Evaluation method for impact of jamming on radar based on expert knowledge and data mining

Li, Tingpeng; Wang, Zelong; Liu, Jiying

doi:10.1049/iet-rsn.2020.0141

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…However, in the real world, behavioral decision-making is often affected by multiple factors, and the interaction of behavior is also complex and diverse. Only linear expressions of the relationship between the two cannot meet the needs [ 29 , 30 ]. Therefore, in the present work, the deep learning algorithm is applied to the education field to meet the learning needs of students.…”

Section: Educational Theories and Personalized Recommendation Systemmentioning

confidence: 99%

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Yang

2022

PLoS ONE

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To improve the teaching effect of western music history, the curriculum reform of history education needs to be promoted under the background of the Internet of Things (IoT). At first, a discussion is made on the characteristics of history course, which is combined with the characteristics of teaching data easy to collect under the background of IoT. An analysis is conducted on the related theory of educational data mining. Then, the concept of personalized recommendation is proposed based on deep learning (DL) algorithm. Finally, online and offline experiments are designed to verify the performance of the algorithm from review and investigation, smoothness, and participation of difficulty. The research results show that in terms of offline recommendation accuracy, the average record length in Math data set is 24.5, which is much smaller than that in range data set. The research has obvious innovation significance compared with other studies. In the process of target review and investigation, it is found that the research method here involves a wider range of knowledge and higher reliability. In terms of the difficulty of recommending questions, the Deep Reinforcement Exercise (DRE) recommendation algorithm can adaptively adjust the difficulty of recommending questions. It also allows students to set different learning goals through participation goals. But in the experiments on Math data set, Step 10’s recommendation results are not very good, and the difficulty level varies greatly. If the goal setting is high, the problem recommended to students is too difficult, students may answer these questions wrongly, forcing the algorithm to adjust the difficulty adaptively. According to the above results, DRE recommendation algorithm can adapt to different learning needs and customize the recommendation results, thus opening up a new path for the teaching of western music history. Besides, the combination of DL algorithm and western music history teaching design can recommend learning materials, which is of great significance in the teaching of history courses.

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Section: Educational Theories and Personalized Recommendation Systemmentioning

confidence: 99%

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Yang

2022

PLoS ONE

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“…In numerical experiments of TF analysis, we assume that four targets with the same parameters to the aforementioned target are located at 10km, 10.5km, 11km, and 12.3km, respectively, and that the blanket jamming has the SJR of -15dB. For the proposed RTFA, the model in (18), with length-63 signal patches and the parameter = 10 −3 , is numerically solved by basis pursuit algorithm with stopping thresholding 10 −6 . As compared methods, smooth pseudo Wigner-Ville (SPWV) distribution and STFT are exploited, where the length-63 'Kaiser' time window or/and the length-15 'Kaiser' frequency window are used.…”

Section: A Rtfamentioning

confidence: 99%

“…For instance, wavelets weight correlation coefficient of signals has been exploited to demonstrate the jamming effectiveness for moving tracked vehicles [16]. Indeed, many popular physical quantities, such as bandwidth, SNR, SJR, entropy, etc., are always used to measure the quality of signals with jamming [17], [18]. These signal-level evaluation methods, however, quickly become impractical as they often must require distinct signal models and algorithms or rely on good observation conditions (e.g.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Effect of Blanket Jamming on Radar Via Robust Time-Frequency Analysis and Peak to Average Power Ratio

Wang

Liu

2020

IEEE Access

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Evaluating the effect of blanket jamming is at the core of performance analysis and jamming/anti-jamming design for radar. Restricted to diverse jamming types and radar's applications, it is challenging to put forward a unified framework for quantitative evaluation. To address this issue, we come up with a composite evaluation by combining the robust time-frequency analysis (RTFA) and peak to average power ratio (PAPR). In term of signal-level evaluation, RTFA is exploited to analyze the echoes directly, providing the time-frequency (TF) spectrum for calculating two-dimensional image entropy. For system/application-level evaluation, we derive the variation of signal to jamming ratio (SJR) in radar processing chain, and thus define the PAPR to associate SJR with target detection, a type of common and fundamental application that usually affects the other subsequent ones. To refine composite evaluation, we modify the traditional RTFA by leveraging joint sparse model with convolution framelets to improve TF concentration and to avoid the crossing terms; meanwhile, we derive the quantitative relationship between SJR and detection probability, leading to theoretical guarantee of PAPR for evaluation. Finally, the feasibility and the superiority of the proposed evaluation approach are validated in numerical experiments.

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“…Currently, experts and researchers have conducted preliminary investigations on the evaluation of jamming effectiveness. Various conventional methods have been employed in the assessment of radar jamming effectiveness, such as the hierarchical analysis method [4], expert knowledge [5], fuzzy comprehensive assessment method [6] and Entropy Method [7], etc. Most of these methods rely on manual assignment and expert evaluation, and there are a large number of subjective factors.…”

Section: Introductionmentioning

confidence: 99%

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM

Yang,

Wang,

Cheng

et al. 2024

Preprint

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Cooperative jamming effectiveness evaluation is a key component in completing the cooperative jamming OODA loop. For the problem of evaluating the effectiveness of cooperative jamming to group network radar by formation aircraft, a cooperative jamming effectiveness evaluation method based on Improved Particle Swarm Optimization- Extreme Learning Machine (IPSO-ELM) is proposed. Firstly, based on the working parameters of the group network radar and the information fusion rules, the cooperative jamming effectiveness evaluation function is established. On this basis, the cooperative jamming decision schemes and their corresponding cooperative jamming effectiveness values are solved at different locations in the target space, and the results are detected as outliers using box plots, thus constructing sample data for cooperative jamming effectiveness evaluation. Subsequently, a neural network based on Extreme Learning Machine (ELM) methodology is developed, with its initial weights and biases fine-tuned through an improved particle swarm optimization, which is termed IPSO-ELM. This optimization aims to boost the model’s predictive precision. Finally, the IPSO-ELM algorithm is subjected to rigorous assessment via simulation, confirming its performance of accuracy and efficiency. From the simulation results, the advanced performance of the IPSO-ELM algorithm, specifically in the context of assessing the effectiveness of cooperative jamming is verified.

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Evaluation method for impact of jamming on radar based on expert knowledge and data mining

Cited by 9 publications

References 29 publications

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Data analysis and personalized recommendation of western music history information using deep learning under Internet of Things

Evaluating Effect of Blanket Jamming on Radar Via Robust Time-Frequency Analysis and Peak to Average Power Ratio

Research on the evaluation method of cooperative jamming effectiveness based on IPSO-ELM

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