Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104224
Suleiman Erateb, J. Chambers
Independent vector analysis (IVA) is a blind source separation (BSS) technique that has demonstrated efficiency in separating speech signals from their convolutive mixtures in the frequency domain. Particularly, it avoids the problematic permutation problem by using a multivariate source prior to model statistical inter dependency across the frequency bins of each source signal. The selection of the source prior is vital to the performance of the method. Practical real time systems require an online mode which is performed iteratively as signal data arrive. The performance of the online IVA is measured by the convergence time and steady state separation and accuracy. This paper proposes a novel switched source prior technique to improve the performance of the online IVA algorithm. The techniques switches between two source priors to acquire the better performance properties of both distributions at different stages of the learning algorithm. The switching process is controlled by an adaptive learning scheme as a function of proximity to the target solution. The experimental results demonstrate an enhanced separation performance using real room impulse responses and recorded speech signals.
{"title":"Enhanced Online IVA with Switched Source Prior for Speech Separation","authors":"Suleiman Erateb, J. Chambers","doi":"10.1109/SAM48682.2020.9104224","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104224","url":null,"abstract":"Independent vector analysis (IVA) is a blind source separation (BSS) technique that has demonstrated efficiency in separating speech signals from their convolutive mixtures in the frequency domain. Particularly, it avoids the problematic permutation problem by using a multivariate source prior to model statistical inter dependency across the frequency bins of each source signal. The selection of the source prior is vital to the performance of the method. Practical real time systems require an online mode which is performed iteratively as signal data arrive. The performance of the online IVA is measured by the convergence time and steady state separation and accuracy. This paper proposes a novel switched source prior technique to improve the performance of the online IVA algorithm. The techniques switches between two source priors to acquire the better performance properties of both distributions at different stages of the learning algorithm. The switching process is controlled by an adaptive learning scheme as a function of proximity to the target solution. The experimental results demonstrate an enhanced separation performance using real room impulse responses and recorded speech signals.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"40 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":"90074162","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/SAM48682.2020.9104269
Yuxing Wang, Jun Tao, Le Yang, F. Yu, Chunguo Li, Xiao Han
For tracking time-varying underwater acoustic (UWA) channels, a state-space model based scheme generally outperforms a direct adaptive method. The success for the former depends on the choice of a proper state transition model as well as accurate estimation of the model parameters. The autoregressive (AR) transition model has proven to be useful and the key is to determine the AR coefficients so as to achieve a good channel tracking performance. In this paper, we revisit the problem of determining the AR coefficients via Yule-Walker equation, for which the required autocorrelations are estimated as an ensemble average of estimated channel impulse responses (CIRs). Different from existing scheme employing least squares (LS) channel estimation, we propose to obtain a sequence of CIR estimations via adaptive schemes. The advantage is twofold: first, complexity reduction is achieved and the saving can be significant for a UWA channel with extensive delay spread; second, improved tracking performance is achieved as the implicit assumption by the LS method that the channel remains constant over a block is not required. We also propose to dynamically update the autocorrelations and AR coefficients as the channel tracking progresses, such that the variation in the channel statistical property can be captured. Both simulations and experimental results verify the performance gain of the proposed model-based channel tracking scheme.
{"title":"Improved Model-Based Channel Tracking for Underwater Acoustic Communications","authors":"Yuxing Wang, Jun Tao, Le Yang, F. Yu, Chunguo Li, Xiao Han","doi":"10.1109/SAM48682.2020.9104269","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104269","url":null,"abstract":"For tracking time-varying underwater acoustic (UWA) channels, a state-space model based scheme generally outperforms a direct adaptive method. The success for the former depends on the choice of a proper state transition model as well as accurate estimation of the model parameters. The autoregressive (AR) transition model has proven to be useful and the key is to determine the AR coefficients so as to achieve a good channel tracking performance. In this paper, we revisit the problem of determining the AR coefficients via Yule-Walker equation, for which the required autocorrelations are estimated as an ensemble average of estimated channel impulse responses (CIRs). Different from existing scheme employing least squares (LS) channel estimation, we propose to obtain a sequence of CIR estimations via adaptive schemes. The advantage is twofold: first, complexity reduction is achieved and the saving can be significant for a UWA channel with extensive delay spread; second, improved tracking performance is achieved as the implicit assumption by the LS method that the channel remains constant over a block is not required. We also propose to dynamically update the autocorrelations and AR coefficients as the channel tracking progresses, such that the variation in the channel statistical property can be captured. Both simulations and experimental results verify the performance gain of the proposed model-based channel tracking scheme.","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":"91381176","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/SAM48682.2020.9104302
Yanning Shen
Canonical correlation analysis (CCA) is a well-documented subspace learning approach widely used to seek for hidden sources common to two or multiple datasets. CCA has been applied in various learning tasks, such as dimensionality reduction, blind source separation, classification, and data fusion. Specifically, CCA aims at finding the subspaces for multi-view datasets, such that the projections of the multiple views onto the sought subspace is maximally correlated. However, simple linear projections may not be able to exploit general nonlinear projections, which motivates the development of nonlinear CCA. However, both conventional CCA and its non-linear variants do not take into consideration the data privacy, which is crucial especially when coping with personal data. To address this limitation, the present paper studies differentially private (DP) scheme for nonlinear CCA with privacy guarantee. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.
