A Cognitive Sub-Nyquist MIMO Radar Prototype

IET Radar, Sonar & Navigation

Eldar

2019

Self Cite

Direction of arrival (DoA) estimation of targets improves with the number of elements employed by a phased array radar antenna. Since larger arrays have high associated cost, area and computational load, there is recent interest in thinning the antenna arrays without loss of far-field DoA accuracy. In this context, a cognitive radar may deploy a full array and then select an optimal subarray to transmit and receive the signals in response to changes in the target environment. Prior works have used optimization and greedy search methods to pick the best subarrays cognitively. In this paper, we leverage deep learning to address the antenna selection problem. Specifically, we construct a convolutional neural network (CNN) as a multi-class classification framework where each class designates a different subarray. The proposed network determines a new array every time data is received by the radar, thereby making antenna selection a cognitive operation. Our numerical experiments show that the proposed CNN structure provides 22% better classification performance than a Support Vector Machine and the resulting subarrays yield 72% more accurate DoA estimates than random array selections. θ = 92 • , φ = 30 • θ = 92 • , φ = 60 • θ = 92 • , φ = 90 • θ = 92 • , φ = 120 • θ = 92 • , φ = 210 • K=3 {1,

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cognitive radar antenna selection via deep learning

Elbir

IET Radar, Sonar & Navigation

Eldar

2019

Self Cite

“…In the analog processing section of hybrid systems, it is common to employ phase shifters with constant modulus. Using analog switches, which are much simpler and cheaper than the phase shifters, it is possible to further make the overall system more energy-efficient by using subarrays of the larger full antenna array [16], [17]. Optimal selection of subarray elements reduces the power consumption of the analog phase shifters and low-noise amplifiers (LNAs) [18]- [20].…”

Section: Introductionmentioning

confidence: 99%

Joint Antenna Selection and Hybrid Beamformer Design Using Unquantized and Quantized Deep Learning Networks

Elbir

IEEE Trans. Wireless Commun.

2020

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142

116

In millimeter wave communications, multiple-inputmultiple-output (MIMO) systems use large antenna arrays to achieve high gain and spectral efficiency. These massive MIMO systems employ hybrid beamformers to reduce power consumption associated with fully digital beamforming in large arrays. Further savings in cost and power is possible through use of subarrays. Unlike prior works which resort to large latency methods such as optimization and greedy search for subarray selection, we propose a deep-learning-based approach in order to overcome the complexity issue without causing significant performance loss. We formulate antenna selection and hybrid beamformer design as a classification/prediction problem for convolutional neural networks (CNNs). For antenna selection, the CNN accepts the channel matrix as input and outputs a subarray with an optimal spectral efficiency. The resultant subarray channel matrix is then again fed to a CNN to obtain analog and baseband beamformers. We train the CNNs with several noisy channel matrices that have different channel statistics in order to achieve a robust performance at the network output. Numerical experiments show that our CNN framework provides an order better spectral efficiency and is 10 times faster than the conventional techniques. Further investigations with quantized-CNNs show that the proposed network, saved in no more than 5 bits, is also suited for digital mobile devices.Index Terms-Antenna selection, CNN, deep learning, hybrid beamforming, massive MIMO.A. M. E. is with the

“…In [19], targets' ranges, velocities, and directions were recovered in a new radar structure called Sub-Nyquist MIMO Radar (SUMMeR) by thinning a colocated MIMO array and collecting low-rate samples at each receiver element. The SUMMeR system was also implemented in a hardware prototype [9,20].…”

Section: Introductionmentioning

confidence: 99%

TenDSuR: Tensor-Based 4D Sub-Nyquist Radar

IEEE Signal Process. Lett.

Liu

et al. 2019

Self Cite

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 resolutions. We formulate the radar signal model wherein the received echo samples are represented by a partial third-order tensor. We then apply compressed sensing in the tensor domain and use our tensor-OMP and tensor completion algorithms for signal recovery. Our numerical experiments demonstrate joint estimation of all three target parameters at the same native resolutions as a conventional radar but with reduced measurements. Furthermore, tensor completion methods show enhanced performance in off-grid target recovery with respect to tensor-OMP. IndexTerms-sub-Nyquist MIMO radar, tensor decomposition, spectrum sharing, canonical polyadic decomposition (CPD), parallel factor analysis (PARAFAC)