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Cooperative target allocation for heterogeneous agent models using a matrix-encoding genetic algorithm

Journal of Information and Intelligence 2025年第2期3卷 154-172页

作者： Shan Gao Lei Zuo Xiaofei Lu Bo Tang National Key Laboratory of Radar Signal Processing Xidian University Xi'an 710071 China Jiuquan Satelite Launch Centre Jiuquan 735400 China College of Electronic Engineering National University of Defense Technology Hefei 230037 China

Heterogeneous platforms collaborate to execute tasks through different operational models, resulting in the task allocation problem that incorporates different agent models. In this paper, we address the problem of cooperative target allocation for heterogeneous agent models, where we design the task-agent matching model and the multi-agent routing model. Since the heterogeneity and cooperativity of agent models lead to a coupled allocation problem, we propose a matrix-encoding genetic algorithm (MEGA) to plan reliable allocation schemes. Specifically, an integer matrix encoding is resorted to represent the priority between targets and agents in MEGA and a ranking rule is designed to decode the priority matrix. Based on the proposed encoding-decoding framework, we use the discrete and continuous optimization operators to update the target-agent match pairs and task execution orders. In addition, to adaptively balance the diversity and intensification of the population, a dynamical supplement strategy based on Hamming distance is proposed. This strategy adds individuals with different diversity and fitness at different stages of the optimization process. Finally, simulation experiments show that MEGA algorithm outperforms the conventional target allocation algorithms in the heterogeneous agent scenario.

关键词： Task allocation Heterogeneous agent models Heuristic algorithm Combination optimization

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A signal Coherence Recovery Method for Dechirp Sampling radar

A Signal Coherence Recovery Method for Dechirp Sampling Rada...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jixiang Fu Xinyi He Zijun Kang Wangshuo Tang Yidi Wang Min Xue Zhixin Wu Yuexin Gao Dan Xu Academy of Advanced Interdisciplinary Research Xidian University Xi’an China National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Aerospace Science and Technology Xidian University Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Due to the range gate hopping and Doppler frequency modulation, the coherence of the high-speed targets radar echoes received via dechirp sampling is disrupted, leading to a significant decrease in the accuracy and robustness of radar imaging. To address this issue, a new method for echo coherence recovery in dechirp sampling radars is proposed. Assuming the range gate hops with a fixed step size, the radar’s reference range can be accurately estimated by combining range alignment (RA) and range unwrapping technique. Simultaneously, the target’s range and velocity can also be estimated by using polynomial smoothing. By deriving and analyzing the accurate dechip signal model, the coherence of the echo is restored by compensating for high-speed Doppler modulation, residual video phase (RVP) terms, and residual motion errors through the construction of corresponding functions. Finally, the effectiveness of this method is validated by simulation experiments.

关键词： Analytical models Accuracy Smoothing methods Coherence radar imaging Logic gates Polynomials

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An end-to-end algorithm for predicting missing load data based on waveGAIN

An end-to-end algorithm for predicting missing load data bas...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiang Zhang Gang Zhou Yuwei Feng Sen Yang Wanlin Wang Jiaqing Mo Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The accuracy of short-term load forecasting has improved with the development of deep learning on complete datasets. However, load data is often lost due to various complex factors in the power system, causing it is a challenge for incomplete load forecasting. To address this issue, this paper proposes an end-to-end load-completion and forecasting network. Specially, we designed the Wavelet-enhanced Generative Adversarial Imputation Net (waveGAIN) to improve the accuracy of data completion. Then, we propose an end-to-end training strategy that combines waveGAIN and Bidirectional Long Short-Term Memory (BiLSTM) networks to improve the accuracy of data filling and reduce the load forecasting error. The experimental results show that our proposed method improves the prediction accuracy in the case of different patterns and levels of missing data. And our method achieves good results even in case of 80% missing data.