{"title":"Differentially Private Nonlinear Canonical Correlation Analysis","authors":"Yanning Shen","doi":"10.1109/SAM48682.2020.9104302","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104302","url":null,"abstract":"Canonical correlation analysis (CCA) is a well-documented subspace learning approach widely used to seek for hidden sources common to two or multiple datasets. CCA has been applied in various learning tasks, such as dimensionality reduction, blind source separation, classification, and data fusion. Specifically, CCA aims at finding the subspaces for multi-view datasets, such that the projections of the multiple views onto the sought subspace is maximally correlated. However, simple linear projections may not be able to exploit general nonlinear projections, which motivates the development of nonlinear CCA. However, both conventional CCA and its non-linear variants do not take into consideration the data privacy, which is crucial especially when coping with personal data. To address this limitation, the present paper studies differentially private (DP) scheme for nonlinear CCA with privacy guarantee. Numerical tests on real datasets are carried out to showcase the effectiveness of the proposed algorithms.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"21 6 Suppl 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":"78021203","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/SAM48682.2020.9104271
C. Wang, Wee Peng Tay, Yang Song
Each node or sensor in a network makes a local observation that is linearly related to a set of public and private parameters. The nodes send their observations to a fusion center to allow it to estimate a set of public parameters. However, the fusion center may also abuse this information to estimate other private parameters. To prevent leakage of the private parameters, each node first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We consider the maximum privacy achievable under perfect utility in terms of the Cramer-Rao lower bounds. We propose a method to maximize the estimation error for inferring the private parameters while ensuring the estimation error for inferring the public parameters remains unchanged after sanitizing the sensors’ measurements.
{"title":"Maximum Privacy under Perfect Utility in Sensor Networks","authors":"C. Wang, Wee Peng Tay, Yang Song","doi":"10.1109/SAM48682.2020.9104271","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104271","url":null,"abstract":"Each node or sensor in a network makes a local observation that is linearly related to a set of public and private parameters. The nodes send their observations to a fusion center to allow it to estimate a set of public parameters. However, the fusion center may also abuse this information to estimate other private parameters. To prevent leakage of the private parameters, each node first sanitizes its local observation using a local privacy mechanism before transmitting it to the fusion center. We consider the maximum privacy achievable under perfect utility in terms of the Cramer-Rao lower bounds. We propose a method to maximize the estimation error for inferring the private parameters while ensuring the estimation error for inferring the public parameters remains unchanged after sanitizing the sensors’ measurements.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"77 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":"80333950","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, a beampattern design method is proposed for the multi-input-multi-output (MIMO) dual-function radar-communication (DFRC) system with an interleaved array. To make full use of the whole aperture, first, the transmit array is partitioned into two interleaved subarrays, one is for radar and the other is for downlink communications. Then, both the radar waveform and communication beamformers are optimized by combining the null-space projection (NSP) method and cyclic approach (CA) to perform the coexistence of MIMO radar and downlink communications. Besides, the communication beampattern can be utilized to improve the detection performance. Finally, several numerical results are given to show the effectiveness of the proposed method.
