Pub Date : 2014-11-03DOI: 10.1109/SSPD.2014.6943332
Y. Pailhas, Y. Pétillot
Multiple Input Multiple Output sonar systems offer new perspectives for target detection and underwater surveillance. In this paper we present an unified formulation for sonar MIMO systems and study their properties in terms of target recognition and imaging. Here we are interested in large MIMO systems. The multiplication of the number of transmitters and receivers non only provides a greater variety in term of target view angles but provides also in a single shot meaningful statistics on the target itself. We demonstrate that using large MIMO sonar systems and with a single shot it is possible to perform automatic target recognition and also to achieve super-resolution imaging. Assuming the view independence between the MIMO pairs the speckle can be solved and individual scatterers within one resolution cell decorelate. A realistic 3D MIMO sonar simulator is also presented. The output of this simulator will demonstrate the theoretical results.
{"title":"Large MIMO sonar systems: A tool for underwater surveillance","authors":"Y. Pailhas, Y. Pétillot","doi":"10.1109/SSPD.2014.6943332","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943332","url":null,"abstract":"Multiple Input Multiple Output sonar systems offer new perspectives for target detection and underwater surveillance. In this paper we present an unified formulation for sonar MIMO systems and study their properties in terms of target recognition and imaging. Here we are interested in large MIMO systems. The multiplication of the number of transmitters and receivers non only provides a greater variety in term of target view angles but provides also in a single shot meaningful statistics on the target itself. We demonstrate that using large MIMO sonar systems and with a single shot it is possible to perform automatic target recognition and also to achieve super-resolution imaging. Assuming the view independence between the MIMO pairs the speckle can be solved and individual scatterers within one resolution cell decorelate. A realistic 3D MIMO sonar simulator is also presented. The output of this simulator will demonstrate the theoretical results.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116834233","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943311
R. Santucci, M. Banavar, Sai Zhang, A. Spanias, C. Tepedelenlioğlu
A preliminary investigation has been conducted into the use of orthogonal frequency-division multiple-access for distributed estimation. The key difference from previous work in the literature is that the channels between the sensors and the fusion center contain multiple paths with time lags, amplitudes, and phase rotations due to fading. Orthogonal frequency-division multiplexing has been proven to be an effective modulation scheme in the presence of multipath channels, and thus has been utilized in these experiments. This estimation system was designed to operate in a heavily scattered environment where synchronizing the transmitters and developing channel statistics has proven difficult to achieve. Sensors measure a signal in noise, modulate the measured data over an OFDM subcarrier, and transmit this to a fusion center over a Gaussian multiple-access channel. The transmissions are received at the fusion center using an OFDM receiver, and the estimation process is completed. Simulations demonstrate the effectiveness of the technique.
{"title":"OFDM-based distributed estimation for rich scattering environments","authors":"R. Santucci, M. Banavar, Sai Zhang, A. Spanias, C. Tepedelenlioğlu","doi":"10.1109/SSPD.2014.6943311","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943311","url":null,"abstract":"A preliminary investigation has been conducted into the use of orthogonal frequency-division multiple-access for distributed estimation. The key difference from previous work in the literature is that the channels between the sensors and the fusion center contain multiple paths with time lags, amplitudes, and phase rotations due to fading. Orthogonal frequency-division multiplexing has been proven to be an effective modulation scheme in the presence of multipath channels, and thus has been utilized in these experiments. This estimation system was designed to operate in a heavily scattered environment where synchronizing the transmitters and developing channel statistics has proven difficult to achieve. Sensors measure a signal in noise, modulate the measured data over an OFDM subcarrier, and transmit this to a fusion center over a Gaussian multiple-access channel. The transmissions are received at the fusion center using an OFDM receiver, and the estimation process is completed. Simulations demonstrate the effectiveness of the technique.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114800059","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943306
Di Wu, Mehrdad Yaghoobi, Shaun Kelly, M. Davies, R. Clewes
Raman spectroscopy has for a long time performed as a common analytical technique in spectroscopic applications. A Raman spectrum depends upon how efficiently a molecule scatters the incident light (electron rich molecules often produce strong signals) which results in difficulties for relating the spectrum to the absolute amounts of present substances. The spectrum is however a stable and accurate representation of the sample measured especially considering that each molecule is associated with a unique spectrum. State-of-the-art spectroscopic calibration methods include the principal component regression (PCR) and partial least squares regression (PLSR) methods which have been proved to be efficient regression methods to realise the quantitative analysis of Raman spectrum. In this paper we consider the problem of Raman spectra deconvolution to analyse the sample composition, as well as possible unknown substances. In particular, we propose a sparse regularized model as a complement to traditional regression methods by leveraging the components sparsity compared to the whole chemical library and the spectra sparsity, given that the chemical fingerprint of each spectrum is mainly determined by the peaks. Experimental results illustrate the effectiveness of this sparse regularized model.
