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检索条件"任意字段=16th Conference on Signal and Data Processing of Small Targets"

共 487 条记录，以下是1-10 订阅

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Machine learning classifier for marine radar small targets based on normalization features 16

Machine learning classifier for marine radar small targets b...

引用

16th International conference on signal processing Systems, ICSPS 2024

作者： Zhao, Zi-Jian Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

targets on the sea surface are of great concern, especially the classification of small targets, which is crucial for marine safety. therefore, we propose a machine learning classifier for marine radar small targets based on normalization features, which can effectively classify targets with small radar cross section (RCS) and signal-to-clutter ratio (SCR) on the sea surface. We standardize the radar parameters because different radars have different system parameters, resulting in limited generalization ability of the trained classifier. We extract 9 features from multiple dimensions of the signal such as time domain, frequency domain and time-frequency domain and then classify targets using decision tree (DT) classifier, K-nearest neighbors (KNN) classifier, bootstrap aggregation (Bagging) classifier and adaptive boosting (AdaBoost) classifier. Experimental validation is performed on publicly available IPIX, self-measured radar data and data provided by the South African Council for Scientific and Industrial Research (CSIR), which contains four types of targets: floating ball, floating boats, low-altitude unmanned aerial vehicles (UAVs) and moving speedboats, and the results of the experiments show that the proposed classifier achieves a correct classification accuracy of 0.8537. © 2025 SPIE.

关键词： Marine radar

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Sixteenth International conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

引用

16th International conference on signal processing Systems, ICSPS 2024

ISBN: (纸本)9781510689251

the proceedings contain 197 papers. the topics discussed include: variational modal decomposition based on whale optimization algorithm for vital signs detection of FMCW radar;sea surface small target detection using tri-median normalized doppler spectrum feature;a physical cluster-based passive scatterer detection method in ISAC systems;on detection of moving weak targets using MIMO continuous active sonar and Doppler filtering;design of optical testing turntable data acquisition system based on LabVIEW;AOA /TDOA location technology of weak satellite signal based on satellite-ground cooperation;bearing-crossing localization technology for UAV target based on signal characteristics;and separation and localization of multiple bat signals using frequency domain beamforming and dual-array-localization method.

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Multiresolution compound-Gaussian models for sea clutter with independent identically distributed textures 16

Multiresolution compound-Gaussian models for sea clutter wit...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Chang, Si-Yuan Xu, Shu-Wen Shui, Peng-Lang National Key Laboratory of Radar Signal Processing Xidian University China

ISBN: (纸本)9781510689251

Range resolution conversion (RRC) is a promising technology for maritime radar detection of targets of different size, and sea clutter modeling and parameter estimation are important in maritime radar signal processing. In this paper, two kinds of multiresolution compound-Gaussian models are proposed to model the clutter after RRC, which are multiresolution K (MRK) distribution and multiresolution CGIG (MRCGIG) model. As another main contribution, across-resolution methods of moments (ARMoM) are proposed to estimate the parameters of MRK and MRCGIG distributions, which have higher estimation accuracy than that of methods of moments. Moreover, the fitting performance of proposed models are verified by the clutter after RRC which is generated by simulating clutter data before RRC, and the comparisons of estimation performance base on simulated data demonstrate the superiority of ARMoM. © 2025 SPIE.

关键词： Marine radar

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Feed-forward Neural Network based Algorithm for Intelligent Positioning of Direction Finding Crossings 16

Feed-forward Neural Network based Algorithm for Intelligent ...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Li, Pangzhe Hu, Dexiu Zhao, Chuang Information Engineering University Signal Processing Laboratory China

ISBN: (纸本)9781510689251

this paper proposes a feed-forward neural network(FNN) based algorithm for intelligent positioning of angle of arrival(AOA) to address the problems of complex computation, slow positioning speed and low positioning accuracy of traditional cross-location direction finding methods. the algorithm obtains the position of a known target from the available data, calculates its angle of arrival at the observatory, combines them into data pairs, and inputs them into a constructed neural network to obtain the corresponding model. the network can then locate unknown targets. It is verified that the algorithm has reliable localization accuracy and higher robustness than conventional algorithms. the proposed method also shows that the training of the neural network on the basis of a large amount of data can greatly reduce the operation time and achieve real-time target localization. © 2025 SPIE.

