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Adaptive Processing
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Sylvie Marcos – One of the best experts on this subject based on the ideXlab platform.
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Adaptive Processing methods for MIMO radar experimental signals
, 2014Co-Authors: Mathieu Cattenoz, Laurent Savy, Sylvie MarcosAbstract:MIMO radar is a promising concept and many theoretical studies have demonstrated its interest. The colored transmission makes it possible to extract each waveform and form the transmission beam by digital Processing on receive.One strong limitation of this technique is that waveforms cannot be perfectly separated in practice, because of the intrinsic lack of orthogonality of the waveforms family or hardware defaults. In case of conventional radar Processing, this badly impacts the performance on target detection and localization. In this paper, we explain in which way the usual approach of adapted filter is not adapted for MIMO radar signals. Then, we introduce different approaches to estimate the target amplitude based on Adaptive Processing, and we deal with their performance and limitations when applied in the context of non perfectly orthogonal waveforms.We especially focus on Orthogonal Matching Pursuit (OMP) procedure which aims at detecting and cleaning each target, successively. We point out the problematic effects due to the granularity of the target amplitude grid and neighbor targets influence. To solve the problem, we propose, at each step of the OMP procedure, not only to clean the estimated position of the target, but also the close neighbor positions.We demonstrate that this “extended rejection” increases the robustness of OMP on realistic MIMO waveforms. Eventually, we apply and compare the classical, IAA (Iterative Adaptive Approach) and OMP approaches on experimental MIMO signals.
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Adaptive Processing for MIMO radar realistic non perfectly orthogonal waveforms
, 2014Co-Authors: Mathieu Cattenoz, Sylvie MarcosAbstract:MIMO radar is a promising concept and many theoretical studies have demonstrated its interest, especially for possible improvements in term of target detection and localization. The colored transmission makes it possible to extract each waveform and form the transmission beam by digital Processing on the reception side. One strong limitation of this technique is that waveforms cannot be perfectly separated in practice, because of the intrinsic lack of orthogonality of the waveforms family or hardware defaults. In case of conventional radar Processing, this badly impacts the performance on target detection.In this paper, we introduce another approach to perform the estimation of target detection and localization based on Adaptive Processing such as Capon estimation. We define the techniques especially for MIMO radar configurations and we use them on realistic signals, taking into account the properties of non perfect orthogonality of the transmitted MIMO waveforms. We compare the results of the different methods to establish to which extent the performance in target detection can be improved. In some conditions, all sidelobes can be suppressed.
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A Riemannian approach for training data selection in space-time Adaptive Processing applications
, 2013Co-Authors: Jean-françois Degurse, Sylvie Marcos, Laurent Savy, Jean-philippe MoliniéAbstract:Heterogeneous situations are a serious problem for Space-Time Adaptive Processing (STAP) in an airborne radar context. Indeed, STAP detectors need secondary training data that have to be homogeneous with the tested data, otherwise the performances of these detectors are severely impacted when facing heterogeneous environments. Hence, training data have to be carefully selected and this is traditionally done in Euclidean geometry. We introduce a new criterion for data selection. We show that it can be viewed as an approximation of the metric distance in Riemannian geometry.
Michael C Wicks – One of the best experts on this subject based on the ideXlab platform.
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Knowledge based Adaptive Processing for ground moving target indication
Digital Signal Processing, 2007Co-Authors: Raviraj S Adve, T.b. Hale, Michael C WicksAbstract:This paper presents a preliminary knowledge based approach to space-time Adaptive Processing (STAP) for ground moving target indication from an airborne platform. The KB-processor accounts for practical aspects of Adaptive Processing, including detection and Processing of non-homogeneous data, appropriate selection of training data, and accounting for array effects such as mutual coupling and channel mismatch. In combining these hitherto separate STAP issues into a unified approach, this paper furthers the move of STAP from theory to practice. The KB-approach is tested using measured data from the multi-channel airborne radar measurements (MCARM) program.
