MIMO radar with broadband waveforms: Smearing filter banks and 2D virtual arrays

Vaidyanathan, P.P.; Pal, Piya; Chen, Chun-Yang

doi:10.1109/acssc.2008.5074389

Cited by 9 publications

(12 citation statements)

References 14 publications

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“…and for all τ 0 . This property implies that the orthogonal signals cannot overlap in frequency [19], leading to FDMA. Alternatively, time invariant orthogonality can be approximately achieved using CDMA.…”

Section: A Mimo Architecturementioning

confidence: 99%

“…In MIMO radar, two of the most popular approaches to ensure waveform orthogonality are code division multiple access (CDMA) and frequency division multiple access (FDMA). Although the narrowband assumption crucial for MIMO processing can hardly be applied to CDMA waveforms [19], it is typically preferred. This is due to two essential drawbacks presented by FDMA: range-azimuth coupling [20], [21], [22] and limited range resolution to a single waveform's bandwidth [23], [24].…”

Section: Introductionmentioning

confidence: 99%

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SUMMeR: Sub-Nyquist MIMO Radar

Cohen

Eldar

et al. 2018

IEEE Trans. Signal Process.

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Multiple input multiple output (MIMO) radar exhibits several advantages with respect to traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, the digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this work, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a rangeazimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets' parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas' locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.

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Section: A Mimo Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SUMMeR: Sub-Nyquist MIMO Radar

Cohen

Eldar

et al. 2018

IEEE Trans. Signal Process.

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show abstract

“…and for all τ 0 . This property implies that the orthogonal signals cannot overlap in frequency [13], leading to frequency division multiple access (FDMA). Alternatively, time invariant orthogonality can be approximately achieved using code division multiple access (CDMA).…”

Section: Ii-a Summer Architecturementioning

confidence: 99%

Sub-Nyquist pulse Doppler MIMO radar

Cohen¹,

Cohen²,

Eldar³

et al. 2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Pulse Doppler multiple input multiple output (MIMO) radar allows to simultaneously detect targets' range, azimuth and velocity. Achieving high resolution requires a large number of transmit and receive antennas, as well as high sampling rates leading to a torrent of samples. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between single antenna radar signal bandwidth and range resolution. In this work, we present a sub-Nyquist MIMO radar (SUMMeR) system that extends these methods to a multiple antenna setting. We apply the Xampling framework both in time and space, thus reducing both the number of deployed antennas and samples per receiver, without degrading time and spatial resolution, as illustrated in the simulations.

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“…This property implies that the orthogonal signals cannot overlap in frequency [16]. Alternatively, time invariant orthogonality can be approximately achieved using coding division.…”

Section: Mimo Radar Modelmentioning

confidence: 99%

Sub-Nyquist collocated MIMO radar in time and space

Cohen

Eldar

et al. 2016

2016 IEEE Radar Conference (RadarConf)

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Multiple input multiple output (MIMO) radar exhibits several advantages with respect to traditional monostatic radar by exploiting transmit waveform diversity. Achieving high resolution requires a large number of transmit and receive antennas. In addition, the digital processing is performed on samples of the received signal at its Nyquist rate, which can be high. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between monostatic radar signal bandwidth and sampling rate. In this work, we extend theses methods to MIMO radar and apply the Xampling framework both in the time and spatial domains, achieving reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions.

show abstract

MIMO radar with broadband waveforms: Smearing filter banks and 2D virtual arrays

Cited by 9 publications

References 14 publications

SUMMeR: Sub-Nyquist MIMO Radar

SUMMeR: Sub-Nyquist MIMO Radar

Sub-Nyquist pulse Doppler MIMO radar

Sub-Nyquist collocated MIMO radar in time and space

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