关键词： Neural network models

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A particle belief propagation algorithm based on track-before-detect for tracking low-observable and maneuvering targets using multiple sensor 16

A particle belief propagation algorithm based on track-befor...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Cao, Chenghu Huang, Haisheng Zhao, Yongbo Li, Xin Hui, Peiyuan Xi’an University of Posts and Communications Xi’an710121 China National Key Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Xi’an Innovation College of Yan’an University Xi’an710100 China

ISBN: (纸本)9781510689251

It is notoriously challenging work to track an unknown number of low-observable maneuvering targets. In this paper, a sequential Bayesian inference method based on multiple-model dynamic model and track-before-detect measurement (TBD) model is proposed for tracking low-observable maneuvering targets using multiple sensors. Our proposed method is based on a new multi-sensor statistical model that allows targets to interact and contribute to more than one data cell for pixeled image TBD approach. Based on the factor graph representing multi-sensor statistical model, the marginal posterior densities are derived by performing the message passing equations of the proposed belief propagation (BP) algorithm for target detection and target state estimation. the simulation results validate that the computational complexity of our proposed multi-sensor BP-TBD algorithm scales in the number of sensor nodes and demonstrate that its performance is superior among the state-of-the-art multi-sensor TBD methods. © 2025 SPIE.

关键词： Sensor nodes

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Asynchronous Fusion Method for Passive Radar data and Satellite data 16

Asynchronous Fusion Method for Passive Radar Data and Satell...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Zhang, Zhuowei 724 Research Institute of China Shipbuilding Corporation Limited Nanjing China

ISBN: (纸本)9781510689251

It is difficult to balance data continuity and direction finding accuracy when detecting targets with a single detection method. An asynchronous fusion method of passive radar data and satellite data is proposed in this paper. Firstly, the azimuth angle of the target relative to the passive radar is calculated through the target parameters detected by the passive radar and the satellite. Next, the fusion cycle T between passive radar and satellite is calculated using the proposed fusion period calculation method. Finally, the proposed asynchronous fusion algorithm is used to fuse azimuth measurement values sequentially obtained in the fusion cycle, and the direction finding results at the fusion time are obtained. the simulation results show that the direction finding results after fusion are better than those before fusion. the asynchronous fusion method proposed in this paper can effectively improve the direction finding accuracy of the detection system. © 2025 SPIE.

关键词： data fusion

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Bayesian Detection for Maritime Radar targets in CG-LN Sea Clutter without Training data 16

Bayesian Detection for Maritime Radar Targets in CG-LN Sea C...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Xue, Jian Feng, Zhuo Li, HongEn Pan, MeiYan School of Communications and Information Engineering Xi’an University of Posts and Telecommunications China Xi'an Electronic Engineering Research Institute China

ISBN: (纸本)9781510689251

this paper addresses the problem of Bayesian adaptive detection for maritime radar targets in compound Gaussian sea clutter with random texture and speckle covariance matrix. To adaptively match sea clutter characteristics well, the prior distributions of sea clutter components are employed. Specially, we model the sea clutter texture component as a lognormal random variable, and the sea clutter speckle covariance matrix as an inverse complex Wishart random matrix. A Bayesian radar target detector is designed based on the criteria of the generalized likelihood ratio test. the proposed detector incorporates the prior knowledge of sea clutter, and, importantly, does not rely on training data. Experimental results on simulated and measured radar data show that the proposed detector outperforms its competitors when the number of training data is limited. © 2025 SPIE.

