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High-Resolution Time Delay Estimation Algorithms Through Cross-Correlation Post-Processing
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DOI：10.1109/LSP.2020.3048843.png)
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摘要
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Conventional cross-correlation (CC) and matched filter (MF) algorithms fail to resolve the multipath signals in cases when they are spaced within a Rayleigh resolution limit. Further post-processing of the CC outputs is necessary to improve the performance of CC. In this work, we propose two high-resolution time delay estimation algorithms by post-processing of the CC outputs. The first method applies a Richardson-Lucy deconvolution algorithm used in image deblurring to the CC outputs (Dec-CC). The proposed Dec-CC algorithm yields narrow beams to improve the estimation resolution and accuracy and achieve excellent performance in low signal-to-noise ratio (SNR) environments. The second method reformulates CC as a sparse signal recovery (SSR) problem and estimates time delay via weighted L1 norm minimization (CC-WL1). CC-WL1 also performs excellently for low SNR cases. Simulation results show that the proposed Dec-CC and CC-WL1 algorithms outperform other counterparts.
Keyword:
Delay effects
Signal processing algorithms
Estimation
Deconvolution
Signal to noise ratio
Correlation
Signal resolution
Cross-correlation post-processor
deconvolution
high-resolution
weighted L1 norm minimizing
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