two-step optimal
fusion localization method based on TSI is proposed, with accuracy reaching that of CRLB.
Passive localization is simulated to verify performance of the proposed algorithm, showing
validity of the theoretical analysis in this paper.
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