关键词： Training Deep learning Accuracy Load forecasting Predictive models Data models Power systems

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LinWA:Linear Weights Attention for Time Series Forecasting

LinWA:Linear Weights Attention for Time Series Forecasting

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International Joint Conference on Neural Networks (IJCNN)

作者： Qiang Li Jiwei Qin Dezhi Sun Fei Shi Daishun Cui Jiachen Xie School of Computer Science and Technology Xinjiang Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Transformer has achieved excellent results in the field of time series forecasting. However, some recent studies have pointed out that the existing Transformer models are ineffective in preserving the temporal order information. To solve this problem, we propose a Linear Weighted Attention mechanism (LinWA), a new, simple, and effective attention weights calculation method. Linwa calculates the linear weights and the attention weights, respectively, and combines them to reduce the proportion of the original attention weights to use the order information effectively and improve the forecasting performance. Additionally, we introduce an adaptive parameter adjustment mechanism to adjust the proportion between linear weights and attention weights dynamically. Experimental results on six publicly available datasets show that LinWA preserves temporal order well and improves performance on many Transformer models.

关键词： Adaptation models Attention mechanisms Time series analysis Neural networks Benchmark testing Transformers Forecasting

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Log anomaly detection based on parallel fusion of CNN and GRU

Log anomaly detection based on parallel fusion of CNN and GR...

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International Joint Conference on Neural Networks (IJCNN)

作者： Cheng Li Jiaqing Mo Gang Zhou Guangming Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the development of deep learning and big data technology, system log anomaly detection technology has received widespread attention. Although many methods have achieved good results in anomaly detection in logs, it remains an extremely difficult task mining the rich dependencies hidden within log data from both global and local perspectives. In this paper, we propose a multi-scale parallel fusion network based on GRU and CNN, which more comprehensively captures the overall dependencies of log data. Specifically, we employ BERT to semantize the preprocessed log sequences, subsequently feeding the semantic vectors into the neural networks of a dual-branch CNN and a variant GRU. This approach enables the capture of both local and global features of the log sequences. Finally, we parallelly fuse the outputs of the dual-branch CNN and variant GRU. We conduct extensive experiments and evaluate three well-known benchmark datasets, BGL, Thunderbird, and Spirit. The results show that our method is better than other methods.

关键词： Deep learning Fuses Neural networks Semantics Network architecture Big Data Feature extraction

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Ship Target Micro-Doppler Feature Extracted by Multi-Synchrosqueezing Transform and Inverse Radon Transform

Ship Target Micro-Doppler Feature Extracted by Multi-Synchro...

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2021 CIE International Conference on radar, radar 2021

作者： Li, Ruonan Yang, Zhiwei He, Shun Liao, Guisheng Xidian University National Laboratory of Radar Signal Processing Xi'an China Communication and Information Engineering College Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665498142

Ship targets with oceanic waves appear a unique micro-Doppler (m-D) feature, which is important for target identification. In this paper, we propose a method to extract the features of ship target based on Multi-Synchrosqueezing Transform (MSST) and Inverse radon transform (IRT). Firstly, we analyze the complex relative motion between radar and the ship target. After the envelope correction, the echo is a sinusoidal frequency modulation (SFM) signal mixed with a remained time-varying phase. However, it is a challenge to extract the m-D feature directly. Thus, the MSST is used to enhance the TF spectrum concentration, in order to extract the remained phase with m-D characteristics accurately by IRT. The experimental results prove the effectiveness of the proposed method and the accurate estimation of micro motion features. © 2021 IEEE.

关键词： Inverse radon transform (IRT) Micro-Doppler (m-D) Multi-Synchrosqueezing Transform (MSST) Ship Target

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HiparaLog: Improving Log-based Anomaly Detection through Parameter Feature Integration

HiparaLog: Improving Log-based Anomaly Detection through Par...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guangming Li Jiaqing Mo Gang Zhou Cheng Li Xinjiang Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Anomaly detection methods based on system logs are effective means of maintaining large-scale systems. In recent years, numerous excellent log anomaly detection methods have emerged. However, existing log anomaly detection methods identify anomalous logs either through the single template feature or by combining template features with the contextual information, ignoring the role of parameter value features in log messages. In this paper, we propose a novel log anomaly detection method named HiparaLog. HiparaLog converts each raw log message into a semantic vector, which incorporates both parameter features and template features. It then detects anomalies by employing a self-attention-enhanced GRU model, which has the ability to capture the global dependencies and contextual information within the log sequence. In this way, HiparaLog is able to detect abnormal logs from multiple dimensions. Our experimental results on two of the most challenging datasets demonstrate that HiparaLog achieves superior performance compared to current state-of-the-art methods.

