The Experts below are selected from a list of 696 Experts worldwide ranked by ideXlab platform
Michael C. Wicks - One of the best experts on this subject based on the ideXlab platform.
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Localized three-dimensional adaptive spatial-temporal processing for airborne radar
RADAR 2002, 2020Co-Authors: T B Hale, Michael A. Temple, John F. Raquet, Mark E. Oxley, Michael C. WicksAbstract:Radar space-time adaptive processing (STAP) techniques have classically focused on azimuth-Doppler adaptivity while placing minimal emphasis on elevation. Elevation adaptivity offers significant clutter suppression improvement, allowing further suppression of interference sources having identical Doppler and azimuth as the expected target. This work incorporates elevation adaptivity using two approaches: (1) a factored approach and (2) a Joint Domain approach, both greatly improving clutter suppression performance. The proposed concepts are validated using results based on simulated range ambiguous airborne radar data. Target detection improvements on the order of 8 dB and 10 dB (as compared to standard 2D-JDL processing) are demonstrated for the factored and Joint Domain approaches, respectively, using an 8 /spl times/ 8 non-uniform rectangular array.
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Accounting for array effects in Joint-Domain localized STAP processing
RADAR 2002, 2020Co-Authors: Braham Himed, Michael C. Wicks, G.j. GenelloAbstract:We study critical issues associated with the application of multi-dimensional adaptive. filtering, including space-time adaptive processing (STAP), to real-world radar systems. In particular, transform Domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. It is shown that the selection of asymmetric auxiliary beams offers dramatic improvements in signal-to-interference ratio for targets with low Doppler.
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Pre-filtering for clutter rejection in beamspace STAP
IEEE International Radar Conference 2005., 2005Co-Authors: M. Khanpour-ardestani, Raviraj S. Adve, Michael C. WicksAbstract:Several space-time adaptive processing algorithms have been proposed to detect weak targets in the presence of strong interference, especially clutter and jamming. Except for displaced phase center array (DPCA) processing, radar signal processing algorithms ignore the fact that the location of the clutter ridge in angle-Doppler space is known, given the platform speed and direction. This paper introduces our attempt to exploit this a priori knowledge in conjunction with the Joint Domain localized processing algorithm. Using a two-dimensional filter, clutter is rejected in a first, non-adaptive stage, followed by adaptive processing in the angle-Doppler Domain.
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Localised three-dimensional adaptive spatial-temporal processing for airborne radar
IEE Proceedings - Radar Sonar and Navigation, 2003Co-Authors: T B Hale, Michael A. Temple, John F. Raquet, Mark E. Oxley, Michael C. WicksAbstract:Radar space1–time adaptive processing (STAP) techniques have classically focused on azimuth–Doppler adaptivity while placing minimal emphasis on elevation. Elevation adaptivity offers significant clutter suppression improvement, allowing further suppression of interference sources having identical Doppler and azimuth as the expected target. Work is presented which incorporates elevation adaptivity using two approaches: a factored approach and a Joint Domain approach, both greatly improving clutter suppression performance. The proposed concepts are validated using results based on simulated range ambiguous airborne radar data. Target detection improvements on the order of 8 dB and 10 dB (as compared to standard 2D-JDL processing) are demonstrated for the factored and Joint Domain approaches, respectively, using an 8×8 nonuniform rectangular array.
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A new constrained Joint-Domain localized approach for airborne radars
Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322), 2002Co-Authors: Braham Himed, Michael C. Wicks, Peter ZulchAbstract:Critical issues associated with the application of multi-dimensional adaptive filtering including space-time adaptive processing (STAP) to real-world radar systems are considered. In particular, the design of transform Domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. In particular, asymmetry in the selection of auxiliary beams and the effects of spatial tapering are shown to offer dramatic improvements in signal to interference ratio for targets with low Doppler.
Raviraj S. Adve - One of the best experts on this subject based on the ideXlab platform.
