Pub Date : 2020-06-01DOI: 10.1109/SAM48682.2020.9104258
Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yimin Liu, Yonina C. Eldar
Future cars will constantly sense the environment and interchange information with their surrounding in order to successfully choose routes, avoid hazards, and comply with traffic regulations. These vehicles will be equipped with multiple sensors, including automotive radar, as well as wireless communications capabilities. The similarity in hardware and signal processing of automotive radar and wireless communications motivates designing these functionalities in a joint manner. Such dual function radar-communications (DFRC) designs are the focus of a large body of recent works. These joint designs lead to substantial gains in size, cost, power consumption, and performance, making them especially important for vehicular applications, where both the radar and communications operate in similar ranges. This paper reviews a wide variety of existing DFRC strategies and their relevance to automotive systems. We discuss the pros and cons of current methods, mapping them in the context of vehicular application, and present the main challenges and possible research directions.
{"title":"Automotive Dual-Function Radar Communications Systems: An Overview","authors":"Dingyou Ma, Nir Shlezinger, Tianyao Huang, Yimin Liu, Yonina C. Eldar","doi":"10.1109/SAM48682.2020.9104258","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104258","url":null,"abstract":"Future cars will constantly sense the environment and interchange information with their surrounding in order to successfully choose routes, avoid hazards, and comply with traffic regulations. These vehicles will be equipped with multiple sensors, including automotive radar, as well as wireless communications capabilities. The similarity in hardware and signal processing of automotive radar and wireless communications motivates designing these functionalities in a joint manner. Such dual function radar-communications (DFRC) designs are the focus of a large body of recent works. These joint designs lead to substantial gains in size, cost, power consumption, and performance, making them especially important for vehicular applications, where both the radar and communications operate in similar ranges. This paper reviews a wide variety of existing DFRC strategies and their relevance to automotive systems. We discuss the pros and cons of current methods, mapping them in the context of vehicular application, and present the main challenges and possible research directions.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"37 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":"82639777","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/SAM48682.2020.9104380
Jingyu Cong, Xianpeng Wang, Mengxing Huang, G. Bi
The size of an over complete dictionary seriously affects the computation speed of on-grid sparse algorithms. In the case of multi parameter estimation, the required dictionary size increases rapidly by multiplication to ensure the accuracy of the results. Therefore, it becomes infeasible to estimate all of the parameters directly by on-grid methods. In this paper, the feasible sparse spectrum fitting (SpSF) algorithm for computing both the direction of arrival (DOA) and range estimation in collocated FDA-MIMO radars is introduced. Firstly, due to fact that a receive spatial frequency only depends on the angle, a covariance fitting technique for data preprocessing is adopted to reshape the data for DOA estimation. Next, the range is calculated in the transmit-receive spatial frequency domain by the SpSF algorithm. In addition, to improve the computational efficiency for an increased number of targets, the traditional convex optimization is replaced with a one-dimensional peak search approximation. Numerical simulations are carried out to verify the effectiveness of the proposed approach.
{"title":"Feasible Sparse Spectrum Fitting of DOA and Range Estimation for Collocated FDA-MIMO radars","authors":"Jingyu Cong, Xianpeng Wang, Mengxing Huang, G. Bi","doi":"10.1109/SAM48682.2020.9104380","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104380","url":null,"abstract":"The size of an over complete dictionary seriously affects the computation speed of on-grid sparse algorithms. In the case of multi parameter estimation, the required dictionary size increases rapidly by multiplication to ensure the accuracy of the results. Therefore, it becomes infeasible to estimate all of the parameters directly by on-grid methods. In this paper, the feasible sparse spectrum fitting (SpSF) algorithm for computing both the direction of arrival (DOA) and range estimation in collocated FDA-MIMO radars is introduced. Firstly, due to fact that a receive spatial frequency only depends on the angle, a covariance fitting technique for data preprocessing is adopted to reshape the data for DOA estimation. Next, the range is calculated in the transmit-receive spatial frequency domain by the SpSF algorithm. In addition, to improve the computational efficiency for an increased number of targets, the traditional convex optimization is replaced with a one-dimensional peak search approximation. Numerical simulations are carried out to verify the effectiveness of the proposed approach.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"82 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":"89012270","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/SAM48682.2020.9104309
Wei Zhang, Yi Jiang, Bin Zhou, Die Hu
Strong interferences occur in several wireless scenarios, such as full-duplex wireless communications and heterogenous networks in unlicensed spectrum. Because strong interferences can cause excessive quantization noise in the receiver’s analog-to-digital converters (ADC), mitigation of strong interferences needs to be conducted not only after but before the ADCs, i.e., via hybrid processing – an actively researched topic in recent years. In this paper, we propose to use an M-input M-output analog phase shifter network (PSN) between the receiving antennas and the ADCs to prewhiten spatially the interferences (plus signal and noise). This scheme, referred to as the Hybrid Interference Mitigation using Analog Prewhitening (HIMAP), requires no information about the interferences except an estimated spatial covariance matrix. Before the ADCs, the HIMAP scheme suppresses the strong interferences through optimizing the PSN; after the ADCs, the HIMAP suppresses the residual interferences through employing minimum mean squared error (MMSE) beamforming. The simulation results verify the effectiveness of the HIMAP scheme.