{"title":"A sparse regularized model for Raman spectral analysis","authors":"Di Wu, Mehrdad Yaghoobi, Shaun Kelly, M. Davies, R. Clewes","doi":"10.1109/SSPD.2014.6943306","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943306","url":null,"abstract":"Raman spectroscopy has for a long time performed as a common analytical technique in spectroscopic applications. A Raman spectrum depends upon how efficiently a molecule scatters the incident light (electron rich molecules often produce strong signals) which results in difficulties for relating the spectrum to the absolute amounts of present substances. The spectrum is however a stable and accurate representation of the sample measured especially considering that each molecule is associated with a unique spectrum. State-of-the-art spectroscopic calibration methods include the principal component regression (PCR) and partial least squares regression (PLSR) methods which have been proved to be efficient regression methods to realise the quantitative analysis of Raman spectrum. In this paper we consider the problem of Raman spectra deconvolution to analyse the sample composition, as well as possible unknown substances. In particular, we propose a sparse regularized model as a complement to traditional regression methods by leveraging the components sparsity compared to the whole chemical library and the spectra sparsity, given that the chemical fingerprint of each spectrum is mainly determined by the peaks. Experimental results illustrate the effectiveness of this sparse regularized model.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"32 21","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941470","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943324
Tarek Mouats, N. Aouf
A background subtraction (BS) technique based on the fusion of thermal and visible imagery using Gaussian mixture models (GMM) is presented in this work. An automatic daytime/night-time detection is introduced that can be used to dynamically adapting the fusion scheme. Three fusion schemes are investigated and coined as early, late and image fusion. The first consists in augmenting the GMM model with thermal information prior to foreground segmentation. The second, as it name indicates, consists in the fusion of the outputs of BS applied to each sensor separately. The last one considers different linear combinations of both images forming a hybrid image. Most approaches improve the performance of the combined system by compensating the failures of individual sensors. Quantitative as well as qualitative results are shown to demonstrate the accuracy of each fusion approach with respect to foreground segmentation.
{"title":"Fusion of thermal and visible images for day/night moving objects detection","authors":"Tarek Mouats, N. Aouf","doi":"10.1109/SSPD.2014.6943324","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943324","url":null,"abstract":"A background subtraction (BS) technique based on the fusion of thermal and visible imagery using Gaussian mixture models (GMM) is presented in this work. An automatic daytime/night-time detection is introduced that can be used to dynamically adapting the fusion scheme. Three fusion schemes are investigated and coined as early, late and image fusion. The first consists in augmenting the GMM model with thermal information prior to foreground segmentation. The second, as it name indicates, consists in the fusion of the outputs of BS applied to each sensor separately. The last one considers different linear combinations of both images forming a hybrid image. Most approaches improve the performance of the combined system by compensating the failures of individual sensors. Quantitative as well as qualitative results are shown to demonstrate the accuracy of each fusion approach with respect to foreground segmentation.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121789933","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943307
Y. Altmann, S. Mclaughlin
This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.
{"title":"Nonlinear spectral unmixing of hyperspectral images using residual component analysis","authors":"Y. Altmann, S. Mclaughlin","doi":"10.1109/SSPD.2014.6943307","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943307","url":null,"abstract":"This paper presents a nonlinear mixing model for linear/nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are linear mixtures of endmembers, corrupted by an additional nonlinear term and an additive Gaussian noise. A Markov random field is considered for nonlinearity detection based on the spatial structure of the nonlinear terms. The observed image is segmented into regions where nonlinear terms, if present, share similar statistical properties. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint nonlinear unmixing and nonlinearity detection algorithm. Simulations conducted with real data show the accuracy of the proposed unmixing and nonlinearity detection strategy for the analysis of hyperspectral images.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"507 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115112208","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943321
P. Chambers, M. Sellathurai
This work presents a testbed implementation of a spectrum sensing algorithm for cognitive radio that is based on the autocorrelation function. Much of the work in current literature uses simulation based approaches to characterize functionality. In contrast here, the algorithm is applied in real-world channels and compared with appropriate simulations. It is shown how the algorithm may be improved to overcome the problem of frequency offset, which is a hardware-based impairment that current literature on the algorithm generally does not consider.
{"title":"Implementation of an autocorrelation-based spectrum sensing algorithm in real-world channels with frequency offset","authors":"P. Chambers, M. Sellathurai","doi":"10.1109/SSPD.2014.6943321","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943321","url":null,"abstract":"This work presents a testbed implementation of a spectrum sensing algorithm for cognitive radio that is based on the autocorrelation function. Much of the work in current literature uses simulation based approaches to characterize functionality. In contrast here, the algorithm is applied in real-world channels and compared with appropriate simulations. It is shown how the algorithm may be improved to overcome the problem of frequency offset, which is a hardware-based impairment that current literature on the algorithm generally does not consider.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121877931","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943309
D. Gaglione, C. Clemente, L. Pallotta, I. Proudler, A. De Maio, J. Soraghan
Automatic Target Recognition (ATR) is one of the most challenging areas of the modern radar signal processing field. In this paper a recognition algorithm for full-polarimetric SAR images, that is robust with respect to rotations and target roll, is presented. It is based on the use of the pseudo-Zernike moments and the Krogager decomposition components, and exploits multiple sources of information such as polarization and spatial diversity. The effectiveness of the proposed approach has been demonstrated with real full polarimetric SAR data.
