10 results on '"CHEN Zengping"'
Search Results
2. Joint waveform design and resource allocation strategy for cognitive radar target situation awareness.
- Author
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Song, Yuxiao, Tian, Biao, Wang, Rongqing, Xu, Shiyou, and Chen, Zengping
- Subjects
RADAR cross sections ,RADAR signal processing ,RADAR targets ,MULTIPLE target tracking ,SITUATIONAL awareness - Abstract
Traditional radar systems use fixed patterns and constant electromagnetic wave transmission to illuminate targets, but they often do not effectively use prior information about targets and consume significant radar resources. Cognitive radar has emerged as a way to improve resource efficiency and address these shortcomings. A joint waveform design and resource allocation strategy for cognitive radar that incorporates target situational awareness is proposed. This method integrates the interacting multiple model algorithm and the Unscented Kalman Particle Filter to achieve target situation awareness as prior knowledge. By combining the target attitude and the frequency response function of the target radar cross section at different time points in the prior knowledge, a joint beam control and power allocation strategy is formulated and transformed into an optimization problem. In addition, a cognitive pulse‐to‐pulse frequency agile waveform design method is proposed to support multiple target tracking under complex motion models. Simulation experiments demonstrate the effectiveness of this approach in obtaining accurate target situation information, achieving beam control, and optimizing power allocation. The designed waveforms can enhance radar target detection performance and improve low probability of intercept characteristics by adjusting the pulse repetition interval. This method has significant technical value. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
3. A Step-Wise Domain Adaptation Detection Transformer for Object Detection under Poor Visibility Conditions.
- Author
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Zhang, Gege, Wang, Luping, and Chen, Zengping
- Subjects
TRANSFORMER models ,KNOWLEDGE transfer ,WEATHER ,LIGHTING - Abstract
To address the performance degradation of cross-domain object detection under various illumination conditions and adverse weather scenarios, this paper introduces a novel method a called Step-wise Domain Adaptation DEtection TRansformer (SDA-DETR). Our approach decomposes the adaptation process into three sequential steps, progressively transferring knowledge from a labeled dataset to an unlabeled one using the DETR (DEtection TRansformer) architecture. Each step precisely reduces domain discrepancy, thereby facilitating effective transfer learning. In the initial step, a target-like domain is constructed as an auxiliary to the source domain to reduce the domain gap at the image level. Then, we adaptively align the source domain and target domain features at both global and local levels. To further mitigate model bias towards the source domain, we develop a token-masked autoencoder (t-MAE) to enhance target domain features at the semantic level. Comprehensive experiments demonstrate that the SDA-DETR outperforms several popular cross-domain object detection methods on three challenging public driving datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
4. Rumen Development of Tianhua Mutton Sheep Was Better than That of Gansu Alpine Fine Wool Sheep under Grazing Conditions
- Author
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Li, Dengpan, primary, Liu, Zhanjing, additional, Duan, Xinming, additional, Wang, Chunhui, additional, Chen, Zengping, additional, Zhang, Muyang, additional, Li, Xujie, additional, and Ma, Youji, additional
- Published
- 2024
- Full Text
- View/download PDF
5. Deep-Reinforcement-Learning-Based Radar Parameter Adaptation for Multiple-Target Tracking
- Author
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Huang, Yongbing, Guo, Rui, Zhang, Yue, and Chen, Zengping
- Abstract
The field of cognitive radar seeks to improve performance in different scenarios using adaptive means based on environmental information. This work focuses on the problem of radar parameter adaptation to ensure that targets are not lost and improve tracking accuracy in multiple-target tracking (MTT) scenarios with variable target numbers and multiple target types. The dimensionality increase due to multiple targets and the complexity due to multidimensional parameter space are the difficulties of the MTT scenario optimization problem, whereas the variation of the number of targets in a specific scenario is also a problem to be investigated. A deep reinforcement learning (DRL)-based radar parameter adaptation approach is proposed to solve the problem. The complexity is reduced by dividing the parameter space and using two agents in cooperation to perform parameter selection. We design the reward functions for each agent separately and adopt additional information for training to enhance the training efficiency. An improved DRL agent using a long short-term memory (LSTM) network and a self-attention mechanism is proposed to deal with the problem of target number variation and multiple target types. Simulation results demonstrate the performance of the proposed approach, achieving almost no target tracking loss in simulation scenarios, optimal performance compared with the criterion-based methods and the fixed parameter method, and obtaining about 90% improvement in range and velocity accuracy compared with the fixed parameter method, whereas the processing time is only 0.48% of that of the grid search criterion-based methods.
