Nonlinear Least-Squares Time-Difference Estimation from Sub-Nyquist-Rate Samples

作者：Harada Koji^*; Sakai Hideaki

来源：IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 2012, E95A(7): 1117-1124.

DOI：10.1587/transfun.E95.A.1117

• TDOA
• time-delay estimation
• time-difference estimation
• innovation-rate sampling
• sampling kernel

In this paper, time-difference estimation of filtered random signals passed through multipath channels is discussed. First, we reformulate the approach based on innovation-rate sampling (IRS) to fit our random signal model, then use the IRS results to drive the nonlinear least-squares (NLS) minimization algorithm. This hybrid approach (referred to as the IRS-NLS method) provides consistent estimates even for cases with sub-Nyquist sampling assuming the use of compactly-supported sampling kernels that satisfies the recently-developed nonaliasing condition in the frequency domain. Numerical simulations show that the proposed NLS-IRS method can improve performance over the straight-forward IRS method, and provides approximately the same performance as the NLS method with reduced sampling rate, even for closely-spaced time delays. This enables, given a fixed observation time, significant reduction in the required number of samples, while maintaining the same level of estimation performance.

• 出版日期2012-7