{"title":"Hybrid Interference Mitigation Using Analog Prewhitening","authors":"Wei Zhang, Yi Jiang, Bin Zhou, Die Hu","doi":"10.1109/SAM48682.2020.9104309","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104309","url":null,"abstract":"Strong interferences occur in several wireless scenarios, such as full-duplex wireless communications and heterogenous networks in unlicensed spectrum. Because strong interferences can cause excessive quantization noise in the receiver’s analog-to-digital converters (ADC), mitigation of strong interferences needs to be conducted not only after but before the ADCs, i.e., via hybrid processing – an actively researched topic in recent years. In this paper, we propose to use an M-input M-output analog phase shifter network (PSN) between the receiving antennas and the ADCs to prewhiten spatially the interferences (plus signal and noise). This scheme, referred to as the Hybrid Interference Mitigation using Analog Prewhitening (HIMAP), requires no information about the interferences except an estimated spatial covariance matrix. Before the ADCs, the HIMAP scheme suppresses the strong interferences through optimizing the PSN; after the ADCs, the HIMAP suppresses the residual interferences through employing minimum mean squared error (MMSE) beamforming. The simulation results verify the effectiveness of the HIMAP scheme.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"87 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":"83775224","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/SAM48682.2020.9104356
Zhongtao Chen, Lei Cheng
The Bayesian mixture of factor analyzers (BMFA), which achieves joint clustering and dimensionality reduction, is with an appealing feature of automatic hyper-parameter learning. In addition to its great success in various unsupervised learning tasks, it exemplifies how the Bayesian statistics can be leveraged to achieve automatic hyper-parameter learning, which is an open problem of modern simultaneous (deep) dimensionality reduction and clustering. Due to the importance of the BMFA, in this paper, its mechanism is carefully investigated, and a robust variant of the BMFA that can mitigate potential outliers is further proposed. Numerical studies are presented to show the remarkable performance of the proposed algorithm in terms of accuracy and robustness.
{"title":"Mitigating Outliers for Bayesian Mixture of Factor Analyzers","authors":"Zhongtao Chen, Lei Cheng","doi":"10.1109/SAM48682.2020.9104356","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104356","url":null,"abstract":"The Bayesian mixture of factor analyzers (BMFA), which achieves joint clustering and dimensionality reduction, is with an appealing feature of automatic hyper-parameter learning. In addition to its great success in various unsupervised learning tasks, it exemplifies how the Bayesian statistics can be leveraged to achieve automatic hyper-parameter learning, which is an open problem of modern simultaneous (deep) dimensionality reduction and clustering. Due to the importance of the BMFA, in this paper, its mechanism is carefully investigated, and a robust variant of the BMFA that can mitigate potential outliers is further proposed. Numerical studies are presented to show the remarkable performance of the proposed algorithm in terms of accuracy and robustness.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"44 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":"91325074","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/SAM48682.2020.9104268
Y. Yang, Ziping Zhao
The reduced-rank regression (RRR) model is widely used in data analytics where the response variables are believed to depend on a few linear combinations of the predictor variables, or when such linear combinations are of special interest. In this paper, we will address the RRR model estimation problem by considering two targets which are popular especially in big data applications: i) the estimation should be robust to heavytailed data distribution or outliers; ii) the estimation should be amenable to large-scale data sets or data streams. In this paper, we address the robustness via the robust maximum likelihood estimation procedure based on Cauchy distribution and a stochastic estimation procedure is further adopted to deal with the large-scale data sets. An efficient algorithm leveraging on the stochastic majorization minimization method is proposed for problem-solving. The proposed model and algorithm is validated numerically by comparing with the state-of-the-art methods.
