Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168260
Anton Popov, S. Yakovenko
Any movement is generated by synergistic actions of muscle groups. The analytical description of how muscles are combined into synergies to produce their coordination actions remains to be established. Here, we have introduced the wavelet-based analysis to identify common information among major leg muscles during a locomotor tasks with and without asymmetric adaptation.
{"title":"Muscle synergy decomposition analysis using wavelet detection in human locomotor activity","authors":"Anton Popov, S. Yakovenko","doi":"10.1109/SPS.2015.7168260","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168260","url":null,"abstract":"Any movement is generated by synergistic actions of muscle groups. The analytical description of how muscles are combined into synergies to produce their coordination actions remains to be established. Here, we have introduced the wavelet-based analysis to identify common information among major leg muscles during a locomotor tasks with and without asymmetric adaptation.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122353906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168296
I. Prokopenko, N. Babanska
An article is dedicated to creation of locally-optimal robust algorithm of non-Gaussian signal detection on the base of sampling statistical analysis and to investigation of the algorithm effectiveness.
提出了一种基于抽样统计分析的非高斯信号局部最优鲁棒检测算法，并对算法的有效性进行了研究。
{"title":"Detection algorithm of non-Gaussian character of sample1 distribution and its implementation in radar data processing","authors":"I. Prokopenko, N. Babanska","doi":"10.1109/SPS.2015.7168296","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168296","url":null,"abstract":"An article is dedicated to creation of locally-optimal robust algorithm of non-Gaussian signal detection on the base of sampling statistical analysis and to investigation of the algorithm effectiveness.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128025943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168256
Theresa Haumtratz, T. Bieker, S. Lindenmeier
Automatic Target Recognition (ATR) in a monostatic radar configuration is a well-known problem but ATR in a bistatic radar configuration presents some additional challenges. In this paper we discuss the degradation of the range resolution with increasing bistatic angle and the resulting apparent shortening of the radar target and present a possibility to compensate for this shortening by stretching the range profiles of the target. Furthermore we show the potential of that method to make the classification of bistatic data feasible with a classifier, which has been trained using monostatic data only.
{"title":"Compensation of the range resolution degradation in a bistatic scenario and its influence on classification","authors":"Theresa Haumtratz, T. Bieker, S. Lindenmeier","doi":"10.1109/SPS.2015.7168256","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168256","url":null,"abstract":"Automatic Target Recognition (ATR) in a monostatic radar configuration is a well-known problem but ATR in a bistatic radar configuration presents some additional challenges. In this paper we discuss the degradation of the range resolution with increasing bistatic angle and the resulting apparent shortening of the radar target and present a possibility to compensate for this shortening by stretching the range profiles of the target. Furthermore we show the potential of that method to make the classification of bistatic data feasible with a classifier, which has been trained using monostatic data only.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125647432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168259
E. Swiercz
The backscatter from radar provides Doppler information of the scatterer giving the frequency characteristics over time, or in the case of rotating objects, over varying aspect angle. These frequency domain projections are used for Doppler radar tomographic imaging. This paper presents a method for imaging of a rotating target using a time-frequency transformation as a tomographic projection. The resolution of a tomographic image depends on the resolution of input projections. The reassigned spectrogram is proposed as tomographic projections, which allow to obtain a clearer image of a rotating object than conventional Fourier-based image formation. The reassigned spectrogram is very sensitive to noise so the denoising procedure on the time-frequency plane must be performed before the reassigned spectrogram transform. The proposed idea of imaging is illustrated on numerical experiments.
{"title":"Doppler Radar tomography of rotated object in noisy environment based on time-frequency transformation","authors":"E. Swiercz","doi":"10.1109/SPS.2015.7168259","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168259","url":null,"abstract":"The backscatter from radar provides Doppler information of the scatterer giving the frequency characteristics over time, or in the case of rotating objects, over varying aspect angle. These frequency domain projections are used for Doppler radar tomographic imaging. This paper presents a method for imaging of a rotating target using a time-frequency transformation as a tomographic projection. The resolution of a tomographic image depends on the resolution of input projections. The reassigned spectrogram is proposed as tomographic projections, which allow to obtain a clearer image of a rotating object than conventional Fourier-based image formation. The reassigned spectrogram is very sensitive to noise so the denoising procedure on the time-frequency plane must be performed before the reassigned spectrogram transform. The proposed idea of imaging is illustrated on numerical experiments.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115052153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168290
J. Kulpa
In Continuous Wave (CW) Radars, especially ones transmitting noise signals, the masking effect may cause weak echoes to be completely masked in the sidelobes of strong echo targets. In radars where the transmit and receive antennas are close to each other, the strongest signals are direct crosstalk and close reflections. In this paper a novel mismatched filter is considered for suppressing the range sidelobes of the correlation function. Both simulations and measurement results are presented.
