Taking the edge off quantization: projected back projection in dithered compressive sensing

Xu, Chunlei; Schellekens, Vincent; Jacques, Laurent

doi:10.48550/arxiv.1805.04348

Cited by 1 publication

(2 citation statements)

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“…This problem amounts to estimating a sparse signal, whose support and phases encode the target ranges and angles, from a quantized CS (QCS) model. In particular, we explore the estimation in an extreme bit-rate scenario where every measurement takes a single bit achieved by a uniform scalar quantization combined with a random dithering vector [13,14,15].…”

Section: Introductionmentioning

confidence: 99%

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1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

Feuillen,

Xu,

Vandendorpe

et al. 2018

Preprint

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We present a novel scheme allowing for 2D target localization using highly quantized 1bit measurements from a Frequency Modulated Continuous Wave (FMCW) radar with two receiving antennas. Quantization of radar signals introduces localization artifacts, we remove this limitation by inserting a dithering on the unquantized observations. We then adapt the projected back projection algorithm to estimate both the range and angle of targets from the dithered quantized radar observations, with provably decaying reconstruction error when the number of observations increases. Simulations are performed to highlight the accuracy of the dithered scheme in noiseless conditions when compared to the non-dithered and full 32-bit resolution under severe bit-rate reduction. Finally, measurements are performed using a radar sensor to demonstrate the effectiveness and performances of the proposed quantized dithered scheme in real conditions.

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Section: Introductionmentioning

confidence: 99%

“…Second, we provide theoretical guarantees on the estimation error of multiple targets localization using the projected back projection (PBP) algorithm [16,14,15]. This is achieved by promoting in PBP a joint support between the range profiles observed by the two antennas.…”

Section: Introductionmentioning

confidence: 99%

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

Feuillen,

Xu,

Vandendorpe

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Taking the edge off quantization: projected back projection in dithered compressive sensing

Cited by 1 publication

References 15 publications

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

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