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space time Adaptive Processing a knowledge based perspective for airborne radar
IEEE Signal Processing Magazine, 2006Co-Authors: Michael C Wicks, Raviraj S Adve, Muralidhar Rangaswamy, T.b. HaleAbstract:This article provides a brief review of radar space-time Adaptive Processing (STAP) from its inception to state-of-the art developments. The topic is treated from both intuitive and theoretical aspects. A key requirement of STAP is knowledge of the spectral characteristics underlying the interference scenario of interest. Additional issues of importance in STAP include the computational cost of the Adaptive algorithm as well as the ability to maintain a constant false alarm rate (CFAR) over widely varying interference statistics. This article addresses these topics, developing the need for a knowledge-based (KB) perspective. The focus here is on signal Processing for radar systems using multiple antenna elements that coherently process multiple pulses. An Adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, overcomes the directivity and resolution limitations of a single sensor.
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Demonstration of knowledge-aided space-time Adaptive Processing using measured airborne data
IEE Proceedings – Radar Sonar and Navigation, 2006Co-Authors: C.t. Capraro, Gerard T. Capraro, A. De Maio, Alfonso Farina, Michael C WicksAbstract:The design and analysis of a knowledge-aided detector for airborne space-time Adaptive Processing (STAP) applications are addressed. The proposed processor is composed of a training data selector, which chooses secondary cells best representing the clutter statistics in the cell under test, and an Adaptive processor for detection Processing. The data selector is a hybrid algorithm, which pre-screens training data through the use of terrain information from the United States Geological Survey. Then, in the second stage, a data-driven selector attempts to eliminate residual non-homogeneities. The performance of this new approach is analysed using measured airborne radar data, obtained from the multi-channel airborne radar measurements program, and is compared with alternative STAP detectors proposed in the open literature.
Sophie Beau – One of the best experts on this subject based on the ideXlab platform.
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Range recursive and Taylor series based space-time Adaptive Processing for range dependent clutter rejection
, 2011Co-Authors: Sylvie Marcos, Sophie BeauAbstract:This paper presents a new space time Adaptive Processing (STAP) for the rejection of range dependent ground clutter in order to detect slow moving targets with an airborne radar. STAP usually requires the estimation of the clutter plus noise covariance matrix from secondary data neighboring the cell under test. However, in most radar antenna array architectures and/or configurations which are different from the conventional uniform linear antenna array and side-looking configuration the clutter is range dependent. We recently proposed the use of a Taylor series expansion of the clutter plus noise subssubspace in conjunction with the eigencanceler-based (EC) STAP in order to mitigate the range non-stationarity of the clutter. In this paper, the computationally costly EC is replaced by a range-recursive algorithm which is capable of tracking non stationarities with a reduced complexity compared to the EC. The performance of the proposed algorithm is satisfactorily tested and compared to other algorithms in the case of a bistatic configuration.
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Taylor series expansions for airborne radar space-time Adaptive Processing
IET Radar Sonar and Navigation, 2011Co-Authors: Sophie Beau, Sylvie MarcosAbstract:Space time Adaptive Processing (STAP) for range dependent clutter rejection in airborne radar is considered. Indeed, radar antenna architectures or configurations which are different from the conventional uniform linear antenna array (ULA) and side-looking configuration have consequences on the clutter properties. We here investigate the use of Taylor series expansions of the space-time covariance matrix in the classical sample matrix inversion (SMI) STAP method in order to mitigate the range non-stationarity of the clutter and we compare it to the Derivative Based Updating (DBU) already proposed in the literature. We also propose a new algorithm based on a Taylor series expansion of the interference plus noise subssubspace in conjunction with the eigencanceler-based (EC) STAP, which improves the performance in term of SINR loss, compared to the DBU method. In this paper, the particular cases of a ULA in a non side-looking configuration and a uniform circularly curved antenna (UCCA) array in side-looking and non side-looking configurations are considered for the test and the comparison of the presented algorithms.
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Range dependent clutter rejection using range recursive space-time Adaptive Processing (STAP)
Signal Processing, 2010Co-Authors: Sophie Beau, Sylvie MarcosAbstract:This paper adresses the issue of ground clutter rejection for the detection of slowly moving targets in a non side looking (NSL) array configuration airborne radar. The optimum Space Time Adaptive Processing (STAP) filter needs the knowledge of the inverse of the space-time covariance matrix. In practice, it is unknown and has to be estimated. The most popular approximated method is the Sample Matrix Inversion (SMI) method which consists in inverting the covariance matrix estimated by an average of the sample matrix over the secondary range cells. This estimator is unbiased in case of i.i.d. data. In a NSL configuration, the clutter power spectrum is range dependent and the data are consequently not i.i.d.. We here present a solution to mitigate this range dependency of the data : the range recursive subspace-based algorithms. They are used in two architectures: a fully and a partially Adaptive ones. Then a new range recursive algorithm using Taylor series expansion is investigated. The performance of these algorithms are compared with that of the conventional STAP algorithms in term of SINR loss.
