CAJ | 학술논문

为实现目标精确定位，减少初始值对目标定位精度产生的影响，该文提出了一种稳健收敛的时差定位迭代算法。该算法利用最大似然方法确定目标函数，运用牛顿法对目标位置进行迭代求解，对于计算过程中可能出现的病态 Hessian 矩阵，运用正则化理论构造一个对称正定的 Hessian 矩阵，使算法能够稳健高效的运行。实验结果表明：相对于传统牛顿法，本文算法在初始值的选取上具有稳健性，对误差选取较大的初始值，仍能够保证算法的收敛性，相对于现有闭合式定位方法，本文算法在噪声较大时具有较好的定位精度。
위실현목표정학정위，감소초시치대목표정위정도산생적영향，해문제출료일충은건수렴적시차정위질대산법。해산법이용최대사연방법학정목표함수，운용우돈법대목표위치진행질대구해，대우계산과정중가능출현적병태 Hessian 구진，운용정칙화이론구조일개대칭정정적 Hessian 구진，사산법능구은건고효적운행。실험결과표명：상대우전통우돈법，본문산법재초시치적선취상구유은건성，대오차선취교대적초시치，잉능구보증산법적수렴성，상대우현유폐합식정위방법，본문산법재조성교대시구유교호적정위정도。
For the purpose of pursuing precise source localization and reducing the impact of location accuracy brought by the initial value, an iterative algorithm which can converge robustly is proposed in this paper for the source localization using time-difference-of-arrival (TDOA). The new algorithm firstly introduces the maximum likelihood method to determine the objective function, and then uses the Newton method to find the source location. With the problem of the ill-condition Hessian matrix, the algorithm uses the Regularization theory to construct a symmetric definite Hessian matrix, which ensures the robust and the efficient of the algorithm. Experiments results show that this new algorithm is robust to the initial value, and is still able to ensure its convergence even with an inaccurate initial value of large error compared with the classical Newton method, and then compared with some other closed-form source location methods, the new algorithm has better accuracy in large noise levels.