Taylor series expansions for airborne radar space-time adaptive processing

The range-dependence of clutter spectrum for forward-looking airborne radar strongly affects the accuracy of the estimation of clutter covariance matrix at the range under test, which results in poor clutter suppression performance if the conventional space-time adaptive processing (STAP) algorithms were applied, especially in the short range cells. Therefore, a new STAP algorithm with clutter spectrum compensation by utilizing knowledge-aided subspace projection is proposed to suppress clutter for forward-looking airborne radar in this paper. In the proposed method, the clutter covariance matrix of the range under test is firstly constructed based on the prior knowledge of antenna array configuration, and then by decomposing the corresponding space-time covariance matrix to calculate the clutter subspace projection matrix which is applied to transform the secondary range samples so that the compensation of clutter spectrum for forward-looking airborne radar is accomplished. After that the conventional STAP algorithm can be applied to suppress clutter in the range under test. The proposed method is compared with the sample matrix inversion (SMI) and the Doppler Warping (DW) methods. The simulation results show that the proposed STAP method can effectively compensate the clutter spectrum and mitigate the range-dependence significantly.

This paper presents a comparative study of space-time adaptive processing (STAP) methods, adapted to noise-like signals in general and digital video broadcasting-terrestrial (DVB-T) in particular. The advantages and disadvantages of each method are highlighted through the possibility of a multi-target detection among the various kinds of interferences, either homogeneous or not. This study emphasizes the efficiency of the hybridization of both the joint domain localized (JDL) and the direct data domain (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) STAP methods in the conception of a passive DVB-T-based multi-target bistatic radar.

Space-time adaptive monopulse processing for airborne radar in non-homogeneous environments

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling; it derives the optimum phase-only STAP, namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the $N$ -torus of phase-only complex vectors, where $N$ is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phase-only STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.

Conventional Space-time adaptive processing (STAP) requires large numbers of independent and identically distributed (i.i.d.) training samples to ensure the clutter suppression performance, which is hard to be achieved in nonhomogeneous environment. In order to obtain improved clutter suppression with small training support, an iterative sparse recovery STAP algorithm is proposed in this paper. In the proposed method, the clutter spectrum sparse recovery and the calibration of space-time over-complete dictionary are implemented iteratively, modified focal underdetermined system solution (FOCUSS) with recursive calculation is used to alleviate the recovery error and reduce the computational cost, meanwhile the mismatch of space-time overcomplete dictionary is calibrated by minimized the cost function. Based on the simulated and the actual data, it is verified that the proposed method can not only converge with much smaller training samples compared with conventional STAP methods, but also provide improved performance compared with existing sparsity-based STAP methods.

The space-time adaptive processing (STAP) in the airborne multiple-input multiple-output (MIMO) radar with nested structure is studied. According to the special structures of arrays and transmitting pulse interval, a virtual space-time snapshot vector with longer length is constructed. Both the virtual antenna array and the virtual pulse train formed by the nested array and nested pulse train are successive and hole-free. Then a Hermitian matrix is built to estimate the full rank clutter covariance matrix in airborne MIMO radar with nested structure. For the increasing DoFs, the STAP method can achieve higher upper bound in the airborne MIMO radar with nested structure than the STAP in the traditional airborne MIMO radar, which facilitates the clutter suppression and target detection. Simulation results verify the proposed STAP method.

A method that further exploits the sparsity of weight vector in space-time adaptive processing (STAP) and decreases the training sample demanding is proposed. In the proposed method, STAP with the generalized sidelobe canceler (GSC) structure by using the L p -norm regularization, instead of the conventional L 1 -norm regularization, is considered. The core idea behind the method lies in minimizing the cost function that combines the output error and the L p -norm of weight vector, which is a more accurate sparsity representing operator than L 1 -norm. For solving the desired weight vector, the recursive least squares (RLS) algorithm with respect to the gradient of L p -norm is deduced. In addition, since the diagonal loading factor cannot be clearly defined, the L p -norm GSC STAP with parallel structure is designed by choosing the branch that maximizes the output signal to clutter ratio. In the end, the simulation experiments are conducted to verify the performance of the proposed L p -norm GSC STAP.

Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations.

