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.2008.4606912
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.2008.4606915
A. J. Poulsen, R. Nadakuditi, A. Baggeroer
We present analytical results which quantify the effect of system mismatch and finite sample support on acoustic vector sensor array performance. One noteworthy result is that the vector aspect of the array ldquodampensrdquo the effect of array mismatch, enabling deeper true nulls. This is accomplished because the variance of the vector sensor array spatial response (due to rotational, positional and filter gain/phase perturbations) decreases in the sidelobes, unlike arrays of omnidirectional hydrophones. When sensor orientation is measured within a reasonable tolerance, the beampattern variance dominates the average sidelobe power response. Our analysis also suggests that vector sensor array gain performance is less sensitive to rotational than to positional perturbations in the regions of interest. We analytically characterize the eigen-SNR threshold, which depends on the signal and noise covariance and the number of noise-only and signal-plus-noise snapshots, below which (asymptotically speaking) reliable detection using sample eigenvalue based techniques is not possible. Thus for a given number of snapshots, since the dimensionality of the snapshot in a vector sensor array is larger than that of a hydrophone-only array, the eigen-SNR detection threshold will be greater whenever the eigenvector information is discarded. We present processing techniques customized to the unique characteristics of vector sensors, which exploit information encoded in the sample eigenvectors and are robust to the mismatch and finite sample support issues. These methods include adaptive processing techniques with multiple white noise constraints.
{"title":"Robust adaptive vector sensor processing in the presence of mismatch and finite sample support","authors":"A. J. Poulsen, R. Nadakuditi, A. Baggeroer","doi":"10.1109/SAM.2008.4606915","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606915","url":null,"abstract":"We present analytical results which quantify the effect of system mismatch and finite sample support on acoustic vector sensor array performance. One noteworthy result is that the vector aspect of the array ldquodampensrdquo the effect of array mismatch, enabling deeper true nulls. This is accomplished because the variance of the vector sensor array spatial response (due to rotational, positional and filter gain/phase perturbations) decreases in the sidelobes, unlike arrays of omnidirectional hydrophones. When sensor orientation is measured within a reasonable tolerance, the beampattern variance dominates the average sidelobe power response. Our analysis also suggests that vector sensor array gain performance is less sensitive to rotational than to positional perturbations in the regions of interest. We analytically characterize the eigen-SNR threshold, which depends on the signal and noise covariance and the number of noise-only and signal-plus-noise snapshots, below which (asymptotically speaking) reliable detection using sample eigenvalue based techniques is not possible. Thus for a given number of snapshots, since the dimensionality of the snapshot in a vector sensor array is larger than that of a hydrophone-only array, the eigen-SNR detection threshold will be greater whenever the eigenvector information is discarded. We present processing techniques customized to the unique characteristics of vector sensors, which exploit information encoded in the sample eigenvectors and are robust to the mismatch and finite sample support issues. These methods include adaptive processing techniques with multiple white noise constraints.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"15 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":"123292427","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}
Conventional blind beamformers in DS-CDMA systems generally suffer from either unreliable convergence rate or signal cancellation in practical signal environment. In this paper, we propose a novel orthogonal projection scheme to extract spatial interference structure for the blind beamformer, which requires neither the direction of the desired signal nor the array geometry information. Exploiting more signal-free interference samples makes the convergence of the beamformer fast and independent of the desired signal strength. Simulation results demonstrate its better interference suppression ability than the pre- and post- correlation (PAPC) scheme and the filter pair (FP) scheme. Furthermore, the proposed recursive algorithm can null the newly entering interferers within a few symbols, making it suitable for dynamic multiple access channels.
