Pub Date : 2008-07-21DOI: 10.1109/SAM.2008.4606828
Ming Sun, K. C. Ho
This paper investigates the use of energy measurements to improve the localization accuracy in terms of range and bearing in a TDOA based system. We first extend the hybrid energy and TDOA localization algorithm in Cartesian coordinate that is proposed in (Ho and Sun, 2008) to obtain the range and bearing estimates of an emitting source. The accuracy of the range and bearing estimates is then examined with respect to the noise level in the energy measurements relative to that in TDOAs. For comparison purpose, the CRLB for the range and bearing estimates is also derived. We find that energy measurements improve more the accuracy in range than in bearing. Regarding the bearing estimation, the energy measurements provide more improvement for a near-field source than for a far-field source.
本文研究了在基于TDOA的系统中，利用能量测量来提高距离和方位的定位精度。我们首先扩展了(Ho and Sun, 2008)中提出的笛卡尔坐标下的混合能量和TDOA定位算法，以获得发射源的距离和方位估计。然后根据相对于tdoa的能量测量噪声水平来检查距离和方位估计的准确性。为了比较，还推导了距离和方位估计的CRLB。我们发现能量测量在距离上比在方位上更能提高精度。在方位估计方面，能量测量对近场源比远场源提供了更多的改进。
{"title":"The effect of energy measurements on improving the range and bearing estimation in a hybrid energy and TDOA localization system","authors":"Ming Sun, K. C. Ho","doi":"10.1109/SAM.2008.4606828","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606828","url":null,"abstract":"This paper investigates the use of energy measurements to improve the localization accuracy in terms of range and bearing in a TDOA based system. We first extend the hybrid energy and TDOA localization algorithm in Cartesian coordinate that is proposed in (Ho and Sun, 2008) to obtain the range and bearing estimates of an emitting source. The accuracy of the range and bearing estimates is then examined with respect to the noise level in the energy measurements relative to that in TDOAs. For comparison purpose, the CRLB for the range and bearing estimates is also derived. We find that energy measurements improve more the accuracy in range than in bearing. Regarding the bearing estimation, the energy measurements provide more improvement for a near-field source than for a far-field source.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127156767","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606830
Pei-Jung Chung
We study the performance of a recently proposed robust ML estimation procedure for unknown numbers of signals. This approach finds the ML estimate for the maximum number of signals and selects relevant components associated with the true parameters from the estimated parameter vector. Its computational cost is significantly lower than conventional methods based on information theoretic criteria or multiple hypothesis tests. We show that the covariance matrix of relevant estimates is upper and lower bounded by two covariance matrices. These bounds are easy to compute by existing results for standard ML estimation. Our analysis is further confirmed by numerical experiments over a wide range of SNRs.
{"title":"Robust ML estimation for unknown numbers of signals: Performance study","authors":"Pei-Jung Chung","doi":"10.1109/SAM.2008.4606830","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606830","url":null,"abstract":"We study the performance of a recently proposed robust ML estimation procedure for unknown numbers of signals. This approach finds the ML estimate for the maximum number of signals and selects relevant components associated with the true parameters from the estimated parameter vector. Its computational cost is significantly lower than conventional methods based on information theoretic criteria or multiple hypothesis tests. We show that the covariance matrix of relevant estimates is upper and lower bounded by two covariance matrices. These bounds are easy to compute by existing results for standard ML estimation. Our analysis is further confirmed by numerical experiments over a wide range of SNRs.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129543791","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606864
Dinh-Quy Nguyen, W. Gan, Y. Chong
The DORT (decomposition de lpsilaoperateur de retournement temporel) method is an efficient technique to focus signal on the target. The DORT method requires the measurement of the inter-element impulse responses of the propagating medium for all pairs of elements in the array. The time reversal operator (TRO) is deduced and diagonalized from these responses. But DORT method has worse results in non-ideally resolved scatterer case that two scatterers are placed close with each other. Therefore, in this paper, weighted least-squares (WLS) combining with the DORT method are proposed to perform selective focusing in non-ideally resolved scatterer case. This technique can also perform target focusing with higher spatial resolution than original DORT method and faster computation than adaptive beamforming method.
