Design of mutually incoherent arrays for DoA estimation via group-sparse reconstruction

Mateos-Nunez, David; Simoni, Renato; González-Huici, María A.; Correas-Serrano, Aitor

doi:10.1109/radar.2019.8835842

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…In order to have a higher angular resolution, a narrower MLW becomes more attractive. Generally speaking, there is a parametric trade-off between MLW and SLL [22], and we expect most of the results to lie on the SLL-MLW Pareto front boundary [24], which is the state at which resources in a given system are optimized in a way that one dimension cannot improve without the second worsening [40]. Moreover, Algorithm 1 and Algorithm 2 have difficulties in parameter selection.…”

Section: B Algorithm 2: Mlw Optimizationmentioning

confidence: 99%

“…Under the constraints of array element spacing, heuristic algorithms have to filter the solutions at each iteration, which leads to decrease in both accuracy and speed. Moreover, it is worth noting that the SLL and MLW are usually trade-offs to be made in the optimization, as illustrated by studies in [22], [24]. Their objective functions often contain multiple metrics weighted together, like αSLL + βMLW, essentially turning the constrained optimization problem into an unconstrained one, which can then be readily solved by heuristic algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SASA: Super-Resolution and Ambiguity-Free Sparse Array Geometry Optimization With Aperture Size Constraints for MIMO Radar

Huan

Liang

et al. 2023

IEEE Trans. Antennas Propagat.

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Section: B Algorithm 2: Mlw Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SASA: Super-Resolution and Ambiguity-Free Sparse Array Geometry Optimization With Aperture Size Constraints for MIMO Radar

Huan

Liang

et al. 2023

IEEE Trans. Antennas Propagat.

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“…Group-sparse reconstruction has been used for radar signal processing, including incoherent sensor fusion in multi-static ISAR [8], passive radar networks [9], tracking [10], and timefrequency estimation [11]. In other work of the authors [13], a design principle based on group-sparse reconstruction is exploited for optimization of antenna positions of mutually incoherent apertures with the effect of sidelobe averaging while maintaining a thin mainlobe with a most efficient use of space available.…”

Section: Workmentioning

confidence: 99%

Height estimation for automotive MIMO radar with group-sparse reconstruction

Simoni¹,

Mateos-Nunez²,

González-Huici³

et al. 2019

Preprint

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A method is developed for sequential azimuth and height estimation of small objects at far distances in front of a moving vehicle using coherent or mutually incoherent MIMO arrays. The model considers phases and amplitudes of superposition of near-field multipath signals produced by specular non-diffusive ground-reflections. The reflection phase shift and power attenuation due to the interaction with the ground is assumed unknown and is estimated jointly. Groupsparsity allows combining measurements along the trajectory of the vehicle provided that the road is flat as well as measurements from multiple incoherent sensors at different locations in the vehicle. This model can be formulated for non-uniform sparse arrays in 2D and 3D with subsets of antennas at the same height and the resulting inverse problem can be approximated with efficient methods such as Block Orthogonal Matching Pursuit. It is shown in simulations that the proposed approach significantly increases estimation accuracy and decreases false alarms, both crucial for the detection of small objects at far distances.

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“…In [11], the authors approach the antenna placement for direction of arrival (DOA) estimation using a CS-based collocated MIMO radar with a reduced number of elements, by bounding the coherence of the resulting matrix. In [12], [13], the authors have suggested a sparse array design based on optimising the Ambiguity function using global optimiser and have conducted an extensive study on comparing arrays of different number of elements and different field of views. The authors in [14] consider the task of antenna placement for a CS-based collocated MIMO radar with linear arrays; they obtain a probability distribution for selecting the antenna locations and also suggest an antenna selection procedure from the distribution that yields an array configuration that results in a sensing matrix of low mutual coherence.…”

Section: Introductionmentioning

confidence: 99%

Array Position Optimisation for Compressed Sensing MIMO Radar based on Mutual Coherence Minimisation

Nagesh

Ender

González-Huici

2022

2022 23rd International Radar Symposium (IRS)

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In this paper, an optimization methodology for repositioning antenna elements of a collocated Compressed Sensing (CS) based Multiple Input Multiple Output (MIMO) radar, to improve target detection performance, by minimizing the mutual coherence of the associated sensing matrix has been suggested. We initialize the problem as a mutual coherence of the sensing matrix resulting from a simple 3Tx/4Rx Uniform Linear Array (ULA) restricted by an array aperture of specified size, and then reposition the elements within the restricted aperture such that the value of mutual coherence reduces. The optimization problem is formulated as minimizing the l∞ norm of the Gramian of the associated sensing matrix, the global optimization solver simulated annealing is considered to solve the nonconvex problem. The optimized array's performance is evaluated against a ULA, Co-prime array, and Sparse array by comparing metrics such as the probability of perfect reconstruction (Recovery percentage) and Recovery error (root mean square error (rmse)) for scenes with multiple targets and different SNR values, using Monte Carlo simulations. The study demonstrates the methodology to generate a random array, which results in low mutual coherence of its respective sensing matrix, which consequently results in improved performance of the CS-MIMO radar.

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Design of mutually incoherent arrays for DoA estimation via group-sparse reconstruction

Cited by 6 publications

References 26 publications

SASA: Super-Resolution and Ambiguity-Free Sparse Array Geometry Optimization With Aperture Size Constraints for MIMO Radar

SASA: Super-Resolution and Ambiguity-Free Sparse Array Geometry Optimization With Aperture Size Constraints for MIMO Radar

Height estimation for automotive MIMO radar with group-sparse reconstruction

Array Position Optimisation for Compressed Sensing MIMO Radar based on Mutual Coherence Minimisation

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