Pub Date : 2019-05-01DOI: 10.1109/SSPD.2019.8751660
H. T. Hayvaci, Seden Hazal Gulen
In this paper, we deal with the problem of detecting point-like targets in the presence of multipath under the assumption of a partially homogeneous Gaussian disturbance with unknown covariance matrix. Therefore, we introduce an unknown scaling factor which represents the mismatch between the noise covariance matrices of test and training signals. Besides, we model the target echo as a combination of direct and multipath components where multipath echoes are thought of as scattered signals from a glistening surface which is referred to as diffuse multipath environment. Hence, the total multipath return is also represented as a Gaussian distributed random vector with an unknown covariance matrix. At the design stage, we construct a constrained Generalized Likelihood Ratio Test (GLRT) by assuming that the total primary data covariance structure, in the target present case, resembles to the covariance matrix obtained from secondary data up to a degree (related to noise scaling factor and multipath contribution). Finally, at the analysis stage, we compared the developed algorithm with the existing solutions available in the open literature. The results highlight that the new detector copes well with severe multipath conditions and has considerable scale-invariance.
{"title":"Adaptive Detection with Diffuse Multipath Exploitation in Partially Homogeneous Environments","authors":"H. T. Hayvaci, Seden Hazal Gulen","doi":"10.1109/SSPD.2019.8751660","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751660","url":null,"abstract":"In this paper, we deal with the problem of detecting point-like targets in the presence of multipath under the assumption of a partially homogeneous Gaussian disturbance with unknown covariance matrix. Therefore, we introduce an unknown scaling factor which represents the mismatch between the noise covariance matrices of test and training signals. Besides, we model the target echo as a combination of direct and multipath components where multipath echoes are thought of as scattered signals from a glistening surface which is referred to as diffuse multipath environment. Hence, the total multipath return is also represented as a Gaussian distributed random vector with an unknown covariance matrix. At the design stage, we construct a constrained Generalized Likelihood Ratio Test (GLRT) by assuming that the total primary data covariance structure, in the target present case, resembles to the covariance matrix obtained from secondary data up to a degree (related to noise scaling factor and multipath contribution). Finally, at the analysis stage, we compared the developed algorithm with the existing solutions available in the open literature. The results highlight that the new detector copes well with severe multipath conditions and has considerable scale-invariance.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126959282","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751656
Yang Xian, Yang Sun, Wenwu Wang, S. M. Naqvi
The performance of the audio-only neural networks based monaural speech separation methods is still limited, particularly when multiple-speakers are active. The very recent method [1] used the audio-video (AV) model to find the non-linear relationship between the noisy mixture and the desired speech signal. However, the over-fitting problem always happens when the AV model is trained. Hence, the separation performance is limited. To address this limitation, we propose a system with two sequentially trained AV models to separate the desired speech signal. In the proposed system, after the first AV model is trained, its output is used to calculate the training target of the second AV model, which is exploited to further improve the separation performance. The GRID audiovisual sentence corpus is used to generate the training and testing datasets. The signal to distortion ratio (SDR) and short-time objective intelligibility (STOI) proved the proposed system outperforms the state-of-the-art method.
{"title":"Two Stage Audio-Video Speech Separation using Multimodal Convolutional Neural Networks","authors":"Yang Xian, Yang Sun, Wenwu Wang, S. M. Naqvi","doi":"10.1109/SSPD.2019.8751656","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751656","url":null,"abstract":"The performance of the audio-only neural networks based monaural speech separation methods is still limited, particularly when multiple-speakers are active. The very recent method [1] used the audio-video (AV) model to find the non-linear relationship between the noisy mixture and the desired speech signal. However, the over-fitting problem always happens when the AV model is trained. Hence, the separation performance is limited. To address this limitation, we propose a system with two sequentially trained AV models to separate the desired speech signal. In the proposed system, after the first AV model is trained, its output is used to calculate the training target of the second AV model, which is exploited to further improve the separation performance. The GRID audiovisual sentence corpus is used to generate the training and testing datasets. The signal to distortion ratio (SDR) and short-time objective intelligibility (STOI) proved the proposed system outperforms the state-of-the-art method.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122308517","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751652
Murat Üney, L. Millefiori, P. Braca
In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determine the behaviour of typical trajectories of the model. Estimation of these parameters from trajectory samples facilitates learning of patterns and training of predictive models using trajectory data, e.g., automatic identification system (AIS) messages transmitted by vessels. We propose a six-degrees-of-freedom parameterisation and investigate the identifiability of these parameters using the Cramér-Rao bound matrix which we estimate using Monte Carlo methods. We demonstrate that some parameter configurations of interest are identifiable and their maximum likelihood estimate can be found using iterative optimisation algorithms. We demonstrate the efficacy of this approach on both simulated and real data.