{"title":"Online Robust Reduced-Rank Regression","authors":"Y. Yang, Ziping Zhao","doi":"10.1109/SAM48682.2020.9104268","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104268","url":null,"abstract":"The reduced-rank regression (RRR) model is widely used in data analytics where the response variables are believed to depend on a few linear combinations of the predictor variables, or when such linear combinations are of special interest. In this paper, we will address the RRR model estimation problem by considering two targets which are popular especially in big data applications: i) the estimation should be robust to heavytailed data distribution or outliers; ii) the estimation should be amenable to large-scale data sets or data streams. In this paper, we address the robustness via the robust maximum likelihood estimation procedure based on Cauchy distribution and a stochastic estimation procedure is further adopted to deal with the large-scale data sets. An efficient algorithm leveraging on the stochastic majorization minimization method is proposed for problem-solving. The proposed model and algorithm is validated numerically by comparing with the state-of-the-art methods.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"11 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":"72947605","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/SAM48682.2020.9104369
Xin He, Lei Huang
This paper proposes a robust coexistence design of MIMO radar and MIMO communication under model uncertainty. The radar waveform and the precoder of the communication system are jointly designed to minimize the total transmit power of the radar system and the communication system, while the effective signal-to-interference-noise-ratios (SINR) of the radar system and the SINR of the communication system are guaranteed with small outage probability. Simulation results show that the proposed robust coexistence system is robust against model uncertainty with the cost of using extra transmit power.
{"title":"Robust Coexistence Design of MIMO Radar and MIMO Communication under Model Uncertainty","authors":"Xin He, Lei Huang","doi":"10.1109/SAM48682.2020.9104369","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104369","url":null,"abstract":"This paper proposes a robust coexistence design of MIMO radar and MIMO communication under model uncertainty. The radar waveform and the precoder of the communication system are jointly designed to minimize the total transmit power of the radar system and the communication system, while the effective signal-to-interference-noise-ratios (SINR) of the radar system and the SINR of the communication system are guaranteed with small outage probability. Simulation results show that the proposed robust coexistence system is robust against model uncertainty with the cost of using extra transmit power.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"86 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":"84762777","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/SAM48682.2020.9104347
E. Aboutanios, A. Hassanien
Direction of Arrival (DOA) estimation algorithms generally assume knowledge of the number of sources. This crucial parameter is either determined by the problem or estimated from the available observations prior to the application of the DOA estimators. Model order estimation (MOE) strategies via information theoretic criteria such as the Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Hannan-Quinn Criterion (HQC), are usually implemented using the singular value decomposition (SVD) which is computationally expensive. In this work, we incorporate the information theoretic criteria directly into the recently proposed Fast Iterative Interpolation Beamformer (FIIB), thus avoiding the SVD. We derive the expressions for the likelihood function as well as the penalty parameters of the three criteria in terms of the number of sources. The resulting FIIB with MOE algorithm is then able to at once determine the number of sources and estimate their parameters. Simulation results demonstrate that the FIIB-based MOE outperforms the SVD-based MOE. Furthermore the FIIB with MDL achieves a performance that is very close to the original FIIB algorithm.
{"title":"Low-cost Beamforming-based DOA Estimation with Model Order Determination","authors":"E. Aboutanios, A. Hassanien","doi":"10.1109/SAM48682.2020.9104347","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104347","url":null,"abstract":"Direction of Arrival (DOA) estimation algorithms generally assume knowledge of the number of sources. This crucial parameter is either determined by the problem or estimated from the available observations prior to the application of the DOA estimators. Model order estimation (MOE) strategies via information theoretic criteria such as the Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Hannan-Quinn Criterion (HQC), are usually implemented using the singular value decomposition (SVD) which is computationally expensive. In this work, we incorporate the information theoretic criteria directly into the recently proposed Fast Iterative Interpolation Beamformer (FIIB), thus avoiding the SVD. We derive the expressions for the likelihood function as well as the penalty parameters of the three criteria in terms of the number of sources. The resulting FIIB with MOE algorithm is then able to at once determine the number of sources and estimate their parameters. Simulation results demonstrate that the FIIB-based MOE outperforms the SVD-based MOE. Furthermore the FIIB with MDL achieves a performance that is very close to the original FIIB algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"97 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":"84816883","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/SAM48682.2020.9104387
Ziyang Cheng, B. Liao, Zishu He
This paper investigates the problem of hybrid transceiver design for dual-functional radar-communication (DFRC) system. Specifically, we introduce an information embedding scheme for the DFRC system with a subarray structure. The hybrid transmit/receive beamformers are designed by maximizing sum-rate under constraints of power and similarity between the designed bemaformer and the reference one with good beampattern property. Since the formulated problem is difficult to tackle, we propose an alternating optimization method based on the alternating direction method of multipliers (ADMM) framework to obtain the hybrid beamformer. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.
