A non-iterative deconvolution method for estimating multipath channel responses

Manickam, T.; Vaccaro, R.J.

doi:10.1109/icassp.1993.319123

Cited by 25 publications

(7 citation statements)

References 2 publications

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“…We use a typical example adopted in many literatures [2,6]. with those obtained matched filtering, matvhing pursuit (MP) [7], and POCS method [2].…”

Section: Simulation Resultsmentioning

confidence: 99%

Multipath time-of-arrival estimation via modified projection onto convex sets

Zeng

Zhang

et al. 2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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A low complexity algorithm is proposed for estimating the multipath time-of-arrival (TOA) and attenuation factors from a noisy received signal consisting of multiple overlapped echoes. Different from the conventional projection onto convex sets (POCS) method, the proposed approach harnesses the sparse property of multipath channel and the 1 -norm is adopted as the measurement of sparsity. The TOA estimation problem is solved by a series of projection onto convex sets, including hypersphere and hyper-polyhedra. The computational complexity of the modified POCS algorithm is O(N log N ) per iteration with N being the length of the received signal. Simulation results confirm that the proposed approach provides better performance in terms of temporal resolution and robustness to noise compared with the matched filtering and the conventional POCS method.Index Terms-Time-of-arrival (TOA), multipath channel, projection onto convex sets (POCS), hyper-polyhedra, sparse representation.

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“…We use a typical example adopted in many literatures [2,6]. with those obtained matched filtering, matvhing pursuit (MP) [7], and POCS method [2].…”

Section: Simulation Resultsmentioning

confidence: 99%

Multipath time-of-arrival estimation via modified projection onto convex sets

Zeng

Zhang

et al. 2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Since h h h is sparse, we have K N . However, h h h will be smeared if there exist fractional timedelays and the channel sparsity level (number of nonzero taps) will be larger than K. The fractional time-delays can be estimated by interpolation of the discrete estimate h h h. More details on interpolation for fractional time-delay estimation can be found in [3], [15]. In this paper, our attention focuses on how to estimate the discrete version of the channel response.…”

Section: Problem Formulationmentioning

confidence: 98%

A Fast Algorithm for Sparse Channel Estimation via Orthogonal Matching Pursuit

Jiang

Zeng

Cheng

2011

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring)

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Channels with a sparse impulse response arise in a variety of wireless communication applications, such as high definition television (HDTV) terrestrial transmission and underwater acoustic communications. By exploiting the sparsity of the channel, this paper proposes a fast algorithm for sparse channel estimation based on a greedy algorithm called orthogonal matching pursuit (OMP). The proposed fast OMP-based channel estimation algorithm has a low computational complexity of O (KN log N ) with K and N the channel sparsity level and signal length, respectively. The fast OMP is competitive to the 1-minimization based methods in terms of estimation accuracy. In addition, the fast OMP is faster and easier to implement. Therefore it is an attractive alternative to the 1-minimization approaches. Simulation results are provided to demonstrate the performance of the fast OMP algorithm.

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“…The i th user sends its information bit via b i ∈ {−1, 1}, which directly modulates its signature s i (t) 4 . The usual means to deal with (14) is move the problem to "signal space" [23] by projecting onto an orthonormal basis for {s i (t)}, and if we do, the optimal receiver (in the sense of minimum probability of error in the joint decision for…”

Section: The Cdma Problemmentioning

confidence: 99%

“…These iterative algorithms still suffer from high computational cost, and generally converge to one of many local maxima, which, depending on the initial condition, may or may not be the global one. Some deconvolution schemes have been suggested [10,14] to arrive at a trade-off between accuracy and computation. The AP approach appears to be the best competitor.…”

Section: Introductionmentioning

confidence: 99%

<title>High-resolution radar ranging for multiple targets</title>

et al. 2002

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The similarity between the multiple-target radar ranging problem and the multi-user detection problem in CDMA is drawn: in CDMA, users' bits modulate distinct but correlated signature signals; while, in radar, the "bits" are range-bin occupancies and the "signatures" correspond to the known transmitted signal translated to be centered on the appropriate range bin. The analogy is useful: there has been a great deal of recent experience in CDMA, and one of the best and fastest algorithms uses a variant of probabilistic data association (PDA, the target-tracking philosophy). PDA can be augmented by group decision feedback (GDF) -another idea from CDMA -to reÞne the target delay estimates; and Þnally minimum description length (MDL) is applied to estimate the number of targets. Simulation examples are given to illustrate the resolution of closely spaced targets within what would normally be thought the same range bin. Its performance is also compared with the Cramér-Rao lower bound (CRLB) and the alternating projection (AP) algorithm.

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A non-iterative deconvolution method for estimating multipath channel responses

Cited by 25 publications

References 2 publications

Multipath time-of-arrival estimation via modified projection onto convex sets

Multipath time-of-arrival estimation via modified projection onto convex sets

A Fast Algorithm for Sparse Channel Estimation via Orthogonal Matching Pursuit

<title>High-resolution radar ranging for multiple targets</title>

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