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pro vyhledávání: '"Wang, Xiqin"'
We study the problem of representing a discrete tensor that comes from finite uniform samplings of a multi-dimensional and multiband analog signal. Particularly, we consider two typical cases in which the shape of the subbands is cubic or parallelepi
Externí odkaz:
http://arxiv.org/abs/2405.12064
The compressed sensing (CS) model can represent the signal recovery process of a large number of radar systems. The detection problem of such radar systems has been studied in many pieces of literature through the technology of debiased least absolut
Externí odkaz:
http://arxiv.org/abs/2306.17372
We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, e
Externí odkaz:
http://arxiv.org/abs/2212.04707
Compressed sensing (CS) model of complex-valued data can represent the signal recovery process of a large amount types of radar systems, especially when the measurement matrix is row-orthogonal. Based on debiased least absolute shrinkage and selectio
Externí odkaz:
http://arxiv.org/abs/2209.15273
Publikováno v:
Digital Signal Processing, Volume 118, 2021, 103204, ISSN 1051-2004
Automotive radar is a key component in an ADAS. The increasing number of radars implemented in vehicles makes interference between them a noteworthy issue. One method of interference mitigation is to limit the TBP of radar waveforms. However, the pro
Externí odkaz:
http://arxiv.org/abs/2009.01437
Nowadays, mutual interference among automotive radars has become a problem of wide concern. In this paper, a decentralized spectrum allocation approach is presented to avoid mutual interference among automotive radars. Although decentralized spectrum
Externí odkaz:
http://arxiv.org/abs/2001.02629
Autor:
Tee, Augustine, Choo, Bryan Peide, Gokhale, Roshni Sadashiv, Wang, Xiqin, Mansor, Mashithah, Oh, Hong Choon, Jones, Daryl
Publikováno v:
In Resuscitation Plus December 2023 16
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The concept of cognitive radar (CR) enables radar systems to achieve intelligent adaption to a changeable environment with feedback facility from receiver to transmitter. However, the implementation of CR in a fast-changing environment usually requir
Externí odkaz:
http://arxiv.org/abs/1904.10739
We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while simultaneously recovering the target's direction, Doppler velocity, and range without loss of native re
Externí odkaz:
http://arxiv.org/abs/1808.10638