Robust STAP detection in a dense signal airborne radar environment (original) (raw)
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Space-Time Adaptive Processing (STAP) Performance in Non-Homogeneous Radar Clutter
2001
report addresses the statistical analysis of the non-homogeneity detector (NHD) for non-Guassian interference scenarios. An important issue in STAP is that of homogeneity of training data. Non-homogeneity of the training data has a deleterious effect on STAP performance in that undernulled clutter significantly degrades detection and false alarm characteristics. Previous work in this area has proposed the use of non-homogeneity detector based on a generalized inner product (GIP). The unsuitability of the GIP based test for non-Guassian interference scenarios is noted. We present a new non-homogeneity detector for non-Guassian interference scenarios which can be modeled by a spherically invariant random process (SIRP). Our work includes a statistical analysis of the NHD for non-Guassian interference taking into account the fact that finite sample support is used for covariance estimation. In particular, exact theoretical expressions for the NHD test statistic PDF and the mean of a related test statistic are derived. We also note that the related test statistic admits a remarkably simple stochastic representation as a ratio of an F-distributed random variable and a beta-distributed loss factor. Based on this development, a formal goodness-of-fit test is presented. Performance analysis is carried out using simulated and measured data from the MC ARM Program.
Performance comparison of three recent STAP methods
IEEE International Radar Conference, 2005., 2005
In this paper, we present a comparison of target detection performance for normalized adaptive matched filter (NAMF), normalized parametric adaptive matched filter (NPAMF), and normalized low-rank adaptive matched filter (LRNAMF) for space-time adaptive processing. Test statistics for these algorithms as functions of range bins and filter outputs as functions of Doppler beam position (DBP) and azimuth angle (AZ) are computed for the KASSPER L-band datacube, which is simulated for the airborne linear phased array radar application. First, we illustrate that when the signal-to-noise ratio is used as a target-detection parameter, LRNAMF outperforms NAMF and NPAMF under weak conditions of training data contamination. Next, we demonstrate the target cancellation effect when the training data are contaminated by competing targets (outliers). Finally, we present a scenario for target detection in heterogenous radar clutter when there is spatio-temporal steering vector uncertainty. In this scenario, we show that there is substantial broadening in the filter outputs as functions of DBP and AZ for these algorithms.
Comparative study on PC-SD and MWF Algorithms for STAP RADAR
International Journal of Engineering and Technology, 2017
The paramount challenge in the radar system is to alleviate the consequences of cold (homogeneous) clutter, severe dynamic (heterogeneous) hot clutter and jamming interferences while estimating the states of targets under track. To surmount this challenge, Space-Time Adaptive Processing (STAP) intensify the competence of radar systems. Space-time Adaptive Processing is a two-dimensional filtering technique for antenna array with multiple spatially distributed channels. The name 'Space-Time' elucidate the coupling of multifarious spatial channels with pulse-Doppler waveforms. The term "Adaptive processor" signifies that it can employ using a variety of algorithms on many platforms ranging from space satellites to a small low flying unmanned aerial. In order to develop STAP algorithms to operate in adverse environments, where intense environmental interference can reduce STAP proficiency to detect and track ground targets. STAP can effectively suppress these interferences and maximize the signal to interference plus noise ratio (SINR). Methods such as principal component analysis, Multi-stage Weiner Filter (MWF) are applied to STAP system. Rank and Minimum square error are parameters considered for estimating the performance of two stated techniques. Keywords-STAP (space Time Adaptive Processing), homogeneous clutter, heterogeneous clutter, PC-SD, MWF, Rank, MSE. I. INTRODUCTION An adaptive processing uses spatial and temporal domains for signal detection ,which offers significant advantages in a variety of applications including radar, sonar, and satellite communication [1].The signal processing for radar systems uses multiple antenna elements that coherently process multiple pulses. An adaptive array of spatially distributed sensors, which processes multiple temporal snapshots, surmount the directivity and resolution limitations. Specifically, STAP using STAP creates an aptness to suppress interfering signals while simultaneously conserving gain of the desired signal. Additional gain afforded by an array of sensors leads to enhancement in the signal-to-noise ratio, resulting in an ability to place deep nulls in the direction of interfering signals. Advanced airborne radar systems are equipped to detect targets in presence of both clutter and jamming. The ground clutter scrutinize by an airborne platform is extended in both angle and range and is spread in Doppler frequency because of the platform motion. STAP can significantly improve airborne radar performance. Computational complexity and the need to estimate non-stationary interference with limited data forces considerations of partially adaptive architectures. The STAP computational complexity is driven not just be the size of a single adaptive problem, but also by the number of adaptive problems that must be solved per coherent processing interval (CPI) [2].Fully adaptive STAP, though optimum given perfect knowledge, is impractical for two reasons. First is the computational burden of solving large system of equations in real-time. Second is the interference is unknown a-priori and must be estimated from the limited amount of data available during a radar dwell. The inherent non-stationary of radar clutter makes this estimation more difficult. Reduced dimension STAP Algorithms are required to ease both computation and training support. [3,4,5]. This paper utilizes the framework of space time adaptive processing for radar. In STAP, the sensor is composed of K elements and each element is followed by J taps spaced at the pulse repetition interval. The goal of this N=K J-dimensional STAP filter is to suppress clutter and interference for the purpose of improving target detection. In this paper, we propose that, how adaptive filtering of target signals can be achieved via Multi-stage wiener filter and principal component-signal dependent algorithms. Simulations will show that MWF generally offer predominant rank and sample support compression than the more commonly used PC-SD. It is demonstrated that the new multistage wiener filtering technique provides a larger region of support for adaptivity.