{"title":"Krogager decomposition and Pseudo-Zernike moments for polarimetric distributed ATR","authors":"D. Gaglione, C. Clemente, L. Pallotta, I. Proudler, A. De Maio, J. Soraghan","doi":"10.1109/SSPD.2014.6943309","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943309","url":null,"abstract":"Automatic Target Recognition (ATR) is one of the most challenging areas of the modern radar signal processing field. In this paper a recognition algorithm for full-polarimetric SAR images, that is robust with respect to rotations and target roll, is presented. It is based on the use of the pseudo-Zernike moments and the Krogager decomposition components, and exploits multiple sources of information such as polarization and spatial diversity. The effectiveness of the proposed approach has been demonstrated with real full polarimetric SAR data.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122570061","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943323
R. Baxter, Michael J. V. Leach, N. Robertson
This paper presents the novel theory for performing behaviour-based tracking using intentional priors. Motivated by our ultimate goal of anomaly detection, our approach is rooted in building better models of target behaviour. Our novel extension of the Kalman filter combines motion information with an intentional prior. We apply our `Intentional Tracker' to a pedestrian surveillance and tracking problem, using head pose as the intentional prior. We perform a statistical analysis of pedestrian head pose behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using intentional priors our algorithm outperform a standard Kalman filter across a range of target trajectories.
{"title":"Tracking with intent","authors":"R. Baxter, Michael J. V. Leach, N. Robertson","doi":"10.1109/SSPD.2014.6943323","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943323","url":null,"abstract":"This paper presents the novel theory for performing behaviour-based tracking using intentional priors. Motivated by our ultimate goal of anomaly detection, our approach is rooted in building better models of target behaviour. Our novel extension of the Kalman filter combines motion information with an intentional prior. We apply our `Intentional Tracker' to a pedestrian surveillance and tracking problem, using head pose as the intentional prior. We perform a statistical analysis of pedestrian head pose behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using intentional priors our algorithm outperform a standard Kalman filter across a range of target trajectories.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126197436","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943314
D. Garren
This paper examines the signature characteristics in spotlight synthetic aperture radar (SAR) image data for surface targets that are executing braking maneuvers during the SAR collection. This analysis considers the special case in which the radar sensor is assumed to move with constant speed and heading on a level flight path with broadside imaging geometry. The analysis concentrates on the target migration effects in which the moving target smear exhibits some defocus in the range direction, although much smaller in magnitude than the smearing in the radar crossrange direction. This paper focuses on the case of a target that executes a rapid speed transition by decreasing its speed within the synthetic aperture. The SAR simulations are shown to give signatures that are in agreement with the predicted shapes.
{"title":"Signatures of braking surface targets in spotlight synthetic aperture radar","authors":"D. Garren","doi":"10.1109/SSPD.2014.6943314","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943314","url":null,"abstract":"This paper examines the signature characteristics in spotlight synthetic aperture radar (SAR) image data for surface targets that are executing braking maneuvers during the SAR collection. This analysis considers the special case in which the radar sensor is assumed to move with constant speed and heading on a level flight path with broadside imaging geometry. The analysis concentrates on the target migration effects in which the moving target smear exhibits some defocus in the range direction, although much smaller in magnitude than the smearing in the radar crossrange direction. This paper focuses on the case of a target that executes a rapid speed transition by decreasing its speed within the synthetic aperture. The SAR simulations are shown to give signatures that are in agreement with the predicted shapes.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124793985","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 : 2014-11-03DOI: 10.1109/SSPD.2014.6943326
Iain Rice, D. Lowe
This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.
{"title":"Topographic visual analytics of multibeam dynamic SONAR data","authors":"Iain Rice, D. Lowe","doi":"10.1109/SSPD.2014.6943326","DOIUrl":"https://doi.org/10.1109/SSPD.2014.6943326","url":null,"abstract":"This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point relative similarity measures) appropriate to the below-water maritime domain, we are investigating a preliminary prototype topographic visualisation model. The focus of the current paper is on the mathematical problem of exploiting a relative dissimilarity representation of signals in a visual informatics mapping model, driven by real-world sonar systems. A realistic noise model is explored and incorporated into non-linear and topographic visualisation algorithms building on the approach of [9]. Concepts are illustrated using a real world dataset of 32 hydrophones monitoring a shallow-water environment in which targets are present and dynamic.","PeriodicalId":133530,"journal":{"name":"2014 Sensor Signal Processing for Defence (SSPD)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126626028","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：