{"title":"Maximum Likelihood Estimation in a Parametric Stochastic Trajectory Model","authors":"Murat Üney, L. Millefiori, P. Braca","doi":"10.1109/SSPD.2019.8751652","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751652","url":null,"abstract":"In this work, we consider maximum likelihood estimation of parameters in a stochastic trajectory model. The velocity paths are generated from an Ornstein-Uhlenbeck process and thus revert to a latent expected value. In addition to this expected velocity, parameters that specify the reversion characteristics and the process noise covariance determine the behaviour of typical trajectories of the model. Estimation of these parameters from trajectory samples facilitates learning of patterns and training of predictive models using trajectory data, e.g., automatic identification system (AIS) messages transmitted by vessels. We propose a six-degrees-of-freedom parameterisation and investigate the identifiability of these parameters using the Cramér-Rao bound matrix which we estimate using Monte Carlo methods. We demonstrate that some parameter configurations of interest are identifiable and their maximum likelihood estimate can be found using iterative optimisation algorithms. We demonstrate the efficacy of this approach on both simulated and real data.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131212226","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751647
M. Tidwell, J. Buck
Several authors previously found that echolocating animals aim their sonar beam askew of the target of interest. Analysis found the animals' beam aiming strategy maximized the Fisher Information (FI) about the target bearing encoded in the frequency spectrum of the received echoes by the transmitter's frequency dependent beampatterns. This paper reverses the focus from analysis to synthesis. We present design methods to maximize the FI of the bearing estimate at a desired angle using linear frequency modulated (LFM) waveforms transmitted by a continuous line source (CLS) transducer. If the center frequency of the transmitted chirp is sufficiently larger than the bandwidth, the angle maximizing the bearing FI is solely determined by the center frequency. Numerical simulations confirm the effectiveness of the proposed methods for several bearings and waveforms.
{"title":"Designing Linear FM Active Sonar Waveforms for Continuous Line Source Transducers to Maximize the Fisher Information at a Desired Bearing","authors":"M. Tidwell, J. Buck","doi":"10.1109/SSPD.2019.8751647","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751647","url":null,"abstract":"Several authors previously found that echolocating animals aim their sonar beam askew of the target of interest. Analysis found the animals' beam aiming strategy maximized the Fisher Information (FI) about the target bearing encoded in the frequency spectrum of the received echoes by the transmitter's frequency dependent beampatterns. This paper reverses the focus from analysis to synthesis. We present design methods to maximize the FI of the bearing estimate at a desired angle using linear frequency modulated (LFM) waveforms transmitted by a continuous line source (CLS) transducer. If the center frequency of the transmitted chirp is sufficiently larger than the bandwidth, the angle maximizing the bearing FI is solely determined by the center frequency. Numerical simulations confirm the effectiveness of the proposed methods for several bearings and waveforms.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122440813","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751661
Konstantinos A. Voulgaris, Mike E. Davies, Mehrdad Yaghoobi
Non-negative signals form an important class of sparse signals. Many algorithms have already been proposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. One fast implementation is the FNNOMP algorithm that updates the non-negative coefficients in an iterative manner. Even though FNNOMP is a good approach when working on libraries of small size, the operational time of the algorithm grows significantly when the size of the library is large. This is mainly due to the selection step of the algorithm that relies on matrix vector multiplications. We here introduce the Embedded Nearest Neighbor (E-NN) algorithm which accelerates the search over large datasets while it is guaranteed to find the most correlated atoms. We then replace the selection step of FNNOMP by E-NN. Furthermore we introduce the Update Nearest Neighbor (U-NN) at the look up table of FNNOMP in order to assure the non-negativity criteria of FNNOMP. The results indicate that the proposed methodology can accelerate FNNOMP with a factor 4 on a real dataset of Raman Spectra and with a factor of 22 on a synthetic dataset.
