A new method for multiple sources localization is proposed to eliminate association ambiguity for near and far mixed field sources. A spatial source localization model in the modified polar representation was constructed without the prior knowledge needed if the source is near-field or far-field. The localization model for multiple sources was deduced by using all the possible permutation of the TDOA sequences obtained from the original array by GCC-PHAT and the generalized trust region optimization processing method. In order to eliminate the phantom sources in the multiple sources localization, a set of calibration sub-arrays was constructed by switching the reference microphone in the array. The TDOA sequences of the estimated possible sources and all actual sources to the calibration sub-arrays were calculated separately. A reliability evaluation function was constructed based on the two sets of TDOA sequences, as well as a reliability evaluation between the sources identified by all the calibration sub-arrays. According to the principle of minimization of the reliability evaluation function, the real sources were screened out to solve the association ambiguity. Comparison analyses through simulation and experiment on real speech datasets were carried out under different localization scenarios. The results of simulation are consistent with the experimental results, which show that the proposed method effectively eliminates the phantom sources, and has higher positioning accuracy and robustness than the comparison methods, no matter sources are in the near-field or far-field.

提出了一种新的多源定位方法，以消除近场和远场混合声源的关联模糊性。如果源是近场或远场，则在不需要先验知识的情况下，可以构建修改后的极坐标表示中的空间源定位模型。通过使用GCC-PHAT从原始数组获得的TDOA序列的所有可能排列和广义信任区优化处理方法，推导了多个源的定位模型。为了消除多源定位中的幻像源，通过切换阵列中的参考麦克风来构造一组校准子阵列。分别估计了估计的可能来源和校准子阵列的所有实际来源的TDOA序列。基于两组TDOA序列以及所有校准子阵列所标识的源之间的可靠性评估，构建了可靠性评估功能。根据可靠性评估函数最小化的原则，筛选出真实源以解决关联模糊性。在不同的本地化场景下，通过仿真和实验对真实语音数据集进行了比较分析。仿真结果与实验结果吻合，表明该方法有效地消除了幻像源，并且无论是近场还是远场，其定位精度和鲁棒性均高于比较方法。以及所有校准子阵列确定的光源之间的可靠性评估。根据可靠性评估函数最小化的原则，筛选出真实源以解决关联模糊性。在不同的本地化场景下，通过仿真和实验对真实语音数据集进行了比较分析。仿真结果与实验结果吻合，表明该方法有效地消除了幻像源，并且无论是近场还是远场，其定位精度和鲁棒性均高于比较方法。以及所有校准子阵列确定的光源之间的可靠性评估。根据可靠性评估函数最小化的原则，筛选出真实源以解决关联模糊性。在不同的本地化场景下，通过仿真和实验对真实语音数据集进行了比较分析。仿真结果与实验结果吻合，表明该方法有效地消除了幻像源，并且无论是近场还是远场，其定位精度和鲁棒性均高于比较方法。筛选出真实来源以解决关联模糊性。在不同的本地化场景下，通过仿真和实验对真实语音数据集进行了比较分析。仿真结果与实验结果吻合，表明该方法有效地消除了幻象源，无论是近场还是远场，其定位精度和鲁棒性均高于比较方法。筛选出真实来源以解决关联模糊性。在不同的本地化场景下，通过仿真和实验对真实语音数据集进行了比较分析。仿真结果与实验结果吻合，表明该方法有效地消除了幻像源，并且无论是近场还是远场，其定位精度和鲁棒性均高于比较方法。