Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104256
Juliano G. C. Ribeiro, Natsuki Ueno, Shoichi Koyama, H. Saruwatari
A method for interpolating the acoustic transfer function (ATF) between predetermined source and receiver regions is proposed. A previous work has shown that the region-toregion ATF can be estimated by separating it into a reverberant component added to a given direct component and representing the reverberant component with a finite sum of spherical wavefunctions. Our proposed method is based on the kernel ridge regression for estimating the reverberant component with a reproducing kernel Hilbert space defined to include acoustic properties into the interpolation. The proposed method based on the infinite dimensional expansion into spherical wavefunctions is independent of the empirical truncation used in the previous method. Furthermore, by taking the acoustic reciprocity into consideration, more accurate estimations are possible with a limited set of measurements compared to the truncation-based method. The advantages of the proposed method were validated by experiments based on a three-dimensional acoustic simulation.
{"title":"Kernel interpolation of acoustic transfer function between regions considering reciprocity","authors":"Juliano G. C. Ribeiro, Natsuki Ueno, Shoichi Koyama, H. Saruwatari","doi":"10.1109/SAM48682.2020.9104256","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104256","url":null,"abstract":"A method for interpolating the acoustic transfer function (ATF) between predetermined source and receiver regions is proposed. A previous work has shown that the region-toregion ATF can be estimated by separating it into a reverberant component added to a given direct component and representing the reverberant component with a finite sum of spherical wavefunctions. Our proposed method is based on the kernel ridge regression for estimating the reverberant component with a reproducing kernel Hilbert space defined to include acoustic properties into the interpolation. The proposed method based on the infinite dimensional expansion into spherical wavefunctions is independent of the empirical truncation used in the previous method. Furthermore, by taking the acoustic reciprocity into consideration, more accurate estimations are possible with a limited set of measurements compared to the truncation-based method. The advantages of the proposed method were validated by experiments based on a three-dimensional acoustic simulation.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"23 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82181221","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104252
Linjie Yan, Cong'an Xu, Da Xu, C. Hao
In this paper, we focus on the problem of point-like targets detection in a partially homogeneous interference environment with unknown covariance matrix. To this end, we assume the disturbances in both the cell under test and the secondary data share the same covariance matrix up to an unknown power scaling factor. Specifically, we jointly exploit the spillover of target energy to consecutive range samples and the persymmetric structure of the disturbance covariance matrix to improve the performances of target detection and range estimation. An adaptive architecture, referred to as the persymmetric modified AMF for partially homogeneous environment, is developed by relying on the ad hoc modifications of the generalized likelihood ratio test. Finally, a preliminary performance assessment highlights that the proposed decision scheme guarantees better detection and range localization performance compared with their natural competitors in sample starved environment.
{"title":"A Persymmetric AMF for range localization in partially homogenous environment","authors":"Linjie Yan, Cong'an Xu, Da Xu, C. Hao","doi":"10.1109/SAM48682.2020.9104252","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104252","url":null,"abstract":"In this paper, we focus on the problem of point-like targets detection in a partially homogeneous interference environment with unknown covariance matrix. To this end, we assume the disturbances in both the cell under test and the secondary data share the same covariance matrix up to an unknown power scaling factor. Specifically, we jointly exploit the spillover of target energy to consecutive range samples and the persymmetric structure of the disturbance covariance matrix to improve the performances of target detection and range estimation. An adaptive architecture, referred to as the persymmetric modified AMF for partially homogeneous environment, is developed by relying on the ad hoc modifications of the generalized likelihood ratio test. Finally, a preliminary performance assessment highlights that the proposed decision scheme guarantees better detection and range localization performance compared with their natural competitors in sample starved environment.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"29 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82209149","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}
Spike and Slab prior is a well-suited sparsity promoting prior, which is widely used to recover sampled signal in Bayesian inference. However, some sparse signal further involve more prior information-block sparsity structure which the standard Spike and Slab prior cannot cover. Alternatively, the original optimization problem is a hard non-convex problem, which is usually solved through simplifying the assumptions, relaxations or even relying on strong data computing capability. Therefore, a novel block adaptive matching pursuit (BAMP) method based on a hierarchical Bayesian model is proposed, which both use block spike and slab prior to recover sampled signal with exploiting underlying block sparsity structure and settle the non-convex problem more efficiently. In addition, the intermediate steps of the method are calculated by alternating direction method of multipliers (ADMM) algorithm which makes the method much faster. Experimental results on both synthetic data and real dataset demonstrate the proposed BAMP algorithm perform better superior compared with other novel algorithms released in recent years.
