Radar Main-Lobe Jamming Suppression Based on Adaptive Opposite Fireworks Algorithm

Luo, Weilin; Jin, Haoran; Li, Hao; Duan, Keqing

doi:10.1109/ojap.2020.3036878

Cited by 11 publications

(7 citation statements)

References 31 publications

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“…The separation accuracy of signals of the proposed method is investigated and compared with JADE [11], Weights‐Adjusted Second‐Order Blind Identification (WASOBI) [13], SOBI [10] and NC‐FastICA [7]. The similarity coefficients [12] versus input SNR obtained by five methods are shown in Figure 4(a). When the similarity coefficient is closer to 1, the separation accuracy will be better.…”

Section: Performance Analysismentioning

confidence: 99%

“…The traditional radar anti-jamming methods [10][11][12][13]20] use PC technology to detect the target echo after BSS. However, when there is deceptive jamming, both the jamming channel and the target echo channel have obvious spikes after PC, so the target echo and deceptive jamming cannot be distinguished.…”

Section: Identification Of Target and Jamming Signals Via Tjinetmentioning

confidence: 99%

“…A robust whitening BSS method is proposed [9], which can eliminate the influence of noise, but the convergence is poor. Recently years, the BSS method has been applied to the field of radar anti-jamming [10][11][12][13]20]. However, the previous research did not involve the identification of both target echo and complex multi-jamming after BSS, but deceptive jamming will show spikes after pulse compression (PC) and affect the detection of target echo.…”

Section: Introductionmentioning

confidence: 99%

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Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

Gao

Yang

Lan

et al. 2021

IET Radar Sonar & Navi

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The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.

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Section: Performance Analysismentioning

confidence: 99%

Section: Identification Of Target and Jamming Signals Via Tjinetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

Gao

Yang

Lan

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…The method was used for optimizing the various antenna radiation patterns, as well as minimizing the difference between the desired and actual radiation direction. Moreover, Luo et al [23] leveraged the Adaptive Opposite Fireworks Algorithm in order to suppress the interference caused by the main lobe in radar systems. In [24], the authors developed a beamforming technique that is able to achieve fast data updates using the conformal phased array radar system.…”

Section: Introductionmentioning

confidence: 99%

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

Pliatsios

Goudos

Λάγκας

et al. 2022

IEEE Open J. Antennas Propag.

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The emergence of next-generation internet-of-things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.

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“…Wang et al [ 14 ] employed the improved artificial bee colony (ABC) optimization as the optimization algorithm for ICA, despite the fact that this optimization algorithm is very parameter dependent. Luo et al [ 15 , 16 ] applied the improved fireworks algorithm (FA) to the radar signal processing, while the fireworks algorithm is prone to local extremum. Wen et al [ 17 ] used a genetic algorithm (GA) to ICA, although the local search capability of GA is limited.…”

Section: Introductionmentioning

confidence: 99%

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

Xia

Ding

Zhang

et al. 2022

Sensors

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The conventional blind source separation independent component analysis method has the problem of low-separation performance. In addition, the basic butterfly optimization algorithm has the problem of insufficient search capability. In order to solve the above problems, an independent component analysis method based on the double-mutant butterfly optimization algorithm (DMBOA) is proposed in this paper. The proposed method employs the kurtosis of the signal as the objective function. By optimizing the objective function, blind source separation of the signals is realized. Based on the original butterfly optimization algorithm, DMBOA introduces dynamic transformation probability and population reconstruction mechanisms to coordinate global and local search, and when the optimization stagnates, the population is reconstructed to increase diversity and avoid falling into local optimization. The differential evolution operator is introduced to mutate at the global position update, and the sine cosine operator is introduced to mutate at the local position update, hence, enhancing the local search capability of the algorithm. To begin, 12 classical benchmark test problems were selected to evaluate the effectiveness of DMBOA. The results reveal that DMBOA outperformed the other benchmark algorithms. Following that, DMBOA was utilized for the blind source separation of mixed image and speech signals. The simulation results show that the DMBOA can realize the blind source separation of an observed signal successfully and achieve higher separation performance than the compared algorithms.

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Radar Main-Lobe Jamming Suppression Based on Adaptive Opposite Fireworks Algorithm

Cited by 11 publications

References 31 publications

Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

Radar main‐lobe jamming suppression and identification based on robust whitening Blind Source Separation and Convolutional Neural Networks

Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

Blind Source Separation Based on Double-Mutant Butterfly Optimization Algorithm

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