Equilibrium between a statistical MIMO radar and a jammer

Gao, Hao; Wang, Jian; Jiang, Chunxiao; Zhang, Xudong

doi:10.1109/radar.2015.7131043

Cited by 23 publications

(13 citation statements)

References 10 publications

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“…There exists vast literature on the design and analysis of dynamic spectrum sharing, some of which has investigated power allocation games in the presence of a jammer [16]- [23]. The authors investigate equilibrium points in the form of pure strategies [16]- [20] as well as mixed strategies [21], [22] and examine optimal power allocations that maximize the utility functions defined.…”

Section: A Related Workmentioning

confidence: 99%

“…As a matter of fact, they formulate the interactions between a smart jammer and an SU with mixed transmission strategies and apply weighting functions to model the subjectivity of both players in the transmission. Gao et al in [23] investigate the interaction between a statistical multiple-input multiple-output (MIMO) radar and an intelligent target equipped with a jammer. They consider a two-person zero-sum game and a Bayesian game to model the adversarial interaction where assumptions of complete and incomplete information are considered respectively.…”

Section: A Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Strategic Power Allocation With Incomplete Information in the Presence of a Jammer

El-Bardan¹,

Brahma²,

Varshney³

2016

IEEE Trans. Commun.

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In this paper, distributed competitive interactions between a secondary user (SU) transmitter-receiver pair and a jammer are investigated using a game-theoretic framework under physical interference restrictions, power budget constraints, and incomplete knowledge of channel gains. In this game, the SU transmitter is expected to choose its power strategy with the objective of satisfying a minimum signal-to-interference plus noise ratio (SINR) at the corresponding receiver. Similarly, the jammer's objective is to strategically allocate its power so that the SINR constraint of the SU is not satisfied. Due to a lack of complete information, this strategic power allocation problem between the two players is modeled as a Bayesian game for which the self-enforcing strategies of the SU transmitter-receiver pair and the jammer are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game. The solution of the game corresponds to Nash Equilibria (NE) points. Equilibrium analysis is carried out by considering the mixed strategy solution space and numerical examples are presented for illustration.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Strategic Power Allocation With Incomplete Information in the Presence of a Jammer

El-Bardan¹,

Brahma²,

Varshney³

2016

IEEE Trans. Commun.

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“…On the other hand, Song, Willett, Zhou, and Lu () investigate the interaction between a smart MIMO radar and a smart target from a game theory perspective, which is modeled as a two‐person zero‐sum game, and various equilibria solutions are analytically derived. In Gao et al (), the authors extend the results in Song, Willett, Zhou, and Lu () and model the adversarial competition between a statistical MIMO radar and an intelligent jammer as a Bayesian game, where the radar system and the jammer have incomplete information. Furthermore, reference Deligiannis et al () adopts game theory to tackle the problem of competitive power allocation for MIMO radar in the presence of multiple jammers, the main objective of the radar system is to guarantee a desired target detection performance utilizing the minimum transmission power, while the intelligent jammers optimize the jamming power to maximize the interference to the radars.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Jamming Waveform Design for Distributed Multiple‐Radar Architectures Based on Low Probability of Intercept

et al. 2019

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This paper investigates the problem of low probability of intercept (LPI)‐based adaptive jamming waveform design for distributed multiple‐radar architectures. Such a smart jammer system adopts a multibeam working mode, where multiple simultaneous jamming beams are synthesized to interfere with multiple radars independently. The primary objective of the smart jammer is to minimize the total jamming power by optimizing the transmitted jamming waveform while the achieved signal‐to‐interference‐plus‐noise ratio (SINR) and mutual information (MI) between the received echoes from the target at each radar receiver and the target impulse responses are enforced to be below specified thresholds. First, the expressions of SINR and MI are derived to characterize target detection and characterization performance, respectively. Then, two different LPI‐based jamming waveform design strategies are proposed to minimize the total noise jamming power by optimizing the jamming waveform while the achieved SINR/MI is enforced to be below a certain threshold. The resulting optimization problems are solved analytically by employing the technique of Lagrange multipliers. With the aid of some numerical examples, it is illustrated that the two schemes result in different jamming waveform design results, which is useful to guide jamming power allocation for various jamming tasks. It is also shown that the LPI performance of the smart jammer can be efficiently improved by exploiting the proposed jamming waveform design criteria.

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“…They studied the two-person zero-sum game model between MIMO radar and jammer and designed Stackelberg equilibrium solution based on mutual information (MI) criterion. Gao Hao studied the Bayesian game strategy of MIMO radar and jammer from the perspective of incomplete information [20]. But the effects of clutter are not taken into account.…”

Section: Introductionmentioning

confidence: 99%

Radar Waveform Strategy Based on Game Theory

Wang¹,

Zhao²,

Wang³

et al. 2019

RADIOENGINEERING

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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.

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Equilibrium between a statistical MIMO radar and a jammer

Cited by 23 publications

References 10 publications

Strategic Power Allocation With Incomplete Information in the Presence of a Jammer

Strategic Power Allocation With Incomplete Information in the Presence of a Jammer

Adaptive Jamming Waveform Design for Distributed Multiple‐Radar Architectures Based on Low Probability of Intercept

Radar Waveform Strategy Based on Game Theory

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