1,910 research outputs found
Compressive Sensing for MIMO Radar
Multiple-input multiple-output (MIMO) radar systems have been shown to
achieve superior resolution as compared to traditional radar systems with the
same number of transmit and receive antennas. This paper considers a
distributed MIMO radar scenario, in which each transmit element is a node in a
wireless network, and investigates the use of compressive sampling for
direction-of-arrival (DOA) estimation. According to the theory of compressive
sampling, a signal that is sparse in some domain can be recovered based on far
fewer samples than required by the Nyquist sampling theorem. The DOA of targets
form a sparse vector in the angle space, and therefore, compressive sampling
can be applied for DOA estimation. The proposed approach achieves the superior
resolution of MIMO radar with far fewer samples than other approaches. This is
particularly useful in a distributed scenario, in which the results at each
receive node need to be transmitted to a fusion center for further processing
Target Estimation in Colocated MIMO Radar via Matrix Completion
We consider a colocated MIMO radar scenario, in which the receive antennas
forward their measurements to a fusion center. Based on the received data, the
fusion center formulates a matrix which is then used for target parameter
estimation. When the receive antennas sample the target returns at Nyquist
rate, and assuming that there are more receive antennas than targets, the data
matrix at the fusion center is low-rank. When each receive antenna sends to the
fusion center only a small number of samples, along with the sample index, the
receive data matrix has missing elements, corresponding to the samples that
were not forwarded. Under certain conditions, matrix completion techniques can
be applied to recover the full receive data matrix, which can then be used in
conjunction with array processing techniques, e.g., MUSIC, to obtain target
information. Numerical results indicate that good target recovery can be
achieved with occupancy of the receive data matrix as low as 50%.Comment: 5 pages, ICASSP 201
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