{"title":"An orthogonal projection based blind beamformer for DS-CDMA systems","authors":"Jianshu Chen, Jian Wang, Pengyu Zhang, Jian Yuan, X. Shan","doi":"10.1109/SAM.2008.4606819","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606819","url":null,"abstract":"Conventional blind beamformers in DS-CDMA systems generally suffer from either unreliable convergence rate or signal cancellation in practical signal environment. In this paper, we propose a novel orthogonal projection scheme to extract spatial interference structure for the blind beamformer, which requires neither the direction of the desired signal nor the array geometry information. Exploiting more signal-free interference samples makes the convergence of the beamformer fast and independent of the desired signal strength. Simulation results demonstrate its better interference suppression ability than the pre- and post- correlation (PAPC) scheme and the filter pair (FP) scheme. Furthermore, the proposed recursive algorithm can null the newly entering interferers within a few symbols, making it suitable for dynamic multiple access channels.","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":"116494819","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.2008.4606849
V. Cevher, Richard Baraniuk
We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an lscr1-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.
{"title":"Compressive sensing for sensor calibration","authors":"V. Cevher, Richard Baraniuk","doi":"10.1109/SAM.2008.4606849","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606849","url":null,"abstract":"We consider a calibration problem, where we determine an unknown sensor location using the known track of a calibration target and a known reference sensor location. We cast the calibration problem as a sparse approximation problem where the unknown sensor location is determined over a discrete spatial grid with respect to the reference sensor. To achieve the calibration objective, low dimensional random projections of the sensor data are passed to the reference sensor, which significantly reduces the inter-sensor communication bandwidth. The unknown sensor location is then determined by solving an lscr1-norm minimization problem (linear program). Field data results are provided to demonstrate the effectiveness of the approach.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"4 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":"124735292","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.2008.4606836
Tingting Liu, Jian-Kang Zhang, K. M. Wong
For precoder design problems in a multi-input multi-output (MIMO) communication system, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the optimum design of a precoder for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full CSI is known at the receiver, but only the first- and second-order statistics of the channels are available at the transmitter. Our goal is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing decision feedback (ZF-DF) detection subject to a constraint on the total transmitting power. We transform this non-convex optimization problem into a convex geometrical programming problem, which can then be efficiently solved using an interior point method. For the case when the transmission channels are uncorrelated, a closed-form solution of the optimum precoder has been obtained. The superior performance of our MIMO system equipped with the optimum precoder is verified by computer simulations.
{"title":"Optimal precoder design for mimo systems using decision feedback receivers","authors":"Tingting Liu, Jian-Kang Zhang, K. M. Wong","doi":"10.1109/SAM.2008.4606836","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606836","url":null,"abstract":"For precoder design problems in a multi-input multi-output (MIMO) communication system, perfect knowledge of the channel state information (CSI) at both the transmitter and the receiver is usually required. However, it is often difficult to provide sufficiently timely and accurate feedback of CSI from the receiver to the transmitter for such designs to be practically viable. In this paper, we consider the optimum design of a precoder for a wireless communication link having M transmitter antennas and N receiver antennas (M < N), in which the channels are assumed to be flat fading and may be correlated. We assume that full CSI is known at the receiver, but only the first- and second-order statistics of the channels are available at the transmitter. Our goal is to come up with an efficient design of the optimal precoder for such a MIMO system by minimizing the average arithmetic mean-squared error (MSE) of zero-forcing decision feedback (ZF-DF) detection subject to a constraint on the total transmitting power. We transform this non-convex optimization problem into a convex geometrical programming problem, which can then be efficiently solved using an interior point method. For the case when the transmission channels are uncorrelated, a closed-form solution of the optimum precoder has been obtained. The superior performance of our MIMO system equipped with the optimum precoder is verified by computer simulations.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"4 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":"123968495","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.2008.4606910
M. Alaee, H. Amindavar
In this paper, q-chirplet based signal processing is applied to data from a low-resolution ground surveillance pulse Doppler RADAR, to classify three classes of targets: personnel, wheeled vehicles and animals. We utilize Zernike moments (ZM) over the chirplet parameters to determine the pertinent features. Our work provides a new approach for multiresolution analysis and classification of non-stationary signals with the objective of revealing important features in an unknown noise and clutter environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class. The results show the proposed algorithm invariancy against speed and orientation of the targets.
