Range Dimensional Monopulse Approach With FDA-MIMO Radar for Mainlobe Deceptive Jamming Suppression

Tan, Ming; Gong, Jian; Wang, Chunyang

doi:10.1109/lawp.2023.3331573

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…The frequency component FF n equals Taylor(n)∆ f N(n − 1)/2, where Taylor(n) denotes the Taylor window function. We refer readers to [34][35][36] for more details. For the convenience of discussion, we assume a far-field point target located at azimuth angle θ q and range r q without considering interference and noise.…”

Section: Signal Modelmentioning

confidence: 99%

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Geng,

Li,

Dong

et al. 2024

Remote Sensing

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Joint transceiver beamforming is a fundamental and crucial research task in the field of signal processing. Despite extensive efforts made in recent years, the joint transceiver beamforming of frequency diverse array (FDA)-based multiple-input and multiple-output (MIMO) radar has received relatively less attention and is confronted with some tricky challenges, such as range–angle decoupling and the interaction between multiple performance metrics. In this paper, we initially derive the generalized ambiguity function of the FDA-MIMO radar to explore the intrinsic correlation between its waveform design and resolution. Following that, the joint beamforming optimization is formulated as a nonconvex bivariate quadratic programming problem (NBQP) with the aim of maximizing the Signal-to-Interference-Noise Ratio (SINR) of the FDA-MIMO radar system. Building upon this, we introduce an innovative alternating manifold optimization with nested iteration (AMO-NI) algorithm to address the NBQP. By incorporating manifold optimization into iterative updates of transmit waveform and receiving filter, the AMO-NI algorithm considers the interdependencies among the optimization variables. The algorithm efficiently and expeditiously finds global optimum solutions within a finite number of iterations. Compared with other methods, our approach yields a superior beampattern and higher SINR.

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Section: Signal Modelmentioning

confidence: 99%

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Geng,

Li,

Dong

et al. 2024

Remote Sensing

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show abstract

“…Assuming a far-field target located at angle θ and range r, after matched filtering is performed on the receive elements, the received target echo signal by the FDA-MIMO radar at time t can be expressed as follows (more details can be found in [20][21][22]):…”

Section: Signal Modelmentioning

confidence: 99%

“…In contrast to the capability of forming nulls in a specific direction in the beam pattern of phased array radar, by introducing frequency offsets between the elements of the transmitter, FDA allows for the control of nulls in both range and angle dimensions, thus effectively suppressing interference signals from specific directions and ranges. However, the beam pattern of the FDA radar exhibits time-varying characteristics, necessitating the integration of Multiple-Input Multiple-Output (MIMO) technology at the receiver end to achieve an equivalent time-invariant beam pattern [20][21][22], thereby fully leveraging the advantages of the two-dimensional (2D) range-angle beam pattern of the FDA radar.…”

Section: Introductionmentioning

confidence: 99%

A Fast Phase-Only Beamforming Algorithm for FDA-MIMO Radar via Kronecker Decomposition

Chen,

Wang,

Gong

et al. 2024

Electronics

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This paper proposes a fast phase-only beamforming algorithm for frequency diverse array multiple-input multiple-output radar systems. Specifically, we use the Kronecker decomposition to decompose the desired phase-only weight vector into phase-only transmit and receive weight vectors and to decompose the target steering vector into transmit and receive steering vectors. By using the properties of the Kronecker product, the transmit and receive steering vectors and the transmit and receive weight vectors with the Vandermonde structure are decomposed into Kronecker factors with uni-modulus vectors, respectively. On this basis, in order to maintain the mainlobe gain and form a deep null at the desired position, the Kronecker factors are divided into two parts.The first component, referred to as the interference suppression factors, is responsible for creating deep nulls. The second component, known as the signal enhancement factor, maintains the mainlobe gain. We provide an analytical solution with low complexity for the Kronecker factors. This strategy can obtain the phase-only weights while effectively forming a deep null at the desired position. Numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

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“…In addition, the FDA radar can effectively suppress forwarded spoofing jamming located in the main lobe. [20][21][22]. Due to the frequency increment of each array element of the FDA radar, it also has excellent potential to suppress the spectrum-blocking jamming in the main lobe, and it can avoid fixed spectrum-blocking jamming through the transmitting power allocation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

Zhou,

Wang,

Bao

et al. 2024

Remote Sensing

Self Cite

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With the development of electronic warfare technology, the intelligent jammer dramatically reduces the performance of traditional radar anti-jamming methods. A key issue is how to actively adapt radar to complex electromagnetic environments and design anti-jamming strategies to deal with intelligent jammers. The space of the electromagnetic environment is dynamically changing, and the transmitting power of the jammer and frequency diversity array (FDA) radar in each frequency band is continuously adjustable. Both can learn the optimal strategy by interacting with the electromagnetic environment. Considering that the competition between the FDA radar and the jammer is a confrontation process of two agents, we find the optimal power allocation strategy for both sides by using the multi-agent deep deterministic policy gradient (MADDPG) algorithm based on multi-agent reinforcement learning (MARL). Finally, the simulation results show that the power allocation strategy of the FDA radar and the jammer can converge and effectively improve the performance of the FDA radar and the jammer in the intelligent countermeasure environment.

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Range Dimensional Monopulse Approach With FDA-MIMO Radar for Mainlobe Deceptive Jamming Suppression

Cited by 5 publications

References 35 publications

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

Efficiently Refining Beampattern in FDA-MIMO Radar via Alternating Manifold Optimization for Maximizing Signal-to-Interference-Noise Ratio

A Fast Phase-Only Beamforming Algorithm for FDA-MIMO Radar via Kronecker Decomposition

Frequency Diversity Array Radar and Jammer Intelligent Frequency Domain Power Countermeasures Based on Multi-Agent Reinforcement Learning

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