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Ground Moving targets Detection and Relocation in Heterogeneous Environment for Airborne Radar Based on Tri-channel Real data

Ground Moving Targets Detection and Relocation in Heterogene...

引用

2011 CIE International conference on Radar(RADAR 2011)

作者： S.Q.Zhu,G.S.Liao,H.H.Tao National Lab of Radar signal processing,Xidian University,China

This paper describes an approach for ground moving targets detection and relocation in heterogeneous environment which requires three consecutive operations as follows: Firstly, some deleterious factors correction techniques are adopted before clutter rejection. Secondly, different window selection strategies, aiming at different image coregistration error, are given to form the joint data vector for clutter rejection. Finally, the true target steering vector is derived in the presence of image coregistration error and channel mismatch, based on which the radial velocities of moving targets can be determined. Results of measured data processing are included to verify the robustness of this algorithm.

关键词： Ground Moving targets Indication (GMTI), Space-Time Adaptive processing (STAP), Synthetic Aperture Radar (SAR), Airborne radar.

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A Smart wearable motion sensor and acoustic signal processing based on vocabulary for monitoring children's wellbeing using Big data

A Smart wearable motion sensor and acoustic signal processin...

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2022 International conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Nuthakki, Praveena Manju, J. Geetha, R. Suguna, M. Sheik Abdullah, A.S. Vrsiddhartha Engineering College Vijayawada India Rangarajan Dr Sakunthala Engineering College Veltech Multi Tech Dr Chennai India SIMATS Institute of Information Technology Department of Knowledge Engineering Chennai India India

ISBN: (纸本)9781665495158

The brain is the most complex organ in the human body, and it is also the most complex organ in the whole biological system, making it the most complex organ on the planet. According to the findings of current studies, modern study that properly characterises the EEG data signal provides a clear classification accuracy of human activities that is distinct from previous research. Various brain wave patterns related with common activities such as sleeping, reading, and watching a movie may be found in the Electroencephalography (EEG) data that has been collected. In response to these activities, we accumulate various sorts of emotion signals in our brain, such as the Delta, Theta, and Alpha bands, which will provide different types of emotion signals in our brain as a consequence of our actions. When dealing with EEG recordings that are non-stationary in nature, time-frequency domain techniques, on the other hand, are more likely to provide good results. The ability to detect diverse neural rhythm scales using time-frequency representation has also been shown to be a legitimate EEG marker;this ability has also been demonstrated to be a powerful tool for investigating small-scale neural brain oscillations. On the basis of several parameters such as filtering response, precision, recall, and F-measure, as well as accuracy and precision, the Matlab simulation software was used to evaluate the performance of the proposed system. © 2022 IEEE.

关键词： Brain

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Magnetic signal processing Methods with Application to Geophysical Navigation of Marine Robotic Vehicles

Magnetic Signal Processing Methods with Application to Geoph...

引用

MTS/IEEE Oceans conference

作者： Quintas, Joao Teixeira, Francisco Curado Pascoal, Antonio Univ Lisbon Inst Syst & Robot Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal

ISBN: (纸本)9781509015375

The present paper describes the development of geophysical navigation (GN) methods for small, affordable underwater robotic vehicles. The proposed GN methods includes a classical, bathymetric-based terrain-aided navigation (TAN) approach, a magnetic-based geophysical navigation solution (MAGNAV), and an integration of both methods (TAN-MAGNAV). Due to insufficient topographic features in the adopted test site, the classical TAN approach performed poorly in terms of positioning accuracy. To mitigate this, the complementarity of magnetic and topographic terrain information was exploited, initially using only magnetic data to estimate the vehicle position and later by fusing magnetic and altitude data. The results obtained illustrate the high potential of using magnetic data for geophysical navigation of autonomous underwater vehicles. The navigation methods described are validated in simulated trials using real magnetic, topographic, and navigation data acquired with an autonomous marine vehicle in real trials. The equipment employed in the proposed solution consists of standard navigation sensors, a sonar altimeter, and an affordable total field magnetometer.