DORT (decomposition de lpsilaoperateur de retourporel)方法是一种有效的将信号聚焦到目标上的方法。DORT方法要求测量阵列中所有元对的传播介质的元间脉冲响应。从这些响应中推导并对角化了时间反转算子。但对于非理想分辨散射体，当两个散射体放置得很近时，DORT方法的效果较差。因此，本文提出了加权最小二乘(WLS)与DORT方法相结合，在非理想分辨散射体情况下进行选择性聚焦。该方法可以实现比原DORT方法更高的空间分辨率和比自适应波束形成方法更快的计算速度的目标聚焦。
{"title":"Weighted least squares DORT method in selective focusing","authors":"Dinh-Quy Nguyen, W. Gan, Y. Chong","doi":"10.1109/SAM.2008.4606864","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606864","url":null,"abstract":"The DORT (decomposition de lpsilaoperateur de retournement temporel) method is an efficient technique to focus signal on the target. The DORT method requires the measurement of the inter-element impulse responses of the propagating medium for all pairs of elements in the array. The time reversal operator (TRO) is deduced and diagonalized from these responses. But DORT method has worse results in non-ideally resolved scatterer case that two scatterers are placed close with each other. Therefore, in this paper, weighted least-squares (WLS) combining with the DORT method are proposed to perform selective focusing in non-ideally resolved scatterer case. This technique can also perform target focusing with higher spatial resolution than original DORT method and faster computation than adaptive beamforming method.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128431560","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606829
S. Blunt, Tszping Chan, Karl Gerlach
A new approach for spatial direction-of-arrival (DOA) estimation is developed based on the minimum mean-square error (MMSE) framework. Unlike many traditional DOA estimators, the MMSE approach, denoted as Re-Iterative Super-Resolution (RISR), does not employ spatial sample covariance information which may significantly degrade DOA estimation if spatially-separated sources are temporally correlated. Instead, RISR is a recursive algorithm that relies on a structured signal covariance matrix comprised of the set of possible spatial steering vectors each weighted by an associated power estimate from the previous iteration. Furthermore, RISR can naturally accommodate prior information on spatially colored noise, does not require knowledge of the number of sources, and can also exploit multiple time samples in a non-coherent manner to improve performance. For low to moderate time sample support, RISR is demonstrated to provide super-resolution performance superior to MUSIC and spatially-smoothed MUSIC.
{"title":"A new framework for direction-of-arrival estimation","authors":"S. Blunt, Tszping Chan, Karl Gerlach","doi":"10.1109/SAM.2008.4606829","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606829","url":null,"abstract":"A new approach for spatial direction-of-arrival (DOA) estimation is developed based on the minimum mean-square error (MMSE) framework. Unlike many traditional DOA estimators, the MMSE approach, denoted as Re-Iterative Super-Resolution (RISR), does not employ spatial sample covariance information which may significantly degrade DOA estimation if spatially-separated sources are temporally correlated. Instead, RISR is a recursive algorithm that relies on a structured signal covariance matrix comprised of the set of possible spatial steering vectors each weighted by an associated power estimate from the previous iteration. Furthermore, RISR can naturally accommodate prior information on spatially colored noise, does not require knowledge of the number of sources, and can also exploit multiple time samples in a non-coherent manner to improve performance. For low to moderate time sample support, RISR is demonstrated to provide super-resolution performance superior to MUSIC and spatially-smoothed MUSIC.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129225321","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606877
L. Vicente, K. C. Ho
The partial adaptive concentric ring array (CRA) has been successfully applied to 3D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In some cases, prior knowledge regarding some interferences is available so that better beamformers can be designed. The previous method that exploits prior knowledge by using a fixed penalty factor could not guarantee in maintaining a low residual interference and noise level. We propose in this paper an adaptive beamformer that automatically seeks out the optimum penalty factor to achieve the best performance. The proposed beamformer outperforms the previous design in maintaining a higher output signal to interference and noise ratio, even after the characteristics of the interferences have changed. The performance of the proposed beamformer is evaluated through simulations.
