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arXiv · 2512.05355

Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter

Hirotaka Obo·Yuki Fujita·Masahisa Ishii·Hideki Moriyama·Ryota Tsuchiya·Yuta Ohashi·Kotaro Seki
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Abstract

This paper proposes a novel generalized cross-correlation (GCC) method, termed GCC-MSIF, to improve time difference of arrival (TDOA) estimation accuracy in noisy environments. Conventional GCC methods often suffer from performance degradation under low signal-to-noise ratio (SNR) conditions, particularly when the signal bandwidth is unknown. GCC-MSIF introduces a "mean signal" estimated from multi-channel inputs and an "inverse filter" to virtually reconstruct the source signal, enabling adaptive suppression of out-of-band noise. Numerical simulations simulating a small-scale array demonstrate that GCC-MSIF significantly outperforms conventional methods, such as GCC-PHAT and GCC-SCOT, in low SNR regions and achieves robustness comparable to or exceeding the maximum likelihood (GCC-ML) method. Furthermore, the estimation accuracy improves scalably with the number of array elements. These results suggest that GCC-MSIF is a promising solution for robust passive localization in practical blind environments.

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Hirotaka Obo, Yuki Fujita, Masahisa Ishii, Hideki Moriyama, Ryota Tsuchiya, Yuta Ohashi, Kotaro Seki. 2025-12-05. Noise Suppression for Time Difference of Arrival: Performance Evaluation of a Generalized Cross-Correlation Method Using Mean Signal and Inverse Filter. https://arxiv.org/abs/2512.05355

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