A Coordinate Descent Framework for Probing Signal Design in Cognitive MIMO Radars

Feraidooni, Mohammad Mahdi; Gharavian, Davood; Alaee-Kerahroodi, Mohammad; Imani, Sadjad

doi:10.1109/lcomm.2020.2971210

Cited by 19 publications

(17 citation statements)

References 25 publications

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“…Equation (13) represents the receiver output for the reflected signal from the p-th target. The first factor of the right side describes spatial processing at the receiver and is not affected by the waveforms γ m (t).…”

Section: Multi-input Multi-output Radar Signal Modelmentioning

confidence: 99%

On optimum multi‐input multi‐output radar signal design: Ambiguity function, manifold structure and duration‐bandwidth

Liu

Davidson

2021

IET Signal Processing

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A design technique is developed for the probing signals of a Multi-Input Multi-Output (MIMO) radar. The concentration of the energy of the signal in its essential duration and essential bandwidth is achieved through the use of a class of time-frequency concentrated functions called the WLJ functions as the synthesizing signal set. The goal is to design a signal vector having a pre-specified desired covariance (CoV) matrix while ensuring that the side-lobes of the ambiguity functions are small. Since CoV matrices are structurally constrained, they form a manifold in the signal space. Hence, we argue that the difference between these matrices should not be measured in terms of the conventional Euclidean distance (ED); rather, the distance should be measured along the surface of the manifold, that is, in terms of a Riemannian distance (RD). In either case, the signal optimisation problem is non-convex in the design variables, involving, respectively, a quartic and a square-root objective function. An efficient algorithm based on successive convex approximation is developed in which the original non-convex problems are transformed so that they can be approximated by a convex quadratically constrained quadratic problem at each stage, resulting in good approximate solutions. Comparing the designs using ED and RD, we find that the convergence of the algorithm can be significantly faster when optimising over the manifold (RD) than when optimising over the whole space (ED). More importantly, for tight constraints, the use of RD yields solutions which satisfy the constraints far better than the use of ED. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Multi-input Multi-output Radar Signal Modelmentioning

confidence: 99%

On optimum multi‐input multi‐output radar signal design: Ambiguity function, manifold structure and duration‐bandwidth

Liu

Davidson

2021

IET Signal Processing

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“…s , we consider the design of its entries consecutively, using the Coordinate Descent (CD) approach [34]- [37], which enables such an optimization by assuming one entry of the code vector s ∈ C N as the variable, keeping all the others fixed. By examining all the possible alphabet for the chosen variable, it selects the option which, with the correspondent filter, leads to the best SINR.…”

Section: Joint Waveform and Receiver Designmentioning

confidence: 99%

Joint Waveform/Receiver Design for Vital-Sign Detection in Signal-Dependent Interference

Beltrao

Alaee-Kerahroodi

Schroeder

et al. 2020

2020 IEEE Radar Conference (RadarConf20)

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This paper presents the joint design of discrete slow-time radar waveform and receive filter, with the aim of enhancing the Signal to Interference and Noise Ratio (SINR) in phase coded radar systems for vital-sign monitoring. Towards this, we consider maximizing the SINR at the input of the vitalsign estimation block, when transmitting hardware efficient Mary Phase Shift Keying (MPSK) sequences. This multi-variable and non-convex optimization problem is efficiently solved based on a Minimum Variance Distortionless Response (MVDR) filter, with the Coordinate Descent (CD) approach for the sequence optimization, and the obtained results have shown attractive interference suppression capabilities, even for the simple binary case.

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“…The joint design focuses on suppressing the interference in different directions through the adaptive beamforming technique, giving full play to the advantages of the transmitter and the receive filter to process the interference, making the radar output SINR maximum. Since the objective function is usually a quadratic function, and the constraints imposed on the waveform are usually non-convex [ 1 , 2 , 3 , 5 , 7 , 11 , 16 , 17 , 24 , 30 , 31 , 32 , 33 , 34 ], it is difficult to solve the optimization problem [ 31 ]. Various methods [ 3 , 11 , 14 , 16 , 23 , 31 , 35 , 36 , 37 ] are used to solve the non-convex optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods [ 3 , 11 , 14 , 16 , 23 , 31 , 35 , 36 , 37 ] are used to solve the non-convex optimization problem. Feraidooni and Gharavian developed in [ 31 ] an algorithm for the joint design of a continuous/discrete [ 33 , 34 , 36 ] phase sequence and space-time receive filter to improve SINR, using the coordinate descent framework to deal with the constrained non-convex problem. Also, another joint design method is designed in [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss

Huang

Deng

Zheng

et al. 2021

Sensors

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In this paper, we aim at the problem that MIMO radar’s target detection performance is greatly reduced in the complex multi-signal-dependent interferences environment. We propose a joint design method based on semidefinite relaxation (SDR), fractional programming and randomization technique (JD-SFR) and a joint design method based on coordinate descent (JD-CD) to solve the actual transmit waveform and receive filter bank directly to reduce the loss of strong interference to the output signal-to-interference-plus-noise ratio (SINR) of the radar system. Therefore, the maximization of output SINR is taken as the criterion of the optimization problem. The designed waveforms take into account the radar transmitter’s hardware requirements for constant envelope waveforms and impose similarity constraints on the waveforms. JD-SFR uses SDR, fractional programming and randomization technique to deal with the non-convex optimization problems encountered in the solution process. JD-CD transforms the optimization problem into a function of the phase of the waveform and then solves the transmit waveform based on CD. Compared with other methods, the proposed method has lower output SINR loss under strong power interference and forms deep nulls on the direction beampattern of multiple interference sources, which indicates that it has better anti-interference performance.

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A Coordinate Descent Framework for Probing Signal Design in Cognitive MIMO Radars

Cited by 19 publications

References 25 publications

On optimum multi‐input multi‐output radar signal design: Ambiguity function, manifold structure and duration‐bandwidth

On optimum multi‐input multi‐output radar signal design: Ambiguity function, manifold structure and duration‐bandwidth

Joint Waveform/Receiver Design for Vital-Sign Detection in Signal-Dependent Interference

Joint Design of Colocated MIMO Radar Constant Envelope Waveform and Receive Filter to Reduce SINR Loss

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