{"title":"Mismatched filter for range sidelobes suppression of pseudo-noise signals","authors":"J. Kulpa","doi":"10.1109/SPS.2015.7168290","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168290","url":null,"abstract":"In Continuous Wave (CW) Radars, especially ones transmitting noise signals, the masking effect may cause weak echoes to be completely masked in the sidelobes of strong echo targets. In radars where the transmit and receive antennas are close to each other, the strongest signals are direct crosstalk and close reflections. In this paper a novel mismatched filter is considered for suppressing the range sidelobes of the correlation function. Both simulations and measurement results are presented.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116967452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168271
I. Gorovyi, O. Bezvesilniy, D. Vavriv
Formation of high-resolution SAR images from light-weight platforms is a challenging task primarily due to high instability of such platforms. Additional difficulties are related with the precision of navigation systems. In the paper the problem of residual trajectory deviations are analyzed. An efficient trajectory reconstruction method is proposed. Important practical aspects of the developed approach are discussed. Considered ideas are incorporated into the SAR processing chain. The algorithm efficiency is proven via examples of real SAR data obtained with an X-band airborne SAR system.
{"title":"Efficient data focusing and trajectory reconstruction in airborne SAR systems","authors":"I. Gorovyi, O. Bezvesilniy, D. Vavriv","doi":"10.1109/SPS.2015.7168271","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168271","url":null,"abstract":"Formation of high-resolution SAR images from light-weight platforms is a challenging task primarily due to high instability of such platforms. Additional difficulties are related with the precision of navigation systems. In the paper the problem of residual trajectory deviations are analyzed. An efficient trajectory reconstruction method is proposed. Important practical aspects of the developed approach are discussed. Considered ideas are incorporated into the SAR processing chain. The algorithm efficiency is proven via examples of real SAR data obtained with an X-band airborne SAR system.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128327051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168288
Igor Krashenyi, A. Popov, J. Ramírez, J. Górriz
Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard deviations in intensities from most descriptive brain regions. k-fold cross-validation was used to estimate FIS performance, resulting in accuracy, sensitivity, specificity and positive predictive value (ppv) characteristics of FIS classification between different groups. ppv was equal to 0.8778±0.0088 (AD vs. NC), 0.7289±0.0243 (NC vs. MCI), and 0.8531±0.0069 (MCI vs. AD).
采用减法聚类算法开发了模糊推理系统(FIS)，并将其应用于轻度认知障碍(MCI)或阿尔茨海默病(AD)患者的MRI图像与正常对照(NC)之间的分类。用作FIS输入的特征是来自大多数描述性大脑区域的强度的平均值和标准差。采用k-fold交叉验证对FIS进行性能评价，得出不同组间FIS分类的准确性、敏感性、特异性和阳性预测值(ppv)特征。ppv = 0.8778±0.0088 (AD vs. NC)， 0.7289±0.0243 (NC vs. MCI)， 0.8531±0.0069 (MCI vs. AD)。
{"title":"Application of fuzzy logic for Alzheimer's disease diagnosis","authors":"Igor Krashenyi, A. Popov, J. Ramírez, J. Górriz","doi":"10.1109/SPS.2015.7168288","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168288","url":null,"abstract":"Fuzzy Inference System (FIS) is developed using subtractive clustering algorithm, and applied to classification between MRI images of patients having Mild Cognitive Impairment (MCI) or Alzheimer's Disease (AD) and Normal Controls (NC). Features used as FIS inputs are mean values and standard deviations in intensities from most descriptive brain regions. k-fold cross-validation was used to estimate FIS performance, resulting in accuracy, sensitivity, specificity and positive predictive value (ppv) characteristics of FIS classification between different groups. ppv was equal to 0.8778±0.0088 (AD vs. NC), 0.7289±0.0243 (NC vs. MCI), and 0.8531±0.0069 (MCI vs. AD).","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"325 Pt A 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116261196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168278
J. Garry, G. E. Smith, C. Baker
Passive radar systems must detect the presence of a target response many orders of magnitude weaker than the direct signal interference. Even when digitally modulated waveforms with favorable ambiguity surfaces are employed, the floor of the ambiguity surface sets significant limitations on the minimum detectable signal. Suppression of this direct path and close-in clutter from the surveillance waveform is crucial for maximizing the dynamic range which increases the useful detection range of the system. Presented here is an evaluation of various direct signal suppression schemes - both block and adaptive filtering - tested against various metrics on experimental collected passive radar data using North American digital television (DTV) waveforms. Results show the fast block least-mean squares adaptive filter to be significantly faster than existing algorithms with superior suppression performance. Strategies for selecting filtering schemes depending on the task at hand are also discussed.