{"title":"Accelerated Search for Non-Negative Greedy Sparse Decomposition via Dimensionality Reduction","authors":"Konstantinos A. Voulgaris, Mike E. Davies, Mehrdad Yaghoobi","doi":"10.1109/SSPD.2019.8751661","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751661","url":null,"abstract":"Non-negative signals form an important class of sparse signals. Many algorithms have already been proposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. One fast implementation is the FNNOMP algorithm that updates the non-negative coefficients in an iterative manner. Even though FNNOMP is a good approach when working on libraries of small size, the operational time of the algorithm grows significantly when the size of the library is large. This is mainly due to the selection step of the algorithm that relies on matrix vector multiplications. We here introduce the Embedded Nearest Neighbor (E-NN) algorithm which accelerates the search over large datasets while it is guaranteed to find the most correlated atoms. We then replace the selection step of FNNOMP by E-NN. Furthermore we introduce the Update Nearest Neighbor (U-NN) at the look up table of FNNOMP in order to assure the non-negativity criteria of FNNOMP. The results indicate that the proposed methodology can accelerate FNNOMP with a factor 4 on a real dataset of Raman Spectra and with a factor of 22 on a synthetic dataset.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129405299","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751666
Antti Ilari Karjalainen, Roshenac Mitchell, Jose Vazquez
This research provides advances aiming to improve the adaptability and usability of Automatic Target Recognition (ATR) algorithms in new environments. We propose to use a Generative Adversarial Networks (GAN) based approach to add simulated contacts into real sidescan sonar images. Our results show that the GAN approach is able to create realistic contacts. We carried out a visual experiment to validate that a trained operator was unable to distinguish real objects from simulated objects. In addition, we demonstrate that an ATR tuned using simulated objects, generated by the GAN, achieves a comparable performance to an ATR tuned using real data.
{"title":"Training and Validation of Automatic Target Recognition Systems using Generative Adversarial Networks","authors":"Antti Ilari Karjalainen, Roshenac Mitchell, Jose Vazquez","doi":"10.1109/SSPD.2019.8751666","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751666","url":null,"abstract":"This research provides advances aiming to improve the adaptability and usability of Automatic Target Recognition (ATR) algorithms in new environments. We propose to use a Generative Adversarial Networks (GAN) based approach to add simulated contacts into real sidescan sonar images. Our results show that the GAN approach is able to create realistic contacts. We carried out a visual experiment to validate that a trained operator was unable to distinguish real objects from simulated objects. In addition, we demonstrate that an ATR tuned using simulated objects, generated by the GAN, achieves a comparable performance to an ATR tuned using real data.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130740210","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751667
D. Garren
Radar pulses are subject to delay and bending as a result of refraction through the earth's atmosphere. Such effects can yield overall scene defocus in synthetic aperture radar (SAR) images, since the amount of delay and bending can vary from one radar pulse to the next along the synthetic aperture due to spatially varying atmospheric conditions. A recent investigation has resulted in SAR autofocus techniques for estimating and compensating for these atmospheric delay and bending effects. The current analysis examines the performance of this autofocus algorithm for cases in which the atmospheric delay and bending are obtained from error profiles along the synthetic aperture which include both polynomial modeling and power-law contributions. Refocus results from the subject atmospheric-based autofocus methods are quite positive when applied to measured Ku-band radar imagery in which known delay and bending errors have been applied.