{"title":"Chirplet-based target recognition using RADAR technology","authors":"M. Alaee, H. Amindavar","doi":"10.1109/SAM.2008.4606910","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606910","url":null,"abstract":"In this paper, q-chirplet based signal processing is applied to data from a low-resolution ground surveillance pulse Doppler RADAR, to classify three classes of targets: personnel, wheeled vehicles and animals. We utilize Zernike moments (ZM) over the chirplet parameters to determine the pertinent features. Our work provides a new approach for multiresolution analysis and classification of non-stationary signals with the objective of revealing important features in an unknown noise and clutter environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class. The results show the proposed algorithm invariancy against speed and orientation of the targets.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"10 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":"121175930","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.2008.4606822
J. Lopez Vicario, A. Morell, A. Bel, G. Seco-Granados
In this paper, we study the impact of outdated channel state information (CSI) on a cooperative system based on opportunistic relay selection (ORS). The study is carried out by obtaining an analytical expression for the outage probability, defined as the probability that the instantaneous mutual information is lower than a target rate. Besides, we propose the optimal power allocation aimed at minimizing the outage probability when the available CSI is subject to impairments. As shown in the paper, the proposed strategy provides significant gains when compared with uniform power allocation.
{"title":"Optimal power allocation in opportunistic relaying with outdated CSI","authors":"J. Lopez Vicario, A. Morell, A. Bel, G. Seco-Granados","doi":"10.1109/SAM.2008.4606822","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606822","url":null,"abstract":"In this paper, we study the impact of outdated channel state information (CSI) on a cooperative system based on opportunistic relay selection (ORS). The study is carried out by obtaining an analytical expression for the outage probability, defined as the probability that the instantaneous mutual information is lower than a target rate. Besides, we propose the optimal power allocation aimed at minimizing the outage probability when the available CSI is subject to impairments. As shown in the paper, the proposed strategy provides significant gains when compared with uniform power allocation.","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":"116687875","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.2008.4606820
J. Choi, B. Shim, A. Singer, N. Cho
In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.
{"title":"A low-complexity near-ML decoding technique via reduced dimension list stack algorithm","authors":"J. Choi, B. Shim, A. Singer, N. Cho","doi":"10.1109/SAM.2008.4606820","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606820","url":null,"abstract":"In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"15 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":"129284482","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.2008.4606861
Y. Abramovich, B.A. Johnson, N. Spencer
In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter covariance 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 and a companion one [1] addresses the issue of STAP filter performance from covariance matrices reconstructed with a strict adherence to the Toeplitz-block structure versus a ldquorelaxedrdquo reconstruction which employs a maximum entropy completion criteria, but does not enforce a strict Toeplitz-block structure on that completion. Both techniques analyzed use a multivariate spectral reconstruction approach which preserve the Burg spectrum. In this paper, the reconstruction is constrained to result in a Toeplitz-block covariance matrix model, and the solution requires positive definite matrix-valued stable polynomial factorization that can be derived via the multivariate Levinson algorithm. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset in the companion paper.
{"title":"Multivariate spectral reconstruction of stap covariance matrices: Toeplitz-block solution","authors":"Y. Abramovich, B.A. Johnson, N. Spencer","doi":"10.1109/SAM.2008.4606861","DOIUrl":"https://doi.org/10.1109/SAM.2008.4606861","url":null,"abstract":"In space-time adaptive processing (STAP) applications, temporally stationary clutter results in a Toeplitz-block clutter covariance 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 and a companion one [1] addresses the issue of STAP filter performance from covariance matrices reconstructed with a strict adherence to the Toeplitz-block structure versus a ldquorelaxedrdquo reconstruction which employs a maximum entropy completion criteria, but does not enforce a strict Toeplitz-block structure on that completion. Both techniques analyzed use a multivariate spectral reconstruction approach which preserve the Burg spectrum. In this paper, the reconstruction is constrained to result in a Toeplitz-block covariance matrix model, and the solution requires positive definite matrix-valued stable polynomial factorization that can be derived via the multivariate Levinson algorithm. Performance of the reconstructed covariance matrix model as a STAP filter is evaluated using the DARPA KASSPER dataset in the companion paper.","PeriodicalId":422747,"journal":{"name":"2008 5th IEEE Sensor Array and Multichannel Signal Processing Workshop","volume":"18 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":"126692836","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：