{"title":"Optimizing the performance of the partial adaptive concentric ring array in the presence of prior knowledge","authors":"L. Vicente, K. C. Ho","doi":"10.1109/SAM.2008.4606877","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606877","url":null,"abstract":"The partial adaptive concentric ring array (CRA) has been successfully applied to 3D beamforming because of its flexibility, faster tracking ability and reduced computation with respect to the fully adaptive CRA. In some cases, prior knowledge regarding some interferences is available so that better beamformers can be designed. The previous method that exploits prior knowledge by using a fixed penalty factor could not guarantee in maintaining a low residual interference and noise level. We propose in this paper an adaptive beamformer that automatically seeks out the optimum penalty factor to achieve the best performance. The proposed beamformer outperforms the previous design in maintaining a higher output signal to interference and noise ratio, even after the characteristics of the interferences have changed. The performance of the proposed beamformer is evaluated through simulations.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115816767","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606879
Biao Jiang
In this paper, an efficient low-complexity robust adaptive beamforming method based on worst-case performance optimization is proposed. Lagrangian method was applied to obtain the expression for the robust adaptive weight vector, which is optimized on the boundary of the steering vector uncertainty region, that is to say, in the worst mismatch case. Combining the constraint condition and the eigendecomposition of the array covariance matrix, root-finding method is used to obtain the optimal Lagrange multiplier. Then, the diagonal loading-like robust weight vector is achieved. The implementation efficiency is greatly improved since the main computational burden is the eigendecomposition operator. Numerical results show that the performance of the proposed method is nearly identical to the robust Capon beamforming.
{"title":"Low-complexity implementation for worst-case optimization-based robust adaptive beamforming","authors":"Biao Jiang","doi":"10.1109/SAM.2008.4606879","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606879","url":null,"abstract":"In this paper, an efficient low-complexity robust adaptive beamforming method based on worst-case performance optimization is proposed. Lagrangian method was applied to obtain the expression for the robust adaptive weight vector, which is optimized on the boundary of the steering vector uncertainty region, that is to say, in the worst mismatch case. Combining the constraint condition and the eigendecomposition of the array covariance matrix, root-finding method is used to obtain the optimal Lagrange multiplier. Then, the diagonal loading-like robust weight vector is achieved. The implementation efficiency is greatly improved since the main computational burden is the eigendecomposition operator. Numerical results show that the performance of the proposed method is nearly identical to the robust Capon beamforming.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127840544","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606914
F. Schulz
Synthetic aperture radar (SAR) as a method for ground imaging by using a single antenna or a sensor array has widely found attraction for remote sensing applications and reconnaissance tasks. Independent of the particular sensor but inherent to the SAR processing, moving objects in the observed scene will be imaged at wrong positions and can appear in a smeared fashion. To avoid these disturbing artifacts in the image, a joint spatial-spectral filtering approach is proposed in this paper that allows to suppress signal contributions from moving targets in multi-channel radar data. Results obtained with experimental data from an airborne system show the potential for a practical application of the presented method.
{"title":"Post-doppler space-time filtering for suppressing moving target signals in multi-channel SAR data","authors":"F. Schulz","doi":"10.1109/SAM.2008.4606914","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606914","url":null,"abstract":"Synthetic aperture radar (SAR) as a method for ground imaging by using a single antenna or a sensor array has widely found attraction for remote sensing applications and reconnaissance tasks. Independent of the particular sensor but inherent to the SAR processing, moving objects in the observed scene will be imaged at wrong positions and can appear in a smeared fashion. To avoid these disturbing artifacts in the image, a joint spatial-spectral filtering approach is proposed in this paper that allows to suppress signal contributions from moving targets in multi-channel radar data. Results obtained with experimental data from an airborne system show the potential for a practical application of the presented method.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128069520","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606891
Jae-Mo Yang, Min-Seok Choi, Hong-Goo Kang
This paper proposes a robust two-channel frequency selective multiple signal classification (MUSIC) method to find a direction of arrival (DoA) information of speech signal. To overcome a phase distortion caused by reverberation and background noise in real acoustic room environments, we adopt a least square (LS)-based phase estimation method. In the phase compensation stage, distorted phases are replaced by estimated phases to enhance the accuracy of covariance matrix needed for the eigen-decomposition of the MUSIC method. Simulation results verify that the proposed algorithm shows much higher estimation accuracy than conventional one while its complexity can be reduced by the frequency selection method.