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Time-orthogonal waveform diversity and Joint Domain localised algorithm for distributed aperture radars
Iet Radar Sonar and Navigation, 2010Co-Authors: L. Landi, Raviraj S. AdveAbstract:Distributed aperture radars represent an interesting solution for target detection in strong interference environments. Distributed apertures provide improved angular resolution or are able to view a target from multiple look-angles, thereby exploiting scintillation. However, because of the large distances between array elements, both target and interfering sources are in the near field of the antenna array. Furthermore, because of the relative motion between antenna elements and interference sources, the clutter Doppler frequency is not stationary. Recent works have demonstrated the benefits of combining frequency diversity and space-time adaptive processing (STAP) for distributed aperture radars. In this study a new waveform diversity system model is developed. Using orthogonal signalling, the receivers can treat the incoming signals independently, solving several bistatic problems instead of the initial multistatic problem. The authors also apply adaptive techniques to counteract the range dependency of the clutter Doppler frequency. In particular, the authors apply the Joint Domain localised algorithm, specifically chosen because of its need for only limited secondary data.
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Reduced-Rank Adaptive Filtering Using Localized Processing for CDMA Systems
IEEE Transactions on Vehicular Technology, 2007Co-Authors: R.y.m. Wong, Raviraj S. AdveAbstract:An integrated beamforming (spatial processing) and multiuser-detection (temporal processing) scheme is an effective approach to increase system capacity but is also impractical due to the high associated computational costs. To overcome this problem, researchers have developed reduced-rank approaches. Adding to this class of algorithms, this paper introduces a Joint-Domain adaptive algorithm, which processes spatial and temporal data within a localized region after transformation to a "beamspace." This new Joint-Domain-localized (JDL) adaptive algorithm is developed for single-cell uplink code-division multiple-access (CDMA) systems with a receive-antenna array. Given a rank constraint on the JDL transformation, this paper develops the optimal choice of transformation matrix and justifies a simpler suboptimal choice. The JDL algorithm is shown to have relatively low computation load. We also introduce the JDL-Z algorithm that combines JDL processing with zero forcing for multicell uplink CDMA systems. At the cost of higher computational complexity, this new scheme provides better performance and a faster convergence rate than the JDL algorithm. However, the key contribution is a framework wherein adaptive processing can be followed by yet another stage of adaptive processing. Simulations are used to illustrate the efficacy of the two algorithms.
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Pre-filtering for clutter rejection in beamspace STAP
IEEE International Radar Conference 2005., 2005Co-Authors: M. Khanpour-ardestani, Raviraj S. Adve, Michael C. WicksAbstract:Several space-time adaptive processing algorithms have been proposed to detect weak targets in the presence of strong interference, especially clutter and jamming. Except for displaced phase center array (DPCA) processing, radar signal processing algorithms ignore the fact that the location of the clutter ridge in angle-Doppler space is known, given the platform speed and direction. This paper introduces our attempt to exploit this a priori knowledge in conjunction with the Joint Domain localized processing algorithm. Using a two-dimensional filter, clutter is rejected in a first, non-adaptive stage, followed by adaptive processing in the angle-Doppler Domain.
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GLOBECOM - Joint Domain localized adaptive processing with zero forcing for multi-cell CDMA systems
IEEE Global Telecommunications Conference 2004. GLOBECOM '04., 2004Co-Authors: R.y.m. Wong, Raviraj S. AdveAbstract:An integrated beamforming (spatial processing) and multiuser detection (temporal processing) scheme is an effective approach to increase system capacity, but is also impractical due to the high associated computational costs. The authors previously proposed Joint Domain localized (JDL) processing which achieves significantly lower computational cost and a faster convergence rate in terms of number of training symbols. The paper justifies the choice of the transformation matrix that is the basis for the JDL algorithm. Building on JDL processing, we also introduce a new processor that combines JDL processing and zero forcing for multi-cell uplink CDMA systems. Simulations show that this approach achieves better performance and a faster convergence rate than the JDL algorithm as well as the reduced rank and iterative schemes introduced by other researchers. If restricted by short training sequences, it even outperforms the theoretically optimal processor.