{"title":"Effects of Polynomial Plus Power-Law Errors on SAR Refraction Autofocus","authors":"D. Garren","doi":"10.1109/SSPD.2019.8751667","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751667","url":null,"abstract":"Radar pulses are subject to delay and bending as a result of refraction through the earth's atmosphere. Such effects can yield overall scene defocus in synthetic aperture radar (SAR) images, since the amount of delay and bending can vary from one radar pulse to the next along the synthetic aperture due to spatially varying atmospheric conditions. A recent investigation has resulted in SAR autofocus techniques for estimating and compensating for these atmospheric delay and bending effects. The current analysis examines the performance of this autofocus algorithm for cases in which the atmospheric delay and bending are obtained from error profiles along the synthetic aperture which include both polynomial modeling and power-law contributions. Refocus results from the subject atmospheric-based autofocus methods are quite positive when applied to measured Ku-band radar imagery in which known delay and bending errors have been applied.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123247383","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 : 2019-05-01DOI: 10.1109/SSPD.2019.8751657
Christoph Wasserzier, J. Worms, D. O’Hagan
Research on modern EW algorithms follows a trend of increasing digital hardware implementations. The powerful features of these digital algorithms allow detection, location, identification and jamming of hostile radars. This paper presents a sensor concept in which mature EW features are expanded by an active sensing component using noise radar technology. It is shown that the flawless integration of noise radar into the EW functionality is accompanied with effective separation of all concurrent but different tasks of the combined sensor. Experimental results are presented which underline the basic idea of noise radar technology being the key enabler of this combined sensor concept.
{"title":"How Noise Radar Technology Brings Together Active Sensing and Modern Electronic Warfare Techniques in a Combined Sensor Concept","authors":"Christoph Wasserzier, J. Worms, D. O’Hagan","doi":"10.1109/SSPD.2019.8751657","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751657","url":null,"abstract":"Research on modern EW algorithms follows a trend of increasing digital hardware implementations. The powerful features of these digital algorithms allow detection, location, identification and jamming of hostile radars. This paper presents a sensor concept in which mature EW features are expanded by an active sensing component using noise radar technology. It is shown that the flawless integration of noise radar into the EW functionality is accompanied with effective separation of all concurrent but different tasks of the combined sensor. Experimental results are presented which underline the basic idea of noise radar technology being the key enabler of this combined sensor concept.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117099480","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 : 2019-04-11DOI: 10.1109/SSPD.2019.8751644
Fan Liu, C. Masouros, H. Griffiths
In this paper, we focus on constant-modulus waveform design for the dual use of radar target detection and cellular transmission. As the MIMO radar typically transmits orthogonal waveforms to search potential targets, we aim at jointly minimizing the downlink multi-user interference and the non-orthogonality of the transmitted waveform. Given the non-convexity in both orthogonal and CM constraints, we decompose the formulated optimization problem as two sub-problems, where we solve one of the sub-problems by singular value decomposition and the other one by the Riemannian conjugate gradient algorithm. We then propose an alternating minimization approach to obtain a near-optimal solution to the original problem by iteratively solve the two sub-problems. Finally, we assess the effectiveness of the proposed approach via numerical simulations.
{"title":"Dual-Functional Radar-Communication Waveform Design Under Constant-Modulus and Orthogonality Constraints","authors":"Fan Liu, C. Masouros, H. Griffiths","doi":"10.1109/SSPD.2019.8751644","DOIUrl":"https://doi.org/10.1109/SSPD.2019.8751644","url":null,"abstract":"In this paper, we focus on constant-modulus waveform design for the dual use of radar target detection and cellular transmission. As the MIMO radar typically transmits orthogonal waveforms to search potential targets, we aim at jointly minimizing the downlink multi-user interference and the non-orthogonality of the transmitted waveform. Given the non-convexity in both orthogonal and CM constraints, we decompose the formulated optimization problem as two sub-problems, where we solve one of the sub-problems by singular value decomposition and the other one by the Riemannian conjugate gradient algorithm. We then propose an alternating minimization approach to obtain a near-optimal solution to the original problem by iteratively solve the two sub-problems. Finally, we assess the effectiveness of the proposed approach via numerical simulations.","PeriodicalId":281127,"journal":{"name":"2019 Sensor Signal Processing for Defence Conference (SSPD)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131865255","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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