Game theoretic analysis for MIMO radars with multiple targets

Deligiannis, Anastasios; Lambotharan, Sangarapillai; Chambers, Jonathon A.

doi:10.1109/taes.2016.150699

Cited by 43 publications

(34 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last two terms of (8) cause the nonconcavity of the secrecy rate function. By exploiting Taylor series expansion, we can approximate (8), as shown in (9). It is apparent that (9) is concave with regard to W 1 and W 2 since the first two terms are concave functions and the rest are either constant or affine.…”

Section: A Secrecy Rate Maximizationmentioning

confidence: 99%

“…whereSR is defined in (9). The solution of (10) is dependent on the selection of the initial valuesW 1 andW 2 and is…”

Section: A Secrecy Rate Maximizationmentioning

confidence: 99%

“…MIMO radar technology is exploited in the following dominant fields: beamforming, waveform design, target-detection optimization, and radar imaging [4]- [6]. Among the advantages of MIMO radar technology is the direct applicability of adaptive array techniques, adaptive beamforming [7], power allocation optimization [8], higher angular resolution, multiple targets detection [9], and the ability to acquire the target's geometrical characteristics through the spatial diversity of the target's radar cross section (RCS).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Secrecy Rate Optimizations for MIMO Communication Radar

Deligiannis

Daniyan

Lambotharan

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

In this paper, we investigate transmit beampattern optimization techniques for a multiple-input multiple-output radar in the presence of a legitimate communications receiver and an eavesdropping target. The primary objectives of the radar are to satisfy a certain target-detection criterion and to simultaneously communicate safely with a legitimate receiver by maximizing the secrecy rate against the eavesdropping target. Therefore, we consider three optimization problems, namely target return signal-to-interference-plus-noise ratio maximization, secrecy rate maximization, and transmit power minimization. However, these problems are nonconvex due to the nonconcavity of the secrecy rate function, which appears in all three optimizations either as the objective function or as a constraint. To solve this issue, we use Taylor series approximation of the nonconvex elements through an iterative algorithm, which recasts the problem as a convex problem. Two transmit covariance matrices are designed to detect the target and convey the information safely to the communication receiver. Simulation results are presented to validate the efficiency of the aforementioned optimizations.

show abstract

Section: A Secrecy Rate Maximizationmentioning

confidence: 99%

“…whereSR is defined in (9). The solution of (10) is dependent on the selection of the initial valuesW 1 andW 2 and is…”

Section: A Secrecy Rate Maximizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Secrecy Rate Optimizations for MIMO Communication Radar

Deligiannis

Daniyan

Lambotharan

et al. 2018

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…In later work conducted by Panoui and Deligiannis, the power allocation schemes of Multistatic MIMO radar network based on different game models were investigated. Generalized Nash equilibrium, Stackelberg Nash equilibrium and Bayesian Nash equilibrium power strategies were solved to achieve high signal-to-noise ratio (SNR) gain and save power resources [13][14][15]. Considering the joint design of amplitudes and frequency-hopping codes for frequencyhopping waveforms, Han proposed a game theory framework to improve MIMO radar performance [16].…”

Section: Introductionmentioning

confidence: 99%

Radar Waveform Strategy Based on Game Theory

Wang¹,

Zhao²,

Wang³

et al. 2019

RADIOENGINEERING

View full text Add to dashboard Cite

In this paper, we proposed two waveform design methods based on game theory to address the problem of radar detection performance degradation in electronic warfare. Since radar and jammer are completely hostile, their interaction is modeled as two-person zero-sum game. Signal-to-Interference-plus-Noise Ratio (SINR) criterion is used in formulating the utility functions. The existence of Nash equilibrium in games is verified by mathematical derivation. Different game waveform strategies are designed for different information levels of radar and jammer. Iterative water-filling method and two-step waterfilling method are designed to achieve Cournot equilibrium and Stackelberg equilibrium, respectively. Simulation results reveal that game strategies can bring higher radar detection performance than No game signal, especially when jammer power is lower than radar power. Radar detection probability based on game theory can be increased by up to 10% without changing the power. This demonstrates game strategies have great potentials for radar waveform design in electronic warfare.

show abstract

“…Furthermore, to extend the study in [5], a signal-to-disturbance ratio (SDR) estimation technique was applied in [7]. Three different game theoretic techniques were applied in [8] to address a distributed beamforming and power allocation problem for a radar system in the presence of multiple targets. Specifically, a strategic non-cooperative game, a partially cooperative game and a Stackelberg game were applied to obtain the optimal resource allocation strategy, while satisfying a certain SINR criterion for each of the targets.…”

mentioning

confidence: 99%

Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network

Deligiannis

Panoui

Lambotharan

et al. 2017

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

We investigate a game-theoretic power allocation scheme and perform a Nash equilibrium analysis for a multistatic multiple-input multiple-output radar network. We consider a network of radars, organized into multiple clusters, whose primary objective is to minimize their transmission power, while satisfying a certain detection criterion. Since there is no communication between the distributed clusters, we incorporate convex optimization methods and noncooperative game-theoretic techniques based on the estimate of the signal-to-interference-plus-noise ratio (SINR) to tackle the power adaptation problem. Therefore, each cluster egotistically determines its optimal power allocation in a distributed scheme. Furthermore, we prove that the best response function of each cluster regarding this generalized Nash game belongs to the framework of standard functions. The standard function property together with the proof of the existence of the solution for the game guarantees the uniqueness of the Nash equilibrium. The mathematical analysis based on Karush-Kuhn-Tucker conditions reveals some interesting results in terms of the number of active radars and the number of radars that over satisfy the desired SINRs. Finally, the simulation results confirm the convergence of the algorithm to the unique solution and demonstrate the distributed nature of the system.

show abstract

Game theoretic analysis for MIMO radars with multiple targets

Cited by 43 publications

References 35 publications

Secrecy Rate Optimizations for MIMO Communication Radar

Secrecy Rate Optimizations for MIMO Communication Radar

Radar Waveform Strategy Based on Game Theory

Game-Theoretic Power Allocation and the Nash Equilibrium Analysis for a Multistatic MIMO Radar Network

Contact Info

Product

Resources

About