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Joint Domain localized adaptive processing for CDMA systems
2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514), 2004Co-Authors: R. Wong, Raviraj S. AdveAbstract:An effective approach to increase system capacity would be combining space division multiple access (SDMA) with other multiple access approaches such as code division multiple access (CDMA). However, implementing such a system requires interference cancellation to separate users' signals spatially and/or temporally. In this regard, an integrated beamforming (spatial processing) and multiuser detection (temporal processing) scheme outperforms all linear processing schemes, but is also impractical due to the high computational costs. Other researchers have proposed some more practical reduced-rank or iterative schemes. This paper introduces a Joint Domain adaptive algorithm which processes spatial and temporal data within a localized region in beamspace. This new algorithm is developed for uplink CDMA systems with multiple receive antennas. The simulations show that this approach is more effective than the reduced rank and iterative schemes in suppressing interference at significantly lower computational cost.
Frank M Candocia - One of the best experts on this subject based on the ideXlab platform.
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a least squares approach for the Joint Domain and range registration of images
International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Frank M CandociaAbstract:An approach for Jointly registering images in Domain and range using a least squares formalism is presented. This Joint approach is in contrast to almost all current registration methods that only register images with respect to their Domains and neglect accounting for exposure differences between images. The inclusion of range into the registration process is accomplished by approximating the camera's nonlinear comparametric function with a piecewise linear one. This results in a computationally attractive least squares approach to an otherwise inherently nonlinear optimization problem. The effectiveness of this approach is illustrated.
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ICASSP - A least squares approach for the Joint Domain and range registration of images
IEEE International Conference on Acoustics Speech and Signal Processing, 2002Co-Authors: Frank M CandociaAbstract:An approach for Jointly registering images in Domain and range using a least squares formalism is presented. This Joint approach is in contrast to almost all current registration methods that only register images with respect to their Domains and neglect accounting for exposure differences between images. The inclusion of range into the registration process is accomplished by approximating the camera's nonlinear comparametric function with a piecewise linear one. This results in a computationally attractive least squares approach to an otherwise inherently nonlinear optimization problem. The effectiveness of this approach is illustrated.
M Shah - One of the best experts on this subject based on the ideXlab platform.
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Bayesian object detection in dynamic scenes
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005Co-Authors: Yaser Sheikh, M ShahAbstract:Detecting moving objects using stationary cameras is an important precursor to many activity recognition, object recognition and tracking algorithms. In this paper, three innovations are presented over existing approaches. Firstly, the model of the intensities of image pixels as independently distributed random variables is challenged and it is asserted that useful correlation exists in the intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of nominal camera motion and dynamic textures. By using a non-parametric density estimation method over a Joint Domain-range representation of image pixels, multi-modal spatial uncertainties and complex dependencies between the Domain (location) and range (color) are directly modeled. Secondly, temporal persistence is proposed as a detection criteria. Unlike previous approaches to object detection which detect objects by building adaptive models of the only background, the foreground is also modeled to augment the detection of objects (without explicit tracking) since objects detected in a preceding frame contain substantial evidence for detection in a current frame. Third, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of pixel-wise labeling and the posterior function is maximized efficiently using graph cuts. Experimental validation of the proposed method is presented on a diverse set of dynamic scenes.
