2,870 research outputs found
Analysis of Sparse MIMO Radar
We consider a multiple-input-multiple-output radar system and derive a
theoretical framework for the recoverability of targets in the azimuth-range
domain and the azimuth-range-Doppler domain via sparse approximation
algorithms. Using tools developed in the area of compressive sensing, we prove
bounds on the number of detectable targets and the achievable resolution in the
presence of additive noise. Our theoretical findings are validated by numerical
simulations
LiQuiD-MIMO Radar: Distributed MIMO Radar with Low-Bit Quantization
Distributed MIMO radar is known to achieve superior sensing performance by
employing widely separated antennas. However, it is challenging to implement a
low-complexity distributed MIMO radar due to the complex operations at both the
receivers and the fusion center. This work proposed a low-bit quantized
distributed MIMO (LiQuiD-MIMO) radar to significantly reduce the burden of
signal acquisition and data transmission. In the LiQuiD-MIMO radar, the
widely-separated receivers are restricted to operating with low-resolution ADCs
and deliver the low-bit quantized data to the fusion center. At the fusion
center, the induced quantization distortion is explicitly compensated via
digital processing. By exploiting the inherent structure of our problem, a
quantized version of the robust principal component analysis (RPCA) problem is
formulated to simultaneously recover the low-rank target information matrices
as well as the sparse data transmission errors. The least squares-based method
is then employed to estimate the targets' positions and velocities from the
recovered target information matrices. Numerical experiments demonstrate that
the proposed LiQuiD-MIMO radar, configured with the developed algorithm, can
achieve accurate target parameter estimation.Comment: 5 pages, 4 figure
Spatial Compressive Sensing for MIMO Radar
We study compressive sensing in the spatial domain to achieve target
localization, specifically direction of arrival (DOA), using multiple-input
multiple-output (MIMO) radar. A sparse localization framework is proposed for a
MIMO array in which transmit and receive elements are placed at random. This
allows for a dramatic reduction in the number of elements needed, while still
attaining performance comparable to that of a filled (Nyquist) array. By
leveraging properties of structured random matrices, we develop a bound on the
coherence of the resulting measurement matrix, and obtain conditions under
which the measurement matrix satisfies the so-called isotropy property. The
coherence and isotropy concepts are used to establish uniform and non-uniform
recovery guarantees within the proposed spatial compressive sensing framework.
In particular, we show that non-uniform recovery is guaranteed if the product
of the number of transmit and receive elements, MN (which is also the number of
degrees of freedom), scales with K(log(G))^2, where K is the number of targets
and G is proportional to the array aperture and determines the angle
resolution. In contrast with a filled virtual MIMO array where the product MN
scales linearly with G, the logarithmic dependence on G in the proposed
framework supports the high-resolution provided by the virtual array aperture
while using a small number of MIMO radar elements. In the numerical results we
show that, in the proposed framework, compressive sensing recovery algorithms
are capable of better performance than classical methods, such as beamforming
and MUSIC.Comment: To appear in IEEE Transactions on Signal Processin
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
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