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ABSTRACT: We continue our investigation into the new class of two-dimensional autoregressive relaxed models (ldquorelaxationsrdquo) for space-time adaptive processing (STAP) applications. Previously reported results on the DARPA KASSPER simulated dataset for airborne side-looking radar are now complemented by STAP performance analysis for all range bins and varying antenna-array errors. We discuss the variability of signal-to-interference-plus-noise ratio (SINR) performance associated with the changing terrain conditions across all 1000 KASSPER range bins, and more closely investigate the impact of antenna errors and training data inhomogeneity. Performance improvements due to the previously proposed regularisation of the parametric models are also demonstrated in more detail.
Radar, 2008 International Conference on; 10/2008
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ABSTRACT: Airborne radar systems employing radar sensor arrays utilize multi-channel (MC) signal processing techniques for optimal detection and localization of targets. The detection and localization statistics have mathematical structures that typically require evaluating the inverse of an estimated covariance matrix. Due to the size of sensor arrays and the number of pulses in a coherent processing interval (CPI), the dimension of the covariance arrays is very large (1000s); the computational burden of estimating and inverting such large arrays has led to the development of parametric methodologies that significantly reduce both the computational requirements and the amount of measured data to create the estimated inverse covariance matrix. This paper compares the relative merits, by using performance tradeoff plots of six different parametric algorithms when compared to the conventional sample matrix inversion (SMI) approach.
Radar, 2008 International Conference on; 10/2008
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ABSTRACT: We analyze the performance of a recently described class of two-dimensional autoregressive parametric models for space-time adaptive processing (STAP) in airborne radars on the DARPA side-looking radar model known as KASSPER Dataset 1. We investigate the trade-offs between signal-to-interference-plus-noise ratio (SINR) degradation (with respect to the optimal clairvoyant receiver) due to the mismatch between the observed covariance matrix and its parametric model, and the degradation due to the limited training sample volume. The impact of ground-clutter inhomogeneity on parametric STAP performance is demonstrated, as well as the significant superiority of parametric STAP over the conventional loaded sample-matrix inversion (SMI) technique.
Radar Conference, 2008. RADAR '08. IEEE; 06/2008
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ABSTRACT: The optimal (adaptive) linear combiner (beamformer) weights for a sensor array are expressed in terms of the inverse of the multi-channel (MC) covariance matrix. Also, minimum variance (Capon) spectral estimators of the sensor array also depend on the same inverse. Rather than form an estimate of the covariance matrix directly from the available data and inverting it, an alternative direct estimate of the inverse may be obtained by forming parametric MC linear prediction estimates and then expressing the inverse in terms of these parametric MC estimates. The resulting parametric estimate of the inverse is typically more accurate than inverting the estimate of the covariance matrix. This paper reveals the structure of the the inverse of the covariance matrix for the MC version of the covariance least squares linear prediction algorithm. The inverse structure involves products of triangular block MC Toeplitz matrices, which leads to fast computational solutions. An example of a fast MC minimum variance spectral estimator illustrates this exploitation.
Computational Advances in Multi-Sensor Adaptive Processing, 2007. CAMPSAP 2007. 2nd IEEE International Workshop on; 01/2008
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ABSTRACT: In side-looking ground moving target indication (GMTI) radar, the 2-dimensional (2D) space time (azimuth-Doppler) domain can adequately define a clutter spectrum which is accurate for all range gates. However, in applications where the array boresight is not perpendicular to the velocity vector (e.g. forward-looking radar), the azimuth-Doppler clutter spectrum exhibits a dependence on elevation angle-of-arrival, creating range-varying (but elevation-dependent) clutter statistics, or non-stationary clutter. Classical space time adaptive processing (STAP) algorithms suffer substantial performance losses in non-stationary clutter since classical STAP assumes clutter stationary along the range (training) dimension. Planar arrays are inherently able to observe the azimuth-Doppler clutter spectrum as a function of the elevation angle, a capability which linear arrays lack. The incorporation of the planar array's vertical dimension into the joint azimuth-Doppler (2D) STAP domain has previously resulted in 3D STAP. This paper demonstrates the ability of 3D STAP to solve the non-stationary clutter problem by accounting for the elevation-dependent clutter statistics in a 3D covariance matrix. A forward-looking array is used to provide non-stationary clutter, and the performance of 2D and 3D versions of the adaptive matched filter (AMF) and joint domain localized (JDL) are used in a close-in sensing paradigm. The results show a >55 dB improvement in output SINR near the clutter null using 3D STAP algorithms in lieu of 2D STAP algorithms applied to the same (subarrayed) data.
Radar Conference, 2007 IEEE; 05/2007
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ABSTRACT: This work investigates the impact of interpulse (pulse-to-pulse) transmit pattern diversity on space-time adaptive processing (STAP) performance. It is shown that varying interpulse transmit characteristics within a coherent processing interval (CPI) can reshape the clutter power spectrum, resulting in an interference whitening effect. For conducting comparative analysis with non-adaptive transmit techniques, a commonly used clutter model is extended to effectively incorporate interpulse pattern diversity effects. The work shows promise for achieving better minimum discernable velocity (MDV) using phased array transmits weights derived from optimum STAP weights (known covariance). Pattern diversity effectively redistributes clutter energy away from the clutter ridge. For the unambiguous clutter case, the proposed adaptive transmit technique shows promise for improving MDV at the clutter ridge peak.
Radar Conference, 2005 IEEE International; 06/2005
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ABSTRACT: Research done in recent years has clearly demonstrated large improvements in clutter suppression and target detection by including elevation adaptivity, otherwise described as 3-dimensional (3D) STAP. The paper further quantifies the performance gains garnered by 3D STAP by fixing the degrees of freedom (DOF) and varying the array dimensions to include the equivalently sized linear array. The focus is placed on performance bounds established by matched filter and 3D cross-spectral metric (CSM) SINR curves generated with known covariances. The mathematical extension of the CSM from 2D to 3D is shown to be straightforward, thus allowing the CSM to serve as a partially adaptive performance bound for eigenvalue-selection based 3D STAP algorithms.
Radar Conference, 2004. Proceedings of the IEEE; 05/2004
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ABSTRACT: A new class of two-dimensional parametric models for ground clutter is considered for STAP applications in airborne radars and investigated using the side-looking scenario specified by a DARPA KASSPER data set. Signal-to-interference-plus- noise (SINR) degradation with respect to the optimal clairvoyant receiver is investigated for different parametric models, regularized estimation techniques and training sample volumes. We demonstrate that for the selected KASSPER scenario, an extremely small training sample support (10-15 training ranges), properly selected parametric models and estimation techniques can deliver practically acceptable STAP performance.
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ABSTRACT: We continue our investigation into the new class of two-dimensional autoregressive relaxed models ('Lrelaxations") for space-time adaptive processing (STAP) applications. Previously reported results on the DARPA KASSPER simulated dataset for airborne side-looking radar are now complemented by STAP performance analysis for all range bins and varying antenna-array errors. We discuss the variability of signal-to- interference-plus-noise ratio (SINR) performance associated with the changing terrain conditions across all 1000 KASSPER range bins, and more closely investigate the impact of antenna errors and training data inhomogeneity. Performance improvements due to the previously proposed regularisation of the parametric models are also demonstrated in more detail.