{"title":"Transmit Beampattern Design for Dual-Function Radar-Communication System with an Interleaved Array","authors":"Yufeng Chen, G. Liao, Zhiwei Yang, Shengqi Zhu, Yongjun Liu, Mengchao Jiang","doi":"10.1109/SAM48682.2020.9104349","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104349","url":null,"abstract":"In this paper, a beampattern design method is proposed for the multi-input-multi-output (MIMO) dual-function radar-communication (DFRC) system with an interleaved array. To make full use of the whole aperture, first, the transmit array is partitioned into two interleaved subarrays, one is for radar and the other is for downlink communications. Then, both the radar waveform and communication beamformers are optimized by combining the null-space projection (NSP) method and cyclic approach (CA) to perform the coexistence of MIMO radar and downlink communications. Besides, the communication beampattern can be utilized to improve the detection performance. Finally, several numerical results are given to show the effectiveness of the proposed method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"45 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":"78614677","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/SAM48682.2020.9104227
Ammar Ahmed, D. Silage, Yimin D. Zhang
We present an intelligent sensor array-based joint radarcommunication system which exploits chance constrained programming to develop a robust beamforming design. Probabilistic chance constraints are introduced for the communication operation where the communication objectives are achieved with a desired success rate in the presence of communication channel uncertainties. The chance constraint optimization is then relaxed to form a deterministic and convex problem by employing the statistical profile of the communication channels. Simulation results illustrate the performance of the proposed strategy.
{"title":"Chance Constrained Beamforming for Joint Radar-Communication Systems","authors":"Ammar Ahmed, D. Silage, Yimin D. Zhang","doi":"10.1109/SAM48682.2020.9104227","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104227","url":null,"abstract":"We present an intelligent sensor array-based joint radarcommunication system which exploits chance constrained programming to develop a robust beamforming design. Probabilistic chance constraints are introduced for the communication operation where the communication objectives are achieved with a desired success rate in the presence of communication channel uncertainties. The chance constraint optimization is then relaxed to form a deterministic and convex problem by employing the statistical profile of the communication channels. Simulation results illustrate the performance of the proposed strategy.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"5 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":"75928784","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/SAM48682.2020.9104332
Z. Xu, Bang Huang, Huawei Hu, Hui Chen, Wen-qin Wang
Frequency-diverse array (FDA) can provide a rangeangle-time dependent beamforming capability that could make a difference in some radar applications. However, the joint rangeangle estimation of FDA will inevitably increase the complexity due to the coupling range and angle response. Estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm cannot be used directly to estimate the angle and range because it does not meet the rotation invariance criterion. In this paper, an ambiguity function (AF)-based method is proposed to avoid the coupling range and angle problem for the FDA and multiple-input multiple-output (MIMO) combined radar to realize high-resolution range and angle estimation. Numerical results show its advantages over conventional method.
{"title":"Ambiguity Function-Based ESPRIT Algorithm for FDA-MIMO Radar Target Localization","authors":"Z. Xu, Bang Huang, Huawei Hu, Hui Chen, Wen-qin Wang","doi":"10.1109/SAM48682.2020.9104332","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104332","url":null,"abstract":"Frequency-diverse array (FDA) can provide a rangeangle-time dependent beamforming capability that could make a difference in some radar applications. However, the joint rangeangle estimation of FDA will inevitably increase the complexity due to the coupling range and angle response. Estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm cannot be used directly to estimate the angle and range because it does not meet the rotation invariance criterion. In this paper, an ambiguity function (AF)-based method is proposed to avoid the coupling range and angle problem for the FDA and multiple-input multiple-output (MIMO) combined radar to realize high-resolution range and angle estimation. Numerical results show its advantages over conventional method.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"60 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":"75932971","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/SAM48682.2020.9104359
Huiping Huang, A. Zoubir, H. So
This paper introduces a compressive sensing approach for single-snapshot adaptive beamforming. The observation data model is considered as source components in additive white noise, and then a compressive sensing formulation is introduced to estimate the parameters of the interference signals. That is, a LASSO regression problem is proposed and solved, yielding the directions as well as the powers of the interference signals. On the other hand, the noise power is estimated by means of averaging the squares of the difference between the observation data and the estimate of the source components. Finally, the interference-plus-noise covariance matrix is reconstructed and used for adaptive beamformer design. Simulation results show better performance of the proposed beamformer than several existing beamformers, in the case of a single snapshot.