关键词： Geophysical Navigation magnetic navigation particle filter potential field continuation

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Multisensor data classification with dependence trees

Multisensor data classification with dependence trees

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conference on Image and signal processing for Remote Sensing VI

作者： Piardi, A Melgani, F Serpico, SB Datcu, M Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

ISBN: (纸本)081943826X

In order to apply the statistical approach to the classification of multisensor remote sensing data, one of the main problems lies in the estimation of the joint probability density functions (pdfs) f(X\omega (k)) of the data vector X given each class omega (k), due to the difficulty of defining a common statistical model for such heterogeneous data. A possible solution is to adopt non-parametric approaches which rely on the availability of training samples without any assumption about the statistical distributions involved. However, as the multisensor aspect involves generally numerous channels, small training sets make difficult a direct implementation of non-parametric pdf estimation. In this paper, the suitability of the concept of dependence tree for the integration of multisensor information through pdf estimation is investigated. First, this concept, introduced by Chow and Liu, is used to provide an approximation of a pdf defined in an N-dimensional space by a product of N-1 pdfs defined in two-dimensional spaces, representing in terms of graph theoretical interpretation a tree of dependencies. For each land cover class, a dependence tree is generated by minimizing an appropriate closeness measure. Then, a non-parametric estimation of the second order pdfs f(x(j)\x(j),omega (k)) is carried out through the Parzen approach, based on the implementation of two-dimensional Gaussian kernels. In this way, it is possible to reduce the complexity of the estimation, while capturing a significant part of the interdependence among variables. A comparative study with two other non-parametric multisensor data fusion methods, namely: the Multilayer Perceptron (MLP) and K-nearest neighbors (K-nn) methods, is reported. Experimental results carried out on a multisensor (ATM and SAR) data set show the interesting performances of the fusion method based on dependence trees with the advantage of a reduced computational cost with respect to the two other methods.

关键词： multisensor fusion dependence trees non-parametric pdf estimation

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Electronic Radar signal Recognition Based on Wavelet Transform and Convolutional Neural Network 2

Electronic Radar Signal Recognition Based on Wavelet Transfo...

引用

2nd Asia-Pacific conference on Communications Technology and Computer Science, ACCTCS 2022

作者： Li, Meilian School of Electronic and Electrical Engineering Anhui Sanlian Univeristy Anhui Hefei230601 China

ISBN: (纸本)9781665400343

In modern warfare, radar signals are becoming more and more complex. How to quickly and accurately obtain the category information of target tracks from various radar detection data with huge amount of data, and provide accurate and effective information for battlefield command is an urgent problem to be solved at present. With the development of wavelet theory, the application of wavelet transform is more and more extensive. In the training stage, the image is decomposed by stationary wavelet with scale 2 using the proposed algorithm, and then the high and low frequency components are respectively input into five designed residual networks for training. In the testing stage, inverse wavelet transform is used to obtain the final predicted image. Firstly, nine kinds of LPI radar signals are subjected to Choi-Williams distribution (CWD) time-frequency transformation to obtain two-dimensional time-frequency images. Then, the time-frequency images are denoised by denoised convolutional neural network. Finally, the images are sent to Incident-v4 network for feature extraction and classified by softmax classifier, so as to realize effective classification and identification of LPI radar signals. Convolution using a large number of analog datasets based on 4 typical LPI radar signals (Linear Frequency Modulation (LFM), Nonlinear Frequency Modulation (NLFM), Biphase Coded signal (BPSK), COSTAS Frequency Coded signal) and white noise signals The neural network model is trained, and a small amount of measured signals (LFM, BPSK) are added as the verification set for adaptation, so as to better fit the detection model of the measured signals. Combining the classic and recent main techniques of radar signal processing, this paper discusses the value and potential of wavelet transform as a new and effective time-frequency analysis tool applied to wavelet transform and convolutional neural network in electronic radar signal recognition. © 2022 IEEE.

关键词： Wavelet transforms

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An initial data association method by using doppler frequency for multi target

An initial data association method by using doppler frequenc...

引用

IEEE signal processing and Communications Applications (SIU)

作者： Yılmaz Kalkan Buyurman Baykal Elektrik ve Elektronik Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi Ankara Turkey Elektronik ve Haberleşme Mühendisliği Bölümü Süleyman Demirel Üniversitesi Isparta Turkey

The association of the received signals with exact targets is the most basic and the vital problem in the multitarget applications. In the literature, this problem is known as data association. In the multitarget case, after the association of the received signal with the correct targets, these associated signals can be used in many applications such as target localization and target tracking. The errors in data association is caused to increase the error in the following steps which use the wrong associated signals. In this paper, a new initial data association method is proposed for the frequency-only systems in multitarget case. In general, the time, frequency and phase informations of the received signal are used for data association. If the time resolution of the used signals is not good or enough (such as in unmodulated continuous wave (CW) signals), in this case instead of time information, using the frequency information is better.

关键词： Doppler effect Target tracking Radar tracking Doppler radar Time frequency analysis MIMO radar

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Application of A Question Recognition Strategy for small data Sets in Medical Insurance QA Systems 24

Application of A Question Recognition Strategy for Small Dat...

