Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array
Evolutionary TDOA-Based Direction Finding Methods With 3-D Acoustic Array
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DOI：
10.1109/tim.2015.2415051
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发表时间：
2015-04
影响因子：
5.6
通讯作者：
Xunxue Cui;Kegen Yu;Songsheng Lu
中科院分区：
文献类型：
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作者：
Xunxue Cui;Kegen Yu;Songsheng Lu
本文主要研究了在三维声传感器阵列上使用到达时差分（TDOA）测量的信号源测向问题。提出了两种求解测向问题的进化计算方法：遗传算法和粒子群优化算法。该算法的开发使用了声速，声速是根据观测到的天气参数和最小二乘估计器的初始方向估计结果来估计的，而最小二乘估计器是目前基于tdoa测向的关键方法。在定义成本函数时，采用所有无参考的TDOA测量来提高性能。为了保证快速收敛，还使用LS估计器对两种群智能算法提供初始方向估计。仿真结果表明，基于完整TDOA集的方法优于基于有限参考TDOA测量集的Cramer-Rao下界方法，显著优于LS估计方法。进行了大量的现场试验，试验结果与模拟结果吻合较好。
This paper focuses on direction finding of a signal source using time-difference-of-arrival (TDOA) measurements at a 3-D acoustic sensor array. Two evolutionary computation methods are proposed to solve the direction finding problem, which are the genetic algorithm and the particle swarm optimization algorithm. Sound speed is used in the development of the algorithms, which is estimated based on observed weather parameters and initial direction estimation results from the least square (LS) estimator which is presently the key method in TDOA-based direction finding. All reference-free TDOA measurements are adopted in defining cost function to improve performance. To guarantee fast convergence, an LS estimator is also utilized to provide initial direction estimates for the two swarm intelligent algorithms. Simulation results demonstrate that the proposed methods with a full TDOA set are superior to the Cramer-Rao lower bound with a limited set of reference-based TDOA measurement, significantly outperforming the LS estimator. Extensive field experiments were conducted and there is good agreement between the experimental results and simulation results.