{"title":"A Compressive Sensing Approach for Single-Snapshot Adaptive Beamforming","authors":"Huiping Huang, A. Zoubir, H. So","doi":"10.1109/SAM48682.2020.9104359","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104359","url":null,"abstract":"This paper introduces a compressive sensing approach for single-snapshot adaptive beamforming. The observation data model is considered as source components in additive white noise, and then a compressive sensing formulation is introduced to estimate the parameters of the interference signals. That is, a LASSO regression problem is proposed and solved, yielding the directions as well as the powers of the interference signals. On the other hand, the noise power is estimated by means of averaging the squares of the difference between the observation data and the estimate of the source components. Finally, the interference-plus-noise covariance matrix is reconstructed and used for adaptive beamformer design. Simulation results show better performance of the proposed beamformer than several existing beamformers, in the case of a single snapshot.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"111 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":"79308356","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/SAM48682.2020.9104351
C. Chu, Yi-jun Chen, Qun Zhang, Ying Luo
The performance of MIMO radar is directly affected by its transmitting waveforms. The waveform design is one of the critical issues in the design of MIMO radar system. In this paper, a new MIMO radar waveform design method based on time domain and frequency domain joint optimization is proposed. Firstly, a continuous phase coded signal waveform set is chosen to be the optimization objective variable. The design objectives is that all waveforms in the set are orthogonal in time domain, and the power spectral density (PSD) of every waveform approximates to the desired distribution in the frequency domain. According to the requirements, the problems in time domain and frequency domain are analyzed, respectively. Meanwhile, two objective functions based on minimizing the weighted correlation sidelobe level (MWISL) and minimizing the stopband power spectral density (MSPSD) are established. Then, an optimal scale factor is introduced to integrate the time domain and frequency domain into a combined model and a close form solution of code element is deduced through proper simplification and equalization of the time-frequency (T-F) joint optimization model. A recursive algorithm is obtained by summarizing the derivation process. At last, numerical examples prove the effectiveness of the proposed method in matching desired spectrum distribution and decreasing correlation sidelobe.
{"title":"MIMO Radar Waveform Joint Optimization Design in Time and Frequency Domain","authors":"C. Chu, Yi-jun Chen, Qun Zhang, Ying Luo","doi":"10.1109/SAM48682.2020.9104351","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104351","url":null,"abstract":"The performance of MIMO radar is directly affected by its transmitting waveforms. The waveform design is one of the critical issues in the design of MIMO radar system. In this paper, a new MIMO radar waveform design method based on time domain and frequency domain joint optimization is proposed. Firstly, a continuous phase coded signal waveform set is chosen to be the optimization objective variable. The design objectives is that all waveforms in the set are orthogonal in time domain, and the power spectral density (PSD) of every waveform approximates to the desired distribution in the frequency domain. According to the requirements, the problems in time domain and frequency domain are analyzed, respectively. Meanwhile, two objective functions based on minimizing the weighted correlation sidelobe level (MWISL) and minimizing the stopband power spectral density (MSPSD) are established. Then, an optimal scale factor is introduced to integrate the time domain and frequency domain into a combined model and a close form solution of code element is deduced through proper simplification and equalization of the time-frequency (T-F) joint optimization model. A recursive algorithm is obtained by summarizing the derivation process. At last, numerical examples prove the effectiveness of the proposed method in matching desired spectrum distribution and decreasing correlation sidelobe.","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":"79526067","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/SAM48682.2020.9104376
Yu Zheng, Muran Guo, Lutao Liu
As the coprime array develops, the coprime multiple-input multiple-output (MIMO) radar has been proposed to achieve a large array aperture. However, holes also exist in the sum-difference coarray of the coprime MIMO radar, thus making the lags out of continuous range unavailable for the subspace based direction of arrival (DOA) estimation algorithm. In this paper, a coarray interpolation algorithm is proposed for the coprime MIMO radar to improve the estimation performance. The interpolation is completed by solving a nuclear norm based optimization problem, where the Toeplitz structure of the interpolated covariance matrix is exploited to reduce the computational complexity. The lags that are not continuous are utilized by using the proposed algorithm. Thus, the number of degrees of freedom (DOFs) and the estimation accuracy are improved. Numerical simulations are designed to examine the corresponding estimation performance.
{"title":"DOA Estimation Using Coarray Interpolation Algorithm Via Nuclear Norm Optimization for Coprime MIMO Radar","authors":"Yu Zheng, Muran Guo, Lutao Liu","doi":"10.1109/SAM48682.2020.9104376","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104376","url":null,"abstract":"As the coprime array develops, the coprime multiple-input multiple-output (MIMO) radar has been proposed to achieve a large array aperture. However, holes also exist in the sum-difference coarray of the coprime MIMO radar, thus making the lags out of continuous range unavailable for the subspace based direction of arrival (DOA) estimation algorithm. In this paper, a coarray interpolation algorithm is proposed for the coprime MIMO radar to improve the estimation performance. The interpolation is completed by solving a nuclear norm based optimization problem, where the Toeplitz structure of the interpolated covariance matrix is exploited to reduce the computational complexity. The lags that are not continuous are utilized by using the proposed algorithm. Thus, the number of degrees of freedom (DOFs) and the estimation accuracy are improved. Numerical simulations are designed to examine the corresponding estimation performance.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"24 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":"76864254","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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