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Bayesian modeling of dynamic scenes for object detection
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Yaser Sheikh, M ShahAbstract:Accurate detection of moving objects is an important precursor to stable tracking or recognition. In this paper, we present an object detection scheme that has three innovations over existing approaches. First, the model of the intensities of image pixels as independent random variables is challenged and it is asserted that useful correlation exists in intensities of spatially proximal pixels. This correlation is exploited to sustain high levels of detection accuracy in the presence of dynamic backgrounds. By using a nonparametric density estimation method over a Joint Domain-range representation of image pixels, multimodal spatial uncertainties and complex dependencies between the Domain (location) and range (color) are directly modeled. We propose a model of the background as a single probability density. Second, temporal persistence is proposed as a detection criterion. Unlike previous approaches to object detection which detect objects by building adaptive models of the background, the foregrounds modeled to augment the detection of objects (without explicit tracking) since objects detected in the preceding frame contain substantial evidence for detection in the current frame. Finally, the background and foreground models are used competitively in a MAP-MRF decision framework, stressing spatial context as a condition of detecting interesting objects and the posterior function is maximized efficiently by finding the minimum cut of a capacitated graph. Experimental validation of the proposed method is performed and presented on a diverse set of dynamic scenes.
Braham Himed - One of the best experts on this subject based on the ideXlab platform.
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Accounting for array effects in Joint-Domain localized STAP processing
RADAR 2002, 2020Co-Authors: Braham Himed, Michael C. Wicks, G.j. GenelloAbstract:We study critical issues associated with the application of multi-dimensional adaptive. filtering, including space-time adaptive processing (STAP), to real-world radar systems. In particular, transform Domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. It is shown that the selection of asymmetric auxiliary beams offers dramatic improvements in signal-to-interference ratio for targets with low Doppler.
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Application of beam Domain STAP techniques to space-based radars
Thirty-Sixth Southeastern Symposium on System Theory 2004. Proceedings of the, 2004Co-Authors: Yuhong Zhang, A. Hajjari, L. Adzima, Braham HimedAbstract:This paper deals with beam-based space-time adaptive processing (STAP) algorithms for a low-earth-orbit (LEO) space-based radar (SBR). The performance of a subarray-based Joint-Domain-localized (JDL) algorithm is first examined for different parameters. Then, a combined beam-Domain STAP algorithm approach that combines JDL with difference (/spl Delta/) beams is presented. It is shown that the combined JDL-/spl Delta/ algorithms offer less system complexity and provide performance similar to that of the JDL with higher degrees of freedom. It is also shown that the earth's rotation induces a crab angle, which makes the clutter range-Doppler spectrum range dependent. This paper shows that this crab angle severely degrades STAP performance.
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Robust STAP detection in a dense signal airborne radar environment
Signal Processing, 2004Co-Authors: James H. Michels, Braham Himed, Muralidhar RangaswamyAbstract:This paper presents the performance of several space-time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched filter (N-PAMF), the Joint Domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and efficient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program.
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Robust STAP detection in a dense signal airborne radar environment
Signal Processing, 2004Co-Authors: James H. Michels, Braham Himed, Muralidhar RangaswamyAbstract:This paper presents the performance of several space-time adaptive processing (STAP) detection methods in a dense signal environment. These include the normalized parametric adaptive matched filter (N-PAMF), the Joint Domain localized (JDL), and a variant of JDL referred to as the normalized JDL (NJDL). Issues considered here include robust detection with respect to signal contamination of training data and efficient estimation procedures with limited training data. The paper also introduces the innovations power sorting (IPS) pre-processing procedure for representative training data selection. Performance analyses are carried out with measured data from the Multichannel Airborne Radar Measurement (MCARM) program. © 2004 Elsevier B.V. All rights reserved.
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A new constrained Joint-Domain localized approach for airborne radars
Proceedings of the 2002 IEEE Radar Conference (IEEE Cat. No.02CH37322), 2002Co-Authors: Braham Himed, Michael C. Wicks, Peter ZulchAbstract:Critical issues associated with the application of multi-dimensional adaptive filtering including space-time adaptive processing (STAP) to real-world radar systems are considered. In particular, the design of transform Domain localized techniques are examined from the perspective of receiver beam position and Doppler filter selection relative to mainlobe clutter as well as target returns. In particular, asymmetry in the selection of auxiliary beams and the effects of spatial tapering are shown to offer dramatic improvements in signal to interference ratio for targets with low Doppler.
