456 research outputs found
Supervised Learning Based Super-Resolution DoA Estimation Utilizing Antenna Array Extrapolation
In this paper, we introduce a novel algorithm that can dramatically reduce
the number of antenna elements needed to accurately predict the direction of
arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed
algorithm predicts the received signal of a large antenna setup using reduced
number of antenna by using coupled dictionary learning. Hence, this enables the
MIMO radar to resolve more paths, which could not be resolved by the fewer
antennas. Specifically, we overcome the problem of inaccurate DOA estimation
due to a small virtual array setup. For example, we can use dictionary learning
to predict 100 virtual array elements using only 25. To evaluate our algorithm,
we used multiple signal classification (MUSIC) as a DOA estimation technique to
estimate the DOA for non coherent multiple targets. The results show that using
the predicted received signal, the proposed algorithm could resolve all the
targets in the scene, which could not been resolved using only the received
signal from the reduced antenna setup.Comment: 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring
Separate DOD and DOA Estimation for Bistatic MIMO Radar
A novel MUSIC-type algorithm is derived in this paper for the direction of departure (DOD) and direction of arrival (DOA) estimation in a bistatic MIMO radar. Through rearranging the received signal matrix, we illustrate that the DOD and the DOA can be separately estimated. Compared with conventional MUSIC-type algorithms, the proposed separate MUSIC algorithm can avoid the interference between DOD and DOA estimations effectively. Therefore, it is expected to give a better angle estimation performance and have a much lower computational complexity. Meanwhile, we demonstrate that our method is also effective for coherent targets in MIMO radar. Simulation results verify the efficiency of the proposed method, particularly when the signal-to-noise ratio (SNR) is low and/or the number of snapshots is small
Efficient Transmit Beamspace Design for Search-free Based DOA Estimation in MIMO Radar
In this paper, we address the problem of transmit beamspace design for
multiple-input multiple-output (MIMO) radar with colocated antennas in
application to direction-of-arrival (DOA) estimation. A new method for
designing the transmit beamspace matrix that enables the use of search-free DOA
estimation techniques at the receiver is introduced. The essence of the
proposed method is to design the transmit beamspace matrix based on minimizing
the difference between a desired transmit beampattern and the actual one under
the constraint of uniform power distribution across the transmit array
elements. The desired transmit beampattern can be of arbitrary shape and is
allowed to consist of one or more spatial sectors. The number of transmit
waveforms is even but otherwise arbitrary. To allow for simple search-free DOA
estimation algorithms at the receive array, the rotational invariance property
is established at the transmit array by imposing a specific structure on the
beamspace matrix. Semi-definite relaxation is used to transform the proposed
formulation into a convex problem that can be solved efficiently. We also
propose a spatial-division based design (SDD) by dividing the spatial domain
into several subsectors and assigning a subset of the transmit beams to each
subsector. The transmit beams associated with each subsector are designed
separately. Simulation results demonstrate the improvement in the DOA
estimation performance offered by using the proposed joint and SDD transmit
beamspace design methods as compared to the traditional MIMO radar technique.Comment: 32 pages, 10 figures, submitted to the IEEE Trans. Signal Processing
in May 201
MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter
Multiple-input multiple-output (MIMO) radar has become a thriving subject of
research during the past decades. In the MIMO radar context, it is sometimes
more accurate to model the radar clutter as a non-Gaussian process, more
specifically, by using the spherically invariant random process (SIRP) model.
In this paper, we focus on the estimation and performance analysis of the
angular spacing between two targets for the MIMO radar under the SIRP clutter.
First, we propose an iterative maximum likelihood as well as an iterative
maximum a posteriori estimator, for the target's spacing parameter estimation
in the SIRP clutter context. Then we derive and compare various
Cram\'er-Rao-like bounds (CRLBs) for performance assessment. Finally, we
address the problem of target resolvability by using the concept of angular
resolution limit (ARL), and derive an analytical, closed-form expression of the
ARL based on Smith's criterion, between two closely spaced targets in a MIMO
radar context under SIRP clutter. For this aim we also obtain the non-matrix,
closed-form expressions for each of the CRLBs. Finally, we provide numerical
simulations to assess the performance of the proposed algorithms, the validity
of the derived ARL expression, and to reveal the ARL's insightful properties.Comment: 34 pages, 12 figure
Cognitive Sub-Nyquist Hardware Prototype of a Collocated MIMO Radar
We present the design and hardware implementation of a radar prototype that
demonstrates the principle of a sub-Nyquist collocated multiple-input
multiple-output (MIMO) radar. The setup allows sampling in both spatial and
spectral domains at rates much lower than dictated by the Nyquist sampling
theorem. Our prototype realizes an X-band MIMO radar that can be configured to
have a maximum of 8 transmit and 10 receive antenna elements. We use frequency
division multiplexing (FDM) to achieve the orthogonality of MIMO waveforms and
apply the Xampling framework for signal recovery. The prototype also implements
a cognitive transmission scheme where each transmit waveform is restricted to
those pre-determined subbands of the full signal bandwidth that the receiver
samples and processes. Real-time experiments show reasonable recovery
performance while operating as a 4x5 thinned random array wherein the combined
spatial and spectral sampling factor reduction is 87.5% of that of a filled
8x10 array.Comment: 5 pages, Compressed Sensing Theory and its Applications to Radar,
Sonar and Remote Sensing (CoSeRa) 201
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