关键词： Inverse problems

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Direct Position Determination based on Sparse signal Representation via Overcomplete Dictionary Design 16

Direct Position Determination based on Sparse Signal Represe...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Liu, Lutao Fan, Xueye Guo, Muran College of Information and Communication Engineering Harbin Engineering University Heilongjiang Harbin150001 China

ISBN: (纸本)9781510689251

In this paper, a sparse signal representation direct position determination algorithm (SSR-DPD) based on overcomplete dictionary matrix design is investigated. In spite of the fact that the existing SSR-DPD algorithm is able to overcome the deterioration in positioning performance of the super resolution direct position determination (SR-DPD) algorithm under conditions of low signal-to-noise ratio (SNR) and small snapshot number, its precision depends heavily on the accuracy of the overcomplete dictionary and the fineness of the grid division. Consequently, we propose an improved SSR-DPD algorithm, which uses the total least squares (TLS) method to design the overcomplete dictionary matrix and enhances positioning accuracy via multilevel grid (MG) division. Specifically, the method involves obtaining the source prior position using subspace data fusion (SDF), followed by denser grid division in the surrounding area, and finally, using the orthogonal matching pursuit method (OMP) to recover the sparse position vector. Simulation results demonstrate superior performance of the proposed algorithm. © 2025 SPIE.

关键词： data fusion

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Sea Surface small Target Detection using Tri-median Normalized Doppler Spectrum Feature 16

Sea Surface Small Target Detection using Tri-median Normaliz...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Guo, Zi-Xun Wu, Kang-Hui Bai, Xiao-Hui Su, Jia Fan, Yi-Fei Gong, Yan-Yun Wang, Ling Shui, Peng-Lang School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China School of Information Xi’an University of Finance and Economics Shaanxi Xi’an710100 China The National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China

ISBN: (纸本)9781510689251

this letter aims to develop a detector for detecting small targets on the sea surface based on feature utilization median normalization. At the long observation time, sea clutter and returns with target show differences of Doppler amplitude spectra (DAS). the median normalized DAS (MNDAS) is used to enhance the Doppler characteristics of returns with target, which is computed from the DAS of the cell under test and its neighboring reference cells. From the MNDAS, three salient features can be extracted for the detection, including the non-zero interval length, vector entropy and global accumulation in the MNDAS. the three features are combined into 3D feature vectors. Considering that the returns with target and their feature vectors cannot be used in the training, one semi-supervised classifier based on the convexhull learning algorithm is built by the feature vectors of sea clutter only. Finally, a new detector based on feature utilization median normalization which can control the false alarm rate precisely is constructed. through the experiments on the IPIX and CSIR radar databases and one offshore experimental data using an unmanned aerial vehicle with a corner reflector, the proposed detector attains competitive performance and lower computation cost with the TF-tri-feature-based detector. © 2025 SPIE.

关键词： Vectors

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On Detection of Moving Weak targets Using MIMO Continuous Active Sonar and Doppler Filtering 16

On Detection of Moving Weak Targets Using MIMO Continuous Ac...

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16th International conference on signal processing Systems, ICSPS 2024

作者： Wang, Jie Liu, Xionghou Yang, Yixin Sun, Chao School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Xi’an710072 China Shaanxi Key Laboratory of Underwater Information Technology Shaanxi Xi’an710072 China Hanjiang National Laboratory Hubei Wuhan430061 China

ISBN: (纸本)9781510689251

In this paper, we propose a moving weak target detection method of utilizing multiple-input multiple-output continuous active sonar (MIMO-CAS) and Doppler filtering. We begin by designing frequency diversity continuous wave (FDCW) pulse signals that have very low cross-correlation coefficients. the number of FDCW pulses is equal to the product of the number of transmitting transducers and the number of detection cycles. Secondly, we transmit these FDCW pulses using MIMO-CAS over multiple cycles with a small periodic interval, projecting a large amount of sound energy into the ocean channel. the energy transmitted by MIMO-CAS is sufficient for detecting weak targets using the energy accumulation method. Next, we process the echoes from the moving target with different Doppler shifts using Doppler filters. For each FDCW pulse, we design a bank of Doppler filters based on the waveform and discrete Doppler shifts of the FDCW pulse. Finally, the Doppler-filtered outputs are accumulated coherently or incoherently in the range Doppler domain. And the high-gain accumulated output is outputted. MIMO-CAS makes full use of the Doppler filtering abilities of the FDCW pulses to suppress the reverberation near-zero Doppler shifts, and simultaneously utilizes the multi-cycle accumulation to suppress noise. Via numerical simulations, we show that MIMO-CAS can improve the detection performance of the weak targets. © 2025 SPIE.

关键词： Sonar

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