引用

3rd International conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML)

作者： Zhang, Shun Fu, Weiwei Hu, Yurong Chen, Huafeng Jingchu Univ Technol Internet Intelligences Applicat Innovat Res Ctr Jingmen 448000 Peoples R China Jingmen Duodao Dist Med Secur Bur Jingmen Peoples R China Jingmen Cryptometry Applicat Technol Res Ctr Jingmen 448000 Peoples R China Jingchu Univ Technol Sch Comp Engn Jingmen 448000 Peoples R China

ISBN: (纸本)9798400709777

Question recognition is crucial in Q&A systems. An effective recognition method significantly enhances the accuracy and efficiency of information retrieval. Advances in natural language processing (NLP) technology have spurred the development of statement recognition techniques. However, many methods rely on extensive datasets, and numerous sectors struggle to keep pace with intelligent advancements due to data scarcity. Policy-related questions, which often involve a plethora of technical terms, exemplify this challenge. For colloquial questions with intricate structures, models require enhanced natural language understanding capabilities. These models must prioritize key information and filter out irrelevant data, particularly when data availability is limited. To address these issues, this study utilized medical insurance approval items and related questions as data sources. It employed sentence vectors and question structures as analytical frameworks and explored innovative solutions. Specifically, the twin network fine-tuning pre-training model RoBERTa was applied to classify medical insurance items, integrating syntactic structure and question word features to pinpoint question targets. This method outperformed standard text classification models in the medical insurance domain and has potential applicability in other policy-oriented Q&A systems, thereby offering precise services to the public.

关键词： Pre-trained model Multiple text classification Sentence structure Question recognition

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The length estimation of ship targets in ISAR images

The length estimation of ship targets in ISAR images

引用

International conference on signal processing Proceedings (ICSP)

作者： Hongxin Yang Fulin Su Jianjun Gao School of Electronic and Information Technology Harbin Institute of Technology Harbin Heilongjiang CN

Inverse Synthetic Aperture Radar produces images which can be trained to recognize by human operators. Because of varying angular motions (roll, pitch, and yaw), the cross-range resolution is produced. So the Automatic Target Recognition (ATR) of ship can be implemented. The centre-line of ship is an important feature. This paper addresses the problem of centre-line detection and length estimation for ship targets. The aim is to calculate the length of ship for recognition. In order to obtain more accurate results, a new analytical model is proposed, the position of mainmast and the differential Doppler concerning the centre-line is used in the estimation the length of ship. The performance of the proposed algorithm is shown by real data and simulated data.

关键词： Image resolution Marine vehicles Doppler effect Analytical models Visualization Radar imaging

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A New Method of Radar Target recognition Based On Neural Network

A New Method of Radar Target recognition Based On Neural Net...

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1996 3rd International conference on signal processing(ICSP’96)

作者： Zhu yong Souyong Wang Jiangmin Qin Radar Academy

This paper provids a new method for recongizing quantity of aircraft in radar targets based on neural network for the first *** method turns many duplicate cycle intermediate frequency narrow bandwidth signal received by radar which once scans targets into complex envelope signal,caculates its autocorrelation matrix,and autocorrelation matrix characteristic value,make these as primary charactor of radar target recognition In order to decrease coherency between characteristic *** divergence,reduce characteristic space dimensions,compress its primary characteristic data through APEX(Adaptive Principal Component Extraction) algorithm,use BP(Back Propagation ) algorithm reeongnize the compressed data as the quadratic *** trial shows the aircraft number information extract from intermediate frequency narrow bandwidth signal using this method,influenced little by targets space position or posture and it has stronger curb noise *** compressing primary characteristic data applying APEX algorithm,make targets characteristic stable and efficient,and has a relatively well recognition result in applying BP algorithm to target recognition.

关键词： Target Recognition Neural Network signal Process

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Intelligent data processing of Marine Target Tracking Process Based on Fuzzy clustering

Intelligent Data Processing of Marine Target Tracking Proces...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Zheng Zhang Yanwei Du Beijing Institute of Control & Electronic Technology Beijing China

ISBN: (纸本)9781665404136;9781665447300

Aiming at the inaccurate tracking and the difficulty of the track initialization during multi-target tracking in marine rescue scenes, this article proposes an intelligent processing method for marine tracking data based on fuzzy clustering. The measurement information received by the sensor of the measuring device is subjected to the fuzzy clustering process in a period of time, then the entropy from the subjection matrix is used to judge the change in the number of targets. In this way, the number of the target quantity can be determined and the target can be tracked more accurate. This article also analyzes and improves the method through simulation experiments to solve to problems such as inaccurate clustering in real situations. And the method can meet the needs of tracking velocity and tracking accuracy.

关键词： Analytical models Target tracking Computational modeling signal processing data processing Time measurement Entropy

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