The steered response power (SRP) with phase transform algorithm has been demonstrated to be robust against reverberation and noise for single-source localization. However, when this algorithm is applied to multisource localization (MSL), the "peak missing problem" can occur, namely, that some sources dominate over others over short time intervals, resulting in fewer significant SRP peaks being found than the true number of sources. This problem makes it difficult to detect all the sources among the available SRP peaks. We propose an iteratively reweighted steered response power (IR-SRP) approach that effectively solves the "peak missing problem" and achieves robust MSL in reverberant noisy environments. The initial IR-SRP localization function is computed over the time-frequency (T-F) bins selected by a combination of two weighting schemes, one using coherence, and the other using signal-to-noise ratio. When iterating, our method finds the significant SRP peaks for the dominant sources and eliminates the T-F bins contributed by these sources using inter-channel phase difference information. As a result, the remaining sources can be found in subsequent iterations among the remaining T-F bins. The proposed IR-SRP method is demonstrated using both simulated and measured experiment data.
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