Raviraj S Adve – One of the best experts on this subject based on the ideXlab platform.
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Two-dimensional Adaptive Processing for ionospheric clutter mitigation in High Frequency Surface Wave Radar
2009 IEEE Radar Conference, 2009Co-Authors: Ryan J. Riddolls, Raviraj S AdveAbstract:High Frequency Surface Wave Radar (HFSWR) is a technology used for over-the-horizon detection of ocean vessels. This radar exploits the diffraction of electromagnetic waves around the curved surface of the Earth. To minimize the attenuation of the diffracted waves, the radar must operate at frequencies in the lower part of the high frequency (HF) band. However, radar signals at these frequencies also reflect from the Earth’s ionosphere, which leads to radar clutter at ranges beyond 200 km. The linear broadside receive arrays used by conventional HFSWR systems cannot filter out this clutter as the arrays do not have any resolving power in elevation angle. Reported here are experimental investigations of the clutter suppression capability of one- and two-dimensional HFSWR Adaptive processors. Three configurations are compared: one-dimensional spatial Adaptive Processing, two-dimensional spatial Adaptive Processing, and space-time Adaptive Processing. In all cases the number of Adaptive degrees of freedom is 16. It is found that the best results are achieved by two-dimensional spatial Adaptive Processing, where a Processing gain of up to about 20 dB can be achieved.
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Knowledge based Adaptive Processing for ground moving target indication
Digital Signal Processing, 2007Co-Authors: Raviraj S Adve, T.b. Hale, Michael C WicksAbstract:This paper presents a preliminary knowledge based approach to space-time Adaptive Processing (STAP) for ground moving target indication from an airborne platform. The KB-processor accounts for practical aspects of Adaptive Processing, including detection and Processing of non-homogeneous data, appropriate selection of training data, and accounting for array effects such as mutual coupling and channel mismatch. In combining these hitherto separate STAP issues into a unified approach, this paper furthers the move of STAP from theory to practice. The KB-approach is tested using measured data from the multi-channel airborne radar measurements (MCARM) program.
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space time Adaptive Processing a knowledge based perspective for airborne radar
IEEE Signal Processing Magazine, 2006Co-Authors: Michael C Wicks, Raviraj S Adve, Muralidhar Rangaswamy, T.b. HaleAbstract:This article provides a brief review of radar space-time Adaptive Processing (STAP) from its inception to state-of-the art developments. The topic is treated from both intuitive and theoretical aspects. A key requirement of STAP is knowledge of the spectral characteristics underlying the interference scenario of interest. Additional issues of importance in STAP include the computational cost of the Adaptive algorithm as well as the ability to maintain a constant false alarm rate (CFAR) over widely varying interference statistics. This article addresses these topics, developing the need for a knowledge-based (KB) perspective. The focus here is on signal Processing for radar systems using multiple antenna elements that coherently process multiple pulses. An Adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, overcomes the directivity and resolution limitations of a single sensor.
Zhengguang Zhou – One of the best experts on this subject based on the ideXlab platform.
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ICASSP – Space-time-range three dimensional Adaptive Processing
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Shengqi Zhu, Guisheng Liao, Zhengguang ZhouAbstract:Space-time Adaptive Processing (STAP) is an effective tool for moving target detection. Conventional STAP methodologies process the angular and Doppler two dimensional data vector. In practical applications, adjacent range cells are statistically dependent due to filtering, since the point spreading function of a target is not an ideal delta function. In this paper, a novel approach incorporating range (fast time) information in STAP is presented for clutter rejection, which we term space-time-range Adaptive Processing (STRAP). This method takes advantage of the correlation information of neighboring range cells. Therefore, the stationary clutter can be suppressed better compared with traditional STAP algorithms ignoring fast time information, resulting in more effective moving target detection. The validity of the STRAP algorithm is verified by the experiments of Processing the real measured data of the three-channel X-band radar and MCARM radar systems.