- Published
- 2024
- Full Text
- View/download PDF
6. Deep Learning Models for Spectrum Prediction: A Review
- Author
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Wang, Lei, Hu, Jun, Zhang, Chudi, Jiang, Rundong, and Chen, Zengping
- Abstract
Spectrum prediction is a promising technique for improving spectrum exploitation in cognitive radio networks (CRNs). Accurate spectrum prediction can assist in reducing the energy consumption of spectrum sensing and improving the network throughput, etc. Recently, a significant amount of research efforts has been devoted to this area, especially deep learning (DL) methods, and greatly advanced spectrum prediction abilities. This article reviews the state-of-the-art developments in DL for spectrum prediction. Specifically, we first summarize the existing spectrum prediction methods and give a taxonomy from different aspects. Second, we formulate long-term, multidimensional, and nonideal spectrum prediction problems to provide theoretical support for spectrum prediction models. Third, we divide the DL models into four categories: basic models, models for the long-term domain, models for the multidimensional domain, and models for the nonideal domain based on application scenarios and offer an in-depth tutorial on these models. Fourth, we comprehensively collect and organize widely used common spectrum data sources in the existing literature and also overview the spectrum data simulation methods to facilitate other researchers. Furthermore, we summarize and analyze the selection of evaluation metrics for spectrum prediction models based on DL. Finally, we summarize the research trends and highlight the critical research challenges.
- Published
- 2024
- Full Text
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7. Spatial–Temporal Joint Design and Optimization of Phase-Coded Waveform for MIMO Radar.
- Author
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Lei, Wei, Zhang, Yue, Chen, Zengping, Chen, Xiaolong, and Song, Qiang
- Subjects
MIMO radar ,LEAST squares ,DEGREES of freedom ,NONLINEAR equations ,RADAR ,PROBLEM solving - Abstract
By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method's effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
8. A Deep Long-Term Joint Temporal–Spectral Network for Spectrum Prediction.
- Author
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Wang, Lei, Hu, Jun, Jiang, Rundong, and Chen, Zengping
- Subjects
DEEP learning ,COGNITIVE radio ,RADIO networks ,FORECASTING ,PREDICTION models - Abstract
Spectrum prediction is a promising technique to release spectrum resources and plays an essential role in cognitive radio networks and spectrum situation generating. Traditional algorithms normally focus on one-dimensional or predict spectrum values in a slot-by-slot manner and thus cannot fully perceive the spectrum states in complex environments and lack timeliness. In this paper, a deep learning-based prediction method with a simple structure is developed for temporal–spectral and multi-slot spectrum prediction simultaneously. Specifically, we first analyze and construct spectrum data suitable for the model to simultaneously achieve long-term and multi-dimensional spectrum prediction. Then, a hierarchical spectrum prediction system is developed that takes advantage of the advanced Bi-ConvLSTM and the seq2seq framework. The Bi-ConvLSTM captures time–frequency characteristics of spectrum data, and the seq2seq framework is used for long-term spectrum prediction. Furthermore, the attention mechanism is used to address the limitations of the seq2seq framework that compresses all inputs into fixed-length vectors, resulting in information loss. Finally, the experimental results have shown that the proposed model has a significant advantage over the benchmark schemes. Especially, the proposed spectrum prediction model achieves 6.15%, 0.7749, 1.0978, and 0.9628 in MAPE, MAE, RMSE, and R 2 , respectively, which is better than all the baseline deep learning models. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
9. Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism.
- Author
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Zhang, Yulan, Hu, Jun, Jiang, Rundong, Lin, Zengrong, and Chen, Zengping
- Subjects
COMPUTER vision ,INTERNET of things ,SMART homes ,HOUSEHOLD appliances ,ZIGBEE ,RADIO frequency - Abstract
With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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10. Self-Supervised Multi-Frame Monocular Depth Estimation for Dynamic Scenes
- Author
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Wu, Guanghui, Liu, Hao, Wang, Longguang, Li, Kunhong, Guo, Yulan, and Chen, Zengping
- Abstract
Self-supervised multi-frame depth estimation outperforms single-frame approaches by utilizing not only appearance information, but also geometric information. A common practice for multi-frame methods is to employ feature-metric bundle adjustment (FBA) to refine depth map initialized from the single-frame prior. However, FBA cannot always provide effective residual updates due to unreliable matching costs, which are corrupted by thin texture, occlusion, and especially object motion. To tackle this problem, we propose a context-aware transformer (CAT) to refine the corrupted matching costs by leveraging the spatial context information. Specifically, the CAT adaptively aggregates matching costs according to the spatial affinity inferred from local appearance context, and produces reliable contextual costs for FBA. Moreover, we design a motion-aware regularization loss to provide supervision for regions with moving objects, making CAT competent for dynamic scenes. Extensive experiments and analyses on the KITTI and Cityscapes datasets demonstrate the effectiveness and superior generalization capability of our approach.
- Published
- 2024
- Full Text
- View/download PDF
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