关键词： Accuracy Semantics Neural networks Feature extraction Vectors Large-scale systems Anomaly detection

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Imaging Strategy Design for Multi-Rotor UAV SAR Real Data

Imaging Strategy Design for Multi-Rotor UAV SAR Real Data

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2021 CIE International Conference on radar, radar 2021

作者： Han, Jiahao Tang, Shiyang Ren, Yi Sun, Juan Xidian University National Laboratory of Radar Signal Processing Xi'an China Xi'An Institude of SpaceRadio Technology Xi'an China

ISBN: (纸本)9781665498142

For multi-rotor unmanned aerial vehicles(UAVs) synthetic aperture radar (SAR), motion errors are divided into low-frequency (LF) components and high-frequency (HF) components. In general, the HF errors are rarely considered which will deteriorate the image quality. In order to solve this problem, an imaging strategy for high-resolution multi-rotor UAV SAR is proposed. Motion compensation (MoCo) techniques based on motion sensors and echo signal are applied to compensate for LF motion errors. A data-based phase estimation method is employed to remove the HF errors. After imaging by the range Doppler algorithm (RDA), the phase gradient autofocus (PGA) techniques can achieve further improvements in imaging results. The real data experiments prove the effectiveness of this algorithm for multi-rotor UAV SAR imaging. © 2021 IEEE.

关键词： high-frequency errors Multi-rotor SAR UAV

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Topographic Phase Self-adaptive Compensation for Multi-satellite Formation Coherence radar Systems

Topographic Phase Self-adaptive Compensation for Multi-satel...

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2021 CIE International Conference on radar, radar 2021

作者： Li, Xianghai Yang, Zhiwei He, Shun Tan, Xiao Liao, Guisheng Xidian University National Laboratory of Radar Signal Processing Xi'an China Xi'An University of Science and Technology Communication and Information Engineering College Xi'an China

ISBN: (纸本)9781665498142

In multi-satellite formation synthetic aperture radar (SAR) systems, terrain interferometric phase is a crucial factor that degrades the performance of multi-channels coherence processing. To address this issue, we developed a high-precision self-adaptive topographic phase compensation (TPC) method. It emphasizes minimizing the topographic phase estimation error based on an error feedback weighting correction structure. The self-adaptive weighting coefficient is constructed using image coherence, phase residual distribution, and fringe structural features. This ensures that the phase estimation error component will be corrected in an extremely heterogeneous observation scene. The experimental results of multi-satellite SAR image simulated data demonstrate that the proposed method can significantly improve the accuracy of the topographic phase compensation. © 2021 IEEE.

关键词： error feedback weighting correction fringe structural features rough-and-refined estimation Topographic phase compensation

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Separation of Mixed Near-field and Far-field Sources and DOA Estimation based on Deep Learning

Separation of Mixed Near-field and Far-field Sources and DOA...

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International Conference on Advanced Infocomm technology (ICAIT)

作者： Meibin Qi Weijie Ren Houhong Xiang Institude of Signal and Information Processing Hefei University of Technology Hefei China Radar Technology and Innovative Application Laboratory Hefei University of Technology Hefei China

Aiming at the common mixed sources including near-field(NF) and far-field(FF), this paper proposes a mixed sources direction-of-arrival(DOA) estimation method based on Deep Learning(DL). By using the real part and imaginary part of the mixed sources as the input of the neural network, after full separation, the expected FF signals and NF signals can be obtained at the output of the network. Then, the traditional estimation methods for a single source can be used to estimate DOA. The proposed method optimizes some defects existing in the estimation of mixed sources containing near and far fields in the past, such as the inability to separate the mixed sources effectively, and a large loss of array aperture. Simulation results show that the proposed method is effective. Under different SNR or channel error conditions, the proposed method has better DOA estimation accuracy and generalization ability than most super-resolution DOA estimation algorithms.

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