Manifold studies on fundamental limits of direction-finding multiple-input multiple-output radar systems

2012 ◽  
Vol 6 (8) ◽  
pp. 708-718 ◽  
Author(s):  
H. Chen ◽  
Y. Peng ◽  
W. Zhou ◽  
X. Li ◽  
J. Yang
Keyword(s):  
Multiple Input Multiple Output ◽  
Direction Finding ◽  
Fundamental Limits ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output

scholarly journals Ground-Based Radar Interferometry: A Bibliographic Review

2019 ◽  
Vol 11 (9) ◽  
pp. 1029 ◽  
Author(s):  
Massimiliano Pieraccini ◽  
Lapo Miccinesi
Keyword(s):  
Multiple Input Multiple Output ◽  
Single Component ◽  
Radar Interferometry ◽  
Radar Systems ◽  
Multiple Input ◽  
Scientific Papers ◽  
Input Multiple Output ◽  
Number Of Publications ◽  
Scientific Topic

Ground-based/terrestrial radar interferometry (GBRI) is a scientific topic of increasing interest in recent years. This article is a bibliographic review, as much complete as possible, of the scientific papers/articles published in the last 20 years, since the pioneering works in the nineties. Some statistics are reported here about the number of publications in the years, popularity of applications, operative modalities, operative bands. The aim of this review is also to identify directions and perspectives. In the opinion of authors, this type of radar systems will move forward faster modulations, wider view angle, MIMO (Multiple Input Multiple Output) systems and radar with capability to detect the vector of displacement and not only a single component.

scholarly journals Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

2021 ◽  
Author(s):  
Pasquale Di Viesti ◽  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Approximate Maximum Likelihood ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

scholarly journals Novel Methods for Approximate Maximum Likelihood Estimation of Multiple Superimposed Undamped Tones and Their Application to Radar Systems

2021 ◽  
Author(s):  
Pasquale Di Viesti ◽  
ALESSANDRO DAVOLI ◽  
Giorgio Guerzoni ◽  
Giorgio Matteo Vitetta
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Multiple Input Multiple Output ◽  
Likelihood Estimation ◽  
Approximate Maximum Likelihood ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Novel Methods

<div>In this manuscript, novel methods for the detection of multiple superimposed tones in noise and the estimation of their parameters are derived, and their application to colocated multiple-input multiple-output radar systems is investigated.</div>

scholarly journals Compressed Sensing in On-Grid MIMO Radar

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Michael F. Minner
Keyword(s):  
Compressive Sensing ◽  
Regularization Method ◽  
Sensor Array ◽  
Multiple Input Multiple Output ◽  
Mimo Radar ◽  
Radar Systems ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Sparsity Constraints ◽  
Recovery Algorithms

The accurate detection of targets is a significant problem in multiple-input multiple-output (MIMO) radar. Recent advances of Compressive Sensing offer a means of efficiently accomplishing this task. The sparsity constraints needed to apply the techniques of Compressive Sensing to problems in radar systems have led to discretizations of the target scene in various domains, such as azimuth, time delay, and Doppler. Building upon recent work, we investigate the feasibility of on-grid Compressive Sensing-based MIMO radar via a threefold azimuth-delay-Doppler discretization for target detection and parameter estimation. We utilize a colocated random sensor array and transmit distinct linear chirps to a small scene with few, slowly moving targets. Relying upon standard far-field and narrowband assumptions, we analyze the efficacy of various recovery algorithms in determining the parameters of the scene through numerical simulations, with particular focus on thel1-squared Nonnegative Regularization method.

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