{"title":"Hybrid Transceiver Design for Dual-Functional Radar-Communication System","authors":"Ziyang Cheng, B. Liao, Zishu He","doi":"10.1109/SAM48682.2020.9104387","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104387","url":null,"abstract":"This paper investigates the problem of hybrid transceiver design for dual-functional radar-communication (DFRC) system. Specifically, we introduce an information embedding scheme for the DFRC system with a subarray structure. The hybrid transmit/receive beamformers are designed by maximizing sum-rate under constraints of power and similarity between the designed bemaformer and the reference one with good beampattern property. Since the formulated problem is difficult to tackle, we propose an alternating optimization method based on the alternating direction method of multipliers (ADMM) framework to obtain the hybrid beamformer. Numerical simulations are provided to demonstrate the effectiveness of the proposed schemes.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"152 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":"84817407","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/SAM48682.2020.9104326
Junhui Liang, Jin He, Wenxian Yu
In this paper, we study the problem of parametric channel estimation for multi-user 3D millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Modeling the channel by a finite number of multipath rays, we propose a new method to jointly estimate the elevation-azimuth angle and path delay parameters for a desired user. In the proposed method, a new version of subcarrier smoothing (SCS) is developed to construct a full rank correlation matrix. Then the 3D parameters are estimated by using the ESPRIT algorithm. Finally, simulation results are finally presented to verify the efficacy of the proposed algorithm.
{"title":"3D Parametric Channel Estimation for Multi-User Massive-MIMO OFDM Systems","authors":"Junhui Liang, Jin He, Wenxian Yu","doi":"10.1109/SAM48682.2020.9104326","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104326","url":null,"abstract":"In this paper, we study the problem of parametric channel estimation for multi-user 3D millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Modeling the channel by a finite number of multipath rays, we propose a new method to jointly estimate the elevation-azimuth angle and path delay parameters for a desired user. In the proposed method, a new version of subcarrier smoothing (SCS) is developed to construct a full rank correlation matrix. Then the 3D parameters are estimated by using the ESPRIT algorithm. Finally, simulation results are finally presented to verify the efficacy of the proposed algorithm.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"2 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":"85185310","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/SAM48682.2020.9104370
Chun-Lin Liu
Sparse arrays have received attention in array signal processing since they can resolve $mathcal{O}left( {{N^2}} right)$ uncorrelated sources using N physical sensors. The reason is that the difference coarray, which consists of the differences between sensor locations, has a central uniform linear array (ULA) segment of size $mathcal{O}left( {{N^2}} right)$. From the theory of the k-essentialness property and the k-fragility, the difference coarrays of some sparse arrays are not robust to sensor failures, possibly affecting the applicability of coarray-based direction-of-arrival (DOA) estimators. However, the k-essentialness property might not fully reflect the conditions under which these estimators fail. This paper proposes a framework for the robustness of array geometries based on the importance function and the generalized k-fragility. The importance function characterizes the importance of the subarrays in an array subject to some defining properties. The importance function is also compatible with the k-essentialness property and the size of the central ULA segment in the difference coarray. The latter is closely related to the performance of some coarray-based DOA estimators. Based on the importance function, the generalized k-fragility is proposed to quantify the robustness of an array. Properties of the importance function and the generalized k-fragility are also studied and demonstrated through numerical examples.
{"title":"A General Framework for the Robustness of Structured Difference Coarrays to Element Failures","authors":"Chun-Lin Liu","doi":"10.1109/SAM48682.2020.9104370","DOIUrl":"https://doi.org/10.1109/SAM48682.2020.9104370","url":null,"abstract":"Sparse arrays have received attention in array signal processing since they can resolve $mathcal{O}left( {{N^2}} right)$ uncorrelated sources using N physical sensors. The reason is that the difference coarray, which consists of the differences between sensor locations, has a central uniform linear array (ULA) segment of size $mathcal{O}left( {{N^2}} right)$. From the theory of the k-essentialness property and the k-fragility, the difference coarrays of some sparse arrays are not robust to sensor failures, possibly affecting the applicability of coarray-based direction-of-arrival (DOA) estimators. However, the k-essentialness property might not fully reflect the conditions under which these estimators fail. This paper proposes a framework for the robustness of array geometries based on the importance function and the generalized k-fragility. The importance function characterizes the importance of the subarrays in an array subject to some defining properties. The importance function is also compatible with the k-essentialness property and the size of the central ULA segment in the difference coarray. The latter is closely related to the performance of some coarray-based DOA estimators. Based on the importance function, the generalized k-fragility is proposed to quantify the robustness of an array. Properties of the importance function and the generalized k-fragility are also studied and demonstrated through numerical examples.","PeriodicalId":6753,"journal":{"name":"2020 IEEE 11th Sensor Array and Multichannel Signal Processing Workshop (SAM)","volume":"191 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":"79582304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：