{"title":"Direct signal suppression schemes for passive radar","authors":"J. Garry, G. E. Smith, C. Baker","doi":"10.1109/SPS.2015.7168278","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168278","url":null,"abstract":"Passive radar systems must detect the presence of a target response many orders of magnitude weaker than the direct signal interference. Even when digitally modulated waveforms with favorable ambiguity surfaces are employed, the floor of the ambiguity surface sets significant limitations on the minimum detectable signal. Suppression of this direct path and close-in clutter from the surveillance waveform is crucial for maximizing the dynamic range which increases the useful detection range of the system. Presented here is an evaluation of various direct signal suppression schemes - both block and adaptive filtering - tested against various metrics on experimental collected passive radar data using North American digital television (DTV) waveforms. Results show the fast block least-mean squares adaptive filter to be significantly faster than existing algorithms with superior suppression performance. Strategies for selecting filtering schemes depending on the task at hand are also discussed.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115287865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168292
K. Konopko, Y. Grishin, D. Janczak
A radar signal recognition can be accomplished by exploiting the particular features of a radar signal observed in presence of noise. The features are the result of slight radar component variations and acts as an individual signature. The paper describes radar signal recognition algorithm based on time frequency analysis, noise reduction and statistical classification procedures. The proposed method is based on the Wigner-Ville Distribution with using a two-dimensional denoising filter which is followed by a probability density function estimator which extracts the features vector. Finally the statistical classifier is used for the radar signal recognition. The numerical simulation results for the P4-coded signals are presented.
{"title":"Radar signal recognition based on time-frequency representations and multidimensional probability density function estimator","authors":"K. Konopko, Y. Grishin, D. Janczak","doi":"10.1109/SPS.2015.7168292","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168292","url":null,"abstract":"A radar signal recognition can be accomplished by exploiting the particular features of a radar signal observed in presence of noise. The features are the result of slight radar component variations and acts as an individual signature. The paper describes radar signal recognition algorithm based on time frequency analysis, noise reduction and statistical classification procedures. The proposed method is based on the Wigner-Ville Distribution with using a two-dimensional denoising filter which is followed by a probability density function estimator which extracts the features vector. Finally the statistical classifier is used for the radar signal recognition. The numerical simulation results for the P4-coded signals are presented.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116712597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-06-10DOI: 10.1109/SPS.2015.7168277
Karolina Szczepankiewicz, M. Malanowski, Michal Szczepankiewicz
The paper presents an effective implementation of passive radar algorithms which uses NVIDIA CUDA technology for parallel processing of DVB-T and FM signals. Algorithms were tested in the Passive Coherent Location system (passive radar) which utilizes commercial FM radio and DVB-T television transmitters as “illuminators of opportunity” to detect and track airborne targets. Radar taken into consideration was developed at the Warsaw University of Technology. High computing power is strongly needed in order to provide real-time radar data processing. The aim of this paper is to present a solution that provides sufficient performance to achieve real-time calculations.
{"title":"Effective implementation of passive radar algorithms using general-purpose computing on graphics processing units","authors":"Karolina Szczepankiewicz, M. Malanowski, Michal Szczepankiewicz","doi":"10.1109/SPS.2015.7168277","DOIUrl":"https://doi.org/10.1109/SPS.2015.7168277","url":null,"abstract":"The paper presents an effective implementation of passive radar algorithms which uses NVIDIA CUDA technology for parallel processing of DVB-T and FM signals. Algorithms were tested in the Passive Coherent Location system (passive radar) which utilizes commercial FM radio and DVB-T television transmitters as “illuminators of opportunity” to detect and track airborne targets. Radar taken into consideration was developed at the Warsaw University of Technology. High computing power is strongly needed in order to provide real-time radar data processing. The aim of this paper is to present a solution that provides sufficient performance to achieve real-time calculations.","PeriodicalId":193902,"journal":{"name":"2015 Signal Processing Symposium (SPSympo)","volume":"209 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131505025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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