Joint optimisation of transmit beamspace and receive filter in frequency diversity array‐multi‐input multi‐output radar

Zhou, Changlin; Wang, Chunyang; Gong, Jian; Tan, Ming; Bao, Lei; Liu, Mingjie

doi:10.1049/rsn2.12314

Cited by 4 publications

(4 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By observing (15) and ( 16), the received signal of the FDA radar is related to the transmission power allocation, and the spectrum of the transmitted signal can be designed by adjusting it to avoid spectrum jamming. The receiving end of the array adopts the processing architecture of a multi-channel mixing and matching filter.…”

Section: Signal Modelmentioning

confidence: 99%

“…In 2006, Antonik et al proposed the FDA radar [5], which has a small frequency increment between each transmitting array element and produces a directional graph with range-angle-time coupling characteristics [6][7][8]. The FDA radar's performance is enhanced by unique features, such as beamforming [9], target parameter estimation [10,11], detection [12], tracking [13], imaging [14], clutter suppression [15], and low intercept performance [16][17][18][19]. In addition, the FDA radar can effectively suppress forwarded spoofing jamming located in the main lobe.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

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.

show abstract

Section: Signal Modelmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Nevertheless, the antenna radiation pattern of FDA-MIMO radar is a joint transmit-receive radiation pattern. Thanks to the receiving links of orthogonal waveform differring from FDA radar, we can be easy to separate receiving signals to pick up range, and angle information from transmitting steering vector [42,43]. Owing to the simple design of time-dependent matched filtering, we do not need to consider the issue of time-varying in FDA-MIMO radar [44].…”

Section: Prerequisitementioning

confidence: 99%

Highly focussed beampattern synthesis in FDA‐MIMO radar with multicarrier transmission

Geng

Cheng

et al. 2022

IET Radar Sonar & Navi

View full text Add to dashboard Cite

Multicarrier technology is widely used for improving the performance of beampattern synthesis in frequency diverse array multiple-input and multiple-output (FDA-MIMO) Radar. However, how to use multicarrier technology to achieve highly focussed beampattern synthesis is still an open problem. To this end, the authors first establish the system model of FDA-MIMO radar with multicarrier transmission and analyse the parameters influencing beampattern synthesis by Cramér-Rao lower bound derivation. It can be recognised that frequency increments and multicarrier distribution are key parameters hindering highly focussed beampattern synthesis. On top of the investigation, a simple yet effective Multidimensional Non-linear Frequency Component, named multidimensional frequency components (MNFC), is proposed for forming a highly focussed beam. Specifically, MNFC is a two-dimension frequency component for FDA-MIMO radar. The first dimension is the frequency increment weighted by the Dynamic Taylor Window Function that effectively decouples the joint-range-angle beampattern. The second dimension is a non-linear multicarrier weighted by Log with a Linear Coefficient for further optimising dot-shaped beampatterns for target indication with high precision. Finally, numerical simulations and comparative experiments are performed to validate the superiority of the MNFC in beampattern synthesis.

show abstract

“…The target localization problem for a possibly cognitive FDA radar is addressed in [31] by optimizing the transmit signal parameters. In [32], the transmit beamspace and receive filter of an FDA-MIMO radar are optimized to maximize the SINR at the radar receiver end. Moreover, in the context of moving target detection, a cognitive design of transmitter and receive filter for an FDA-MIMO radar is investigated in [33].…”

Section: Introductionmentioning

confidence: 99%

FDA-MIMO Transceiver Design Under the Uniform Frequency Increment Constraint

Lan,

Rosamilia,

Aubry

et al. 2024

Trans. Rad. Sys.

View full text Add to dashboard Cite

This paper investigates the joint optimization of transmit parameters and receive filter in a Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar system with a uniform frequency increment from sensor to sensor. The problem is formulated as the maximization of the Signal-to-Interference-plus-Noise Ratio (SINR) at the output of the receive filter in a signal-dependent clutter environment, taking into account some practical constraints on the probing waveform and frequency increment. To tackle the resulting non-convex and NPhard optimization problem, a Minorization-Maximization (MM)-Maximum Block Improvement (MBI) algorithm is developed, which iteratively updates the variables block that yields the maximum increase of the objective function while keeping the others fixed. The convergence properties of the proposed algorithm are rigorously studied, and the computational complexity is analyzed. Numerical results demonstrate the effectiveness of the designed procedure under several clutter scenarios of practical relevance, including proper comparisons with counterparts.

show abstract

Joint optimisation of transmit beamspace and receive filter in frequency diversity array‐multi‐input multi‐output radar

Cited by 4 publications

References 29 publications

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

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

Highly focussed beampattern synthesis in FDA‐MIMO radar with multicarrier transmission

FDA-MIMO Transceiver Design Under the Uniform Frequency Increment Constraint

Contact Info

Product

Resources

About