{"title":"Block-Sparse Signal Recovery Based on Adaptive Matching Pursuit via Spike and Slab Prior","authors":"Fuzai Lv, Changhao Zhang, Zhifeng Tang, Pengfei Zhang","doi":"10.1109/SAM48682.2020.9104311","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104311","url":null,"abstract":"Spike and Slab prior is a well-suited sparsity promoting prior, which is widely used to recover sampled signal in Bayesian inference. However, some sparse signal further involve more prior information-block sparsity structure which the standard Spike and Slab prior cannot cover. Alternatively, the original optimization problem is a hard non-convex problem, which is usually solved through simplifying the assumptions, relaxations or even relying on strong data computing capability. Therefore, a novel block adaptive matching pursuit (BAMP) method based on a hierarchical Bayesian model is proposed, which both use block spike and slab prior to recover sampled signal with exploiting underlying block sparsity structure and settle the non-convex problem more efficiently. In addition, the intermediate steps of the method are calculated by alternating direction method of multipliers (ADMM) algorithm which makes the method much faster. Experimental results on both synthetic data and real dataset demonstrate the proposed BAMP algorithm perform better superior compared with other novel algorithms released in recent years.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"51 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82731814","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104287
Xianxiang Yu, G. Cui, Zhenghong Zhang, Lin Zhou, Jing Yang, L. Kong
This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.
{"title":"Discrete-Phase Waveform Design to Quadratic Optimization via an ADPM Framework with Convergence Guarantee","authors":"Xianxiang Yu, G. Cui, Zhenghong Zhang, Lin Zhou, Jing Yang, L. Kong","doi":"10.1109/SAM48682.2020.9104287","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104287","url":null,"abstract":"This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"48 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83064707","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}
Path planning is one of the key technologies to realize the hidden navigation of underwater vehicles during long-haul. Path planning efficiency and accuracy are at the core of submarine track planning. Combining the navigation task with the geomagnetic map adaptability, the optimal path between the starting point and the target point is searched in the target space. The underwater geomagnetic navigation path planning model is established, and the principle and implementation method of Dijkstra algorithm are analyzed. An underwater geomagnetic navigation path planning model is established, and the Dijkstra algorithm is used for underwater geomagnetic navigation path planning. Combining different local windows in the adaptation area, the path planning calculation time and track cost are optimized. The simulation analyzes the influence of different local windows on the path planning in the adaptation area. The experiment results demonstrate that the Dijkstra algorithm can effectively find the optimal path that satisfies the constraints.
{"title":"Application of Dijkstra Algorithm in Path Planning for Geomagnetic Navigation","authors":"Qingya Liu, Hanchen Xu, Lihui Wang, Jin Chen, Yaoming Li, Lizhang Xu","doi":"10.1109/SAM48682.2020.9104382","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104382","url":null,"abstract":"Path planning is one of the key technologies to realize the hidden navigation of underwater vehicles during long-haul. Path planning efficiency and accuracy are at the core of submarine track planning. Combining the navigation task with the geomagnetic map adaptability, the optimal path between the starting point and the target point is searched in the target space. The underwater geomagnetic navigation path planning model is established, and the principle and implementation method of Dijkstra algorithm are analyzed. An underwater geomagnetic navigation path planning model is established, and the Dijkstra algorithm is used for underwater geomagnetic navigation path planning. Combining different local windows in the adaptation area, the path planning calculation time and track cost are optimized. The simulation analyzes the influence of different local windows on the path planning in the adaptation area. The experiment results demonstrate that the Dijkstra algorithm can effectively find the optimal path that satisfies the constraints.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"36 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88319545","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104388
Yuxuan Xia, P. Wang, K. Berntorp, H. Mansour, P. Boufounos, P. Orlik
This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.
