Direct estimation of time difference of arrival from compressive sensing measurements

Chan, Y.T.; Chan, François; Rajan, Sreeraman; Lee, B.H.

doi:10.1109/cosera.2015.7330307

Cited by 8 publications

(1 citation statement)

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“…There are intensive researches for CS-based signal processing for various applications. Specifying to the radar applications, the CS method has been demonstrated that it achieved both a relatively lower sampling rate and high-resolution property in such as synthetic aperture radar (SAR) based image reconstruction problem [7]- [9], the TDOA (Time Difference of Arrival) discrimination issues [10], [11] and the spectrum reconstruction schemes [12], [13]. However, it has been reported that the original CS algorithm suffers from inaccuracy if the return signals are strongly contaminated by noise [6], especially when highly correlated target signals are also closely located within the theoretical range resolution.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Time of Arrival Estimation Using Random Resampling in Compressed Sensing

Noto

Shang

Kidera

et al. 2018

IEICE Trans. Commun.

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There is a strong demand for super-resolution time of arrival (TOA) estimation techniques for radar applications that can that can exceed the theoretical limits on range resolution set by frequency bandwidth. One of the most promising solutions is the use of compressed sensing (CS) algorithms, which assume only the sparseness of the target distribution but can achieve super-resolution. To preserve the reconstruction accuracy of CS under highly correlated and noisy conditions, we introduce a random resampling approach to process the received signal and thus reduce the coherent index, where the frequency-domain-based CS algorithm is used as noise reduction preprocessing. Numerical simulations demonstrate that our proposed method can achieve super-resolution TOA estimation performance not possible with conventional CS methods.

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