Robust STAP approach in nonhomogeneous clutter environments
2001
The new generation airborne phased array radars widely adopt flexible multiple-beam so that they have multiple functions. In this paper, a robust STAP approach with space-time multiple-beam (STMB) architecture is proposed, which has a low degree of freedom and limits the required secondary data for covariance matrix estimation. A new sample selection scheme based on the correlation dimension has been proposed, which is very suitable for the nonhomogeneous clutter environments. The experimental simulation results indicate that incorporating nonhomogeneity detection with the space-time adaptive processor dramatically improves the detection performance for airborne radar
COMPARISON OF VARIOUS DETECTION SCHEMES FOR STAP RADAR BASED ON EXPERIMENTAL DATA
2009
In the general area of radar detection, several detection schemes have been developed for the two last decades. These detectors may be classified into two major families: Gaussian and non-Gaussian detectors, depending on the clutter assumptions. Moreover, methods have been proposed to take into account the structure of the Clutter Covariance Matrix (CCM) in order to improve its estimation accuracy. In a STAP (Space Time Adaptive Processing) context, this paper compares four Gaussian and non-Gaussian detection schemes on experimental data. The obtained results clearly demonstrate the improved detection performance brought by a recently proposed persymmetric non-Gaussian detector.
Performance Analysis of Two-Dimensional Parametric STAP for Airborne Radar using KASSPER Data
IEEE Transactions on Aerospace and Electronic Systems, 2000
We analyze the performance of a recently introduced class of two-dimensional (2-D) multivariate parametric models for space-time adaptive processing (STAP) in airborne radars on the DARPA airborne side-looking radar model known as KASSPER Dataset 1. Investigation of the impact of linear uniform antenna array errors on techniques that exploit spatial smoothing is demonstrated using a complementary phenomenological clutter model developed at the AFRL. Signal-to-interference-plus-noise ratio (SINR) degradation with respect to the optimal clairvoyant receiver is studied for different parametric models, antenna errors, and training sample volumes. We also analyze the impact of KASSPER training data inhomogeneity on STAP performance. For an extremely small number of training-data samples, we demonstrate that a properly selected parametric model and an accompanying covariance matrix estimation technique should achieve efficient performance for practical STAP applications.
Overview of Space-Time Adaptive Processing Algorithms for Radar Systems
Radar systems are confronted with increasingly complex objectives in a highly non cooperative interference environment. To meet the challenge, sensor systems are forced to utilize multidimensional signal processing techniques. In fact, conventional signal processing perform poorly due to lack of knowledge of highly dimensional statistic requirements. On the other hand, traditional adaptive signal processing techniques break down or have suboptimal performance in these cases. A possible approach is optimal or suboptimal Space-Time Adaptive Processing (STAP) techniques. This article presents an introduction to STAP which was originally developped for detecting slow moving objectives from airborne radars. STAP is a data domain implementation of an optimal filter solution. The optimum filter is designed based on the known covariance matrix and the known Doppler-angle
Airborne radar STAP using sparse recovery of clutter spectrum
2010
Space-time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Statistical-based STAP methods generally need sufficient statistically independent and identically distributed (IID) training data to estimate the clutter characteristics. However, most actual clutter scenarios appear only locally stationary and lack sufficient IID training data. In this paper, by exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain, a new STAP algorithm called SR-STAP is proposed, which uses the technique of sparse recovery to estimate the clutter space-time spectrum. Joint sparse recovery with several training samples is also used to improve the estimation performance.
Robust adaptive signal processing methods for heterogeneous radar clutter scenarios
Signal Processing, 2004
This paper addresses the problem of radar target detection in severely heterogeneous clutter environments. Speciÿcally, we present the performance of the normalized matched ÿlter test in a background of disturbance consisting of clutter having a covariance matrix with known structure and unknown scaling plus background white Gaussian noise. It is shown that when the clutter covariance matrix is low rank, the (LRNMF) test retains invariance with respect to the unknown scaling as well as the background noise level and has an approximately constant false alarm rate (CFAR). Performance of the test depends only upon the number of elements, the number of pulses processed in a coherent processing interval, and the rank of the clutter covariance matrix. Analytical expressions for calculating the false alarm and detection probabilities are presented. Performance of the method is shown to degrade with increasing clutter rank especially for low false alarm rates. An adaptive version of the test (LRNAMF) is developed and its performance is studied with simulated data from the KASSPER program. Results pertaining to sample support for subspace estimation, CFAR, and detection performance are presented. Target contamination of training data has a deleterious impact on the performance of the test. Therefore, a technique known as self-censoring reiterative fast maximum likelihood/adaptive power residue (SCRFML/APR) is developed to treat this problem and its performance is discussed. The SCRFML/APR method is used to estimate the unknown covariance matrix in the presence of outliers. This covariance matrix estimate can then be used in the LRNAMF or any other eigen-based adaptive processing technique.