{"title":"Extended Object Tracking Using Hierarchical Truncation Model with Partial-View Measurements","authors":"Yuxuan Xia, P. Wang, K. Berntorp, H. Mansour, P. Boufounos, P. Orlik","doi":"10.1109/SAM48682.2020.9104388","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104388","url":null,"abstract":"This paper introduces the hierarchical truncated Gaussian model in representing automotive radar measurements for extended object tracking. The model aims at a flexible spatial distribution with adaptive truncation bounds to account for partial-view measurements caused by self-occlusion. Built on a random matrix approach, we propose a new state update step together with an adaptively update of the truncation bounds. This is achieved by introducing spatial-domain pseudo measurements and by aggregating partial-view measurements over consecutive time-domain scans. The effectiveness of the proposed algorithm is verified on a synthetic dataset and an independent dataset generated using the MathWorks Automated Driving toolbox.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"36 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90410500","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104225
Saliha Meziani, A. Belouchrani, K. Abed-Meraim
In this paper, an Approximate Joint Diagonalization (AJD) approach is proposed to separate dependent source signals. The diagonal structure of the Auto Regressive Moving Average (ARMA) matrix coefficients moves the problem from Blind Source Separation (BSS) to AJD one. The identified matrix coefficients of the observed signal are jointly diagonalized to achieve the mixture matrix identification. Simulation results are provided to illustrate the effectiveness of the proposed approach.
{"title":"Approximate Joint Diagonalization for ARMA Dependent Source Separation","authors":"Saliha Meziani, A. Belouchrani, K. Abed-Meraim","doi":"10.1109/SAM48682.2020.9104225","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104225","url":null,"abstract":"In this paper, an Approximate Joint Diagonalization (AJD) approach is proposed to separate dependent source signals. The diagonal structure of the Auto Regressive Moving Average (ARMA) matrix coefficients moves the problem from Blind Source Separation (BSS) to AJD one. The identified matrix coefficients of the observed signal are jointly diagonalized to achieve the mixture matrix identification. Simulation results are provided to illustrate the effectiveness of the proposed approach.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74889250","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104318
Yang Li, Qian He, Rick S. Blum, A. Haimovich
This paper addresses the problem of target detection against a background of clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector under the Neyman-Pearson criterion for general frequency snapshots selection with arbitrary DOF. If the clutter statistics are known/well-estimated, a greedy method for selecting the frequency snapshots is presented. For unknown clutter statistics, we employ a uniform random frequency snapshot selection method and show how the DOF employed affects the detection performance.
{"title":"Target Detection in Clutter Using Receiver with Reduced DOF in Frequency Domain","authors":"Yang Li, Qian He, Rick S. Blum, A. Haimovich","doi":"10.1109/SAM48682.2020.9104318","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104318","url":null,"abstract":"This paper addresses the problem of target detection against a background of clutter by using frequency snapshots with reduced degrees of freedom (DOF). We derive the optimal detector under the Neyman-Pearson criterion for general frequency snapshots selection with arbitrary DOF. If the clutter statistics are known/well-estimated, a greedy method for selecting the frequency snapshots is presented. For unknown clutter statistics, we employ a uniform random frequency snapshot selection method and show how the DOF employed affects the detection performance.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"98 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77200213","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 : 2020-06-01DOI: 10.1109/SAM48682.2020.9104389
Mengmeng Ge, G. Cui, Zhenghong Zhang, Lin Zhou, Xianxiang Yu, F. Yang, L. Kong
The presence of mainlobe jamming will significantly reduce the radar detection capabilities. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric SIMO (P-SIMO) radar to resist mainlobe jamming. Specifically, the signal model of P-SIMO radar accounting for the target and jamming signals is derived. Then, the approach based on ICA is utilized to separate the target component and jamming component while achieving the mainlobe jamming suppression. Finally, the effectiveness and capacities of proposed method are demonstrated by simulations, and the results show that the proposed method outperforms conventional ICA-based method.
{"title":"Mainlobe Jamming Suppression Via Independent Component Analysis for Polarimetric SIMO Radar","authors":"Mengmeng Ge, G. Cui, Zhenghong Zhang, Lin Zhou, Xianxiang Yu, F. Yang, L. Kong","doi":"10.1109/SAM48682.2020.9104389","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104389","url":null,"abstract":"The presence of mainlobe jamming will significantly reduce the radar detection capabilities. Conventional independent component analysis (ICA)-based methods will suffer from the ineffectiveness when the angle of the target is same as that of the jammer. In this paper, exploring polarization characteristics, we propose an approach based on ICA for polarimetric SIMO (P-SIMO) radar to resist mainlobe jamming. Specifically, the signal model of P-SIMO radar accounting for the target and jamming signals is derived. Then, the approach based on ICA is utilized to separate the target component and jamming component while achieving the mainlobe jamming suppression. Finally, the effectiveness and capacities of proposed method are demonstrated by simulations, and the results show that the proposed method outperforms conventional ICA-based method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"114 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77628511","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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