Pub Date : 2022-05-01DOI: 10.1016/j.sigpro.2022.108626
S. Abrar, A. Zerguine, K. Abed-Meraim
{"title":"Adaptive algorithms for blind channel equalization in impulsive noise","authors":"S. Abrar, A. Zerguine, K. Abed-Meraim","doi":"10.1016/j.sigpro.2022.108626","DOIUrl":"https://doi.org/10.1016/j.sigpro.2022.108626","url":null,"abstract":"","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"76 1","pages":"108626"},"PeriodicalIF":0.0,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80671664","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}
—In this paper, we focus on the design of the transmit waveforms of a frequency diverse array (FDA) in order to improve the output signal-to-interference-plus-noise ratio (SINR) in the presence of signal-dependent mainlobe interference. Since the classical multi-carrier matched filtering-based FDA receiver cannot effectively utilize the waveform diversity of FDA, a novel FDA receiver framework based on multi-channel mixing and low-pass filtering is developed to keep the separation of the transmit waveform at the receiver side, while preserving the FDA range-controllable degrees of freedom. Furthermore, a range-angle minimum variance distortionless response beamforming technique is introduced to synthesize receiver filter weights with the ability to suppress a possible signal-dependent mainlobe interference. The resulting FDA transmit waveform design problem is initially formulated as an optimization problem consisting of a non-convex objective function and multiple non- convex constraints. To efficiently slove this, we introduce two algorithms, one based on a signal relaxation technique, and the other based on the majorization minimization technique. The preferable performance of the proposed multi-channel low- pass filtering receiver and the optimized transmit waveforms is illustrated using numerical simulations, indicating that the resulting FDA system is not only able to effectively suppress mainlobe interference, but also to yield estimates with a higher SINR than the FDA system without waveform optimization.
{"title":"Waveform optimization with SINR criteria for FDA radar in the presence of signal-dependent mainlobe interference","authors":"Wenkai Jia, A. Jakobsson, Wen-Qin Wang","doi":"10.2139/ssrn.4204373","DOIUrl":"https://doi.org/10.2139/ssrn.4204373","url":null,"abstract":"—In this paper, we focus on the design of the transmit waveforms of a frequency diverse array (FDA) in order to improve the output signal-to-interference-plus-noise ratio (SINR) in the presence of signal-dependent mainlobe interference. Since the classical multi-carrier matched filtering-based FDA receiver cannot effectively utilize the waveform diversity of FDA, a novel FDA receiver framework based on multi-channel mixing and low-pass filtering is developed to keep the separation of the transmit waveform at the receiver side, while preserving the FDA range-controllable degrees of freedom. Furthermore, a range-angle minimum variance distortionless response beamforming technique is introduced to synthesize receiver filter weights with the ability to suppress a possible signal-dependent mainlobe interference. The resulting FDA transmit waveform design problem is initially formulated as an optimization problem consisting of a non-convex objective function and multiple non- convex constraints. To efficiently slove this, we introduce two algorithms, one based on a signal relaxation technique, and the other based on the majorization minimization technique. The preferable performance of the proposed multi-channel low- pass filtering receiver and the optimized transmit waveforms is illustrated using numerical simulations, indicating that the resulting FDA system is not only able to effectively suppress mainlobe interference, but also to yield estimates with a higher SINR than the FDA system without waveform optimization.","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"1 1","pages":"108851"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91232490","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}
Frequency diverse array (FDA) differs from conventional array techniques in that it imposes an additional frequency offset (FO) across the array elements. The use of FO provides the FDA with the controllable degree of freedom in range dimension, offering preferable performance in joint angle and range localization, range-ambiguous clutter suppression, and low probability of intercept, as compared to its phased-array or multiple-input multiple-output (MIMO) counterparts. In particular, the FO of the coherent FDA is much smaller than the bandwidth of the baseband waveform, capable of obtaining higher transmit gain and output signal-to-interference-plus-noise ratio (SINR). In this paper, we investigate the problem of joint design of the transmit and receive weights for coherent FDA radar systems. The design problem is formulated as the maximization of the ratio of the power in the desired two-dimensional range-angle space to the power in the entire area, subject to an energy constraint that limits the emitted energy of each transmit antenna and a similarity constraint such that a good transmit beampattern can be guaranteed. Due to the resultant problem is NP-hard, therefore, a sequential optimization method based on semidefinite relaxation (SDR) technique is developed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.
{"title":"Joint design of the transmit and receive weights for coherent FDA radar","authors":"Wenkai Jia, Wen Wang, Shenmin Zhang","doi":"10.2139/ssrn.4204371","DOIUrl":"https://doi.org/10.2139/ssrn.4204371","url":null,"abstract":"Frequency diverse array (FDA) differs from conventional array techniques in that it imposes an additional frequency offset (FO) across the array elements. The use of FO provides the FDA with the controllable degree of freedom in range dimension, offering preferable performance in joint angle and range localization, range-ambiguous clutter suppression, and low probability of intercept, as compared to its phased-array or multiple-input multiple-output (MIMO) counterparts. In particular, the FO of the coherent FDA is much smaller than the bandwidth of the baseband waveform, capable of obtaining higher transmit gain and output signal-to-interference-plus-noise ratio (SINR). In this paper, we investigate the problem of joint design of the transmit and receive weights for coherent FDA radar systems. The design problem is formulated as the maximization of the ratio of the power in the desired two-dimensional range-angle space to the power in the entire area, subject to an energy constraint that limits the emitted energy of each transmit antenna and a similarity constraint such that a good transmit beampattern can be guaranteed. Due to the resultant problem is NP-hard, therefore, a sequential optimization method based on semidefinite relaxation (SDR) technique is developed. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme.","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"31 1","pages":"108834"},"PeriodicalIF":0.0,"publicationDate":"2022-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73648627","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}
{"title":"Reversible data hiding based on adaptive IPVO and two-segment pairwise PEE","authors":"Ningxiong Mao, Fan Chen, Hongjie He, Yaolin Yang","doi":"10.2139/ssrn.4011681","DOIUrl":"https://doi.org/10.2139/ssrn.4011681","url":null,"abstract":"","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"20 1","pages":"108577"},"PeriodicalIF":0.0,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72992830","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 : 2022-03-18DOI: 10.48550/arXiv.2203.10044
Y. Chen, Lei Cheng, Yik-Chung Wu
Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.