{"title":"Two-channel DOA estimation usign frequency selective music algorithm with a phase compensation in reverberant room","authors":"Jae-Mo Yang, Min-Seok Choi, Hong-Goo Kang","doi":"10.1109/SAM.2008.4606891","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606891","url":null,"abstract":"This paper proposes a robust two-channel frequency selective multiple signal classification (MUSIC) method to find a direction of arrival (DoA) information of speech signal. To overcome a phase distortion caused by reverberation and background noise in real acoustic room environments, we adopt a least square (LS)-based phase estimation method. In the phase compensation stage, distorted phases are replaced by estimated phases to enhance the accuracy of covariance matrix needed for the eigen-decomposition of the MUSIC method. Simulation results verify that the proposed algorithm shows much higher estimation accuracy than conventional one while its complexity can be reduced by the frequency selection method.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125581479","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606912
Y. Abramovich, B.A. Johnson, N. Spencer
In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter co- variance matrix. In the reduced-order parametric matched filter STAP technique, this covariance matrix is reconstructed from a small number of estimated parameters, resulting in a much more efficient use of training samples. This paper explores a computationally advantageous "relaxed" maximum entropy (Burg) reconstruction technique which does not restore a strict Toeplitz-block structure, but does preserve the Burg spectrum. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset and compared with "proper" Toeplitz-block reconstruction.
{"title":"Multivariate spectral reconstruction of STAP covariance matrices: Hermitian “relaxation” and performance analysis","authors":"Y. Abramovich, B.A. Johnson, N. Spencer","doi":"10.1109/SAM.2008.4606912","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606912","url":null,"abstract":"In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter co- variance matrix. In the reduced-order parametric matched filter STAP technique, this covariance matrix is reconstructed from a small number of estimated parameters, resulting in a much more efficient use of training samples. This paper explores a computationally advantageous \"relaxed\" maximum entropy (Burg) reconstruction technique which does not restore a strict Toeplitz-block structure, but does preserve the Burg spectrum. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset and compared with \"proper\" Toeplitz-block reconstruction.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687482","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 : 2008-07-21DOI: 10.1109/SAM.2008.4606888
J. Dmochowski, J. Benesty, S. Affes
The localization of speech is essential for improving the quality of hands-free pick-up as well as for applications such as automatic camera steering. This paper proposes a source localization method tailored to the distinct nature of speech that is based on the linearly constrained minimum variance (LCMV) beamforming method. The LCMV steered beam temporally focuses the array onto the desired signal. By modeling the desired signal as an autoregressive (AR) process and embedding the AR coefficients in the linear constraints, the localization accuracy is significantly improved as compared to existing techniques.
{"title":"On the use of autoregressive modeling for localization of speech","authors":"J. Dmochowski, J. Benesty, S. Affes","doi":"10.1109/SAM.2008.4606888","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606888","url":null,"abstract":"The localization of speech is essential for improving the quality of hands-free pick-up as well as for applications such as automatic camera steering. This paper proposes a source localization method tailored to the distinct nature of speech that is based on the linearly constrained minimum variance (LCMV) beamforming method. The LCMV steered beam temporally focuses the array onto the desired signal. By modeling the desired signal as an autoregressive (AR) process and embedding the AR coefficients in the linear constraints, the localization accuracy is significantly improved as compared to existing techniques.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130176950","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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