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Particle Filtering Approach to Adaptive Time-Delay Estimation

A particle filter algorithm is developed for the problem of online subsample time-delay estimation between noisy signals received at two spatially separated sensors. The delay is modeled as an adaptive FIR filter whose coefficients are determined by the tracker's particles, and updated on a sample-by-sample basis. Efficient tracking of the delay parameter over time is ensured with the derivation of a global system model integrating the target dynamics for both near-field and far-field operation. Experimental simulations are carried out to assess the algorithm's convergence and tracking performance, and demonstrate that the proposed method is able to efficiently track time delays with stationary signals as well as speech