{"title":"Bayesian Low-rank Matrix Completion with Dual-graph Embedding: Prior Analysis and Tuning-free Inference","authors":"Y. Chen, Lei Cheng, Yik-Chung Wu","doi":"10.48550/arXiv.2203.10044","DOIUrl":"https://doi.org/10.48550/arXiv.2203.10044","url":null,"abstract":"Recently, there is a revival of interest in low-rank matrix completion-based unsupervised learning through the lens of dual-graph regularization, which has significantly improved the performance of multidisciplinary machine learning tasks such as recommendation systems, genotype imputation and image inpainting. While the dual-graph regularization contributes a major part of the success, computational costly hyper-parameter tunning is usually involved. To circumvent such a drawback and improve the completion performance, we propose a novel Bayesian learning algorithm that automatically learns the hyper-parameters associated with dual-graph regularization, and at the same time, guarantees the low-rankness of matrix completion. Notably, a novel prior is devised to promote the low-rankness of the matrix and encode the dual-graph information simultaneously, which is more challenging than the single-graph counterpart. A nontrivial conditional conjugacy between the proposed priors and likelihood function is then explored such that an efficient algorithm is derived under variational inference framework. Extensive experiments using synthetic and real-world datasets demonstrate the state-of-the-art performance of the proposed learning algorithm for various data analysis tasks.","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"50 1","pages":"108826"},"PeriodicalIF":0.0,"publicationDate":"2022-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90050624","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 : 2022-03-07DOI: 10.48550/arXiv.2203.03475
Rui Min, C. Garnier, Françcois Septier, John Klein
The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems. To overcome the curse of dimensionality of PF, the block PF (BPF) inserts a blocking step to partition the state space into several subspaces or blocks of smaller dimension so that the correction and resampling steps can be performed independently on each subspace. Using blocks of small size reduces the variance of the filtering distribution estimate, but in turn the correlation between blocks is broken and a bias is introduced. When the dependence relationships between state variables are unknown, it is not obvious to decide how to split the state space into blocks and a significant error overhead may arise from a poor choice of partitioning. In this paper, we formulate the partitioning problem in the BPF as a clustering problem and we propose a state space partitioning method based on spectral clustering (SC). We design a generalized BPF algorithm that contains two new steps: (i) estimation of the state vector correlation matrix from predicted particles, (ii) SC using this estimate as the similarity matrix to determine an appropriate partition. In addition, a constraint is imposed on the maximal cluster size to prevent SC from providing too large blocks. We show that the proposed method can bring together in the same blocks the most correlated state variables while successfully escaping the curse of dimensionality.
{"title":"State space partitioning based on constrained spectral clustering for block particle filtering","authors":"Rui Min, C. Garnier, Françcois Septier, John Klein","doi":"10.48550/arXiv.2203.03475","DOIUrl":"https://doi.org/10.48550/arXiv.2203.03475","url":null,"abstract":"The particle filter (PF) is a powerful inference tool widely used to estimate the filtering distribution in non-linear and/or non-Gaussian problems. To overcome the curse of dimensionality of PF, the block PF (BPF) inserts a blocking step to partition the state space into several subspaces or blocks of smaller dimension so that the correction and resampling steps can be performed independently on each subspace. Using blocks of small size reduces the variance of the filtering distribution estimate, but in turn the correlation between blocks is broken and a bias is introduced. When the dependence relationships between state variables are unknown, it is not obvious to decide how to split the state space into blocks and a significant error overhead may arise from a poor choice of partitioning. In this paper, we formulate the partitioning problem in the BPF as a clustering problem and we propose a state space partitioning method based on spectral clustering (SC). We design a generalized BPF algorithm that contains two new steps: (i) estimation of the state vector correlation matrix from predicted particles, (ii) SC using this estimate as the similarity matrix to determine an appropriate partition. In addition, a constraint is imposed on the maximal cluster size to prevent SC from providing too large blocks. We show that the proposed method can bring together in the same blocks the most correlated state variables while successfully escaping the curse of dimensionality.","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"67 1","pages":"108727"},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82710307","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 : 2022-03-01DOI: 10.1016/j.sigpro.2022.108535
Byung-Seo Park, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Kim, Young-ho Seo
{"title":"Iterative extrinsic calibration using virtual viewpoint for 3D reconstruction","authors":"Byung-Seo Park, Woosuk Kim, Jin-Kyum Kim, Dong-Wook Kim, Young-ho Seo","doi":"10.1016/j.sigpro.2022.108535","DOIUrl":"https://doi.org/10.1016/j.sigpro.2022.108535","url":null,"abstract":"","PeriodicalId":21745,"journal":{"name":"Signal Process.","volume":"500 1","pages":"108535"},"PeriodicalIF":0.0,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82587814","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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