In this paper, a robust statistical method called projection statistics (PS) is developed into an TV-dimensional complex form required to detect strong nonhomogeneities, or TV-dimensional outlier vectors (i.e., outliers), in space-time adaptive processing (STAP) radar training data, where N is the number of degrees of freedom (DOF) associated with the STAP scenario. The PS technique does not require the estimation of a covariance matrix, as is the case with the generalized inner product (GIP) test. Rather, it uses robust estimators of location (the median) and scale (the median absolute deviation from the median (MAD)) to identify and then excise strong nonhomogeneities in STAP training data that are capable of rendering traditional nonhomogeneity detection (NHD) techniques, such as the GIP, ineffective. Shown in the results section of this paper is sample matrix inversion (SMI) STAP performance using three separate NHD techniques: 1) a power test for high power outlier snapshots, 2) a GIP test, and 3) a PS-based test. The performance when not using any form of NHD is shown for comparison. It is clear from the results that the PS NHD method provides an SMI-STAP processor the best training data selection in the multi channel airborne radar measurements (MCARM) [B.N.S Babu et al., 1996] based radar data scenario studied here, and does so across all range cells of the scenario.

A major issue in space-time adaptive processing (STAP) for airborne moving target indicator (MTI) radar is the so-called sample support problem. Often, the available sample support for estimating the interference covariance matrix leads to severe rank deficiency, thereby precluding STAP beamforming based on the direct sample matrix inversion (SMI) method. The intrinsic interference subspace removal (ISR) technique, which is a computationally useful form of diagonally loaded SMI method, can handle this case, although the performance is poor in low sample situations. In this context, new subarray-subpulse schemes using forward and backward data vectors are introduced to overcome the data deficiency problem. It is shown that a multiplicative improvement in data samples can be obtained at the expense of negligible loss in space-time aperture of the steering vector.

Antenna arrays with space-time adaptive processing (STAP) are commonly utilized to allow the reception of signals in harsh interference environments. The primary applications of STAP have been radar and communications systems, where the STAP weights are optimized for output signal-to-interference-plus-noise ratio (SINR); however, there is increasing demand for STAP in time-of-arrival (TOA) estimation applications, such as global navigation satellite system (GNSS) receivers. It is understood that TOA estimation performance is primarily dependent on receiver postcorrelation carrier-to-noise ratio (C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ). In this study, the distinction between SINR and C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> is established, and a performance analysis of the common STAP algorithms is performed in the presence of interference. It is demonstrated that output SINR is an inadequate indication of GNSS receiver C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> performance. As a result, caution should be used when measuring the relative performance of different STAP algorithms in GNSS applications. Consequently, the present work develops a novel STAP algorithm which maximizes C/N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> . Additionally, the performance of common STAP algorithms is analyzed in the context of this performance bound, and it is demonstrated that, in many cases, their performance is near optimal.

We study the performance of space-time adaptive processing (STAP) applied to coherent multistatic radar. The radars we consider are passive (receive only) but are synchronized in order to allow coherent processing of the data among the different radars. One of the major benefit of multistatism is that the full velocity vectors of the targets can be measured. Furthermore, we study the feasibility of estimating the clutter covariance matrix, required to compute the STAP filter. We show that range-dependence effects cause the usual sample matrix inversion (SMI) scheme to fail and we propose a method able to cope with arbitrary multistatic geometries.

For a general conformal array, the angle-Doppler spectrum of surface clutter in monostatic radar systems is range dependent This results in a mismatch of the clutter covariance matrix (computed from a secondary set of range-cell data) relative to the clutter covariance matrix of a test cell, with attendant degradation of space-time adaptive processing (STAP) performance. Unlike for a line array, the clutter covariance matrix of a range cell depends on both its elevation and azimuth angles. This work develops a least-squares method for estimating the system configuration parameters from the secondary data. The method uses a multiple complex sinusoid model whose parameters are related to the system configuration parameters which are then optimized to match the radar return spatial-temporal data in a least-squares sense. The estimated configuration parameters are then used to predict the clutter covariance matrix in the test cell which is then used with STAP methods. Computer simulation results are presented to show the improved STAP performance compared to standard compensation methods.

In this letter, the local degree-of-freedom (LDOF) theorem for reduced-dimension space-time adaptive processing (STAP) methods is presented, and a rigorous proof is provided. LDOF is more valuable for practical STAP methods than conventional full degrees of freedom. The effectiveness of the LDOF theorem is verified, and the influences of some operations in practice on the LDOF are analyzed by simulations.

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar. However, the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets. It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters. However, in the presence of serious Doppler ambiguity of fast air moving targets, distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets, and as a result the STAP performance will degrade. In this paper, a novel STAP method is proposed for the detection of fast air moving targets from high speed platform, which can deal with the range walk of both clutter and targets. Effectiveness of the new method is verified via simulation examples.