Adaptive Compensation for Conformal Array STAP by Configuration Parameter Estimation

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.

The ground moving-target indication (GMTI) technique can detect civil and military moving targets, which means that this technique has received much attention. Strong clutter background suppression is one of the critical problems in this application. However, the detection performance in heterogeneous environment can be degraded due to the inaccurate estimation of the clutter covariance matrix (CCM). In this paper, we propose a robust GMTI method using a spaceborne dual-platform synthetic aperture radar (SAR) system, which can obtain highly accurate CCM in nonuniform clutter. Firstly, the accurate CCM is estimated based on the SAR image obtained by the former platform. Then, space–time adaptive processing (STAP) is carried out using the obtained the CCM. Finally, the detection threshold is set according to the estimated CCM and detection is executed accurately. Compared with the traditional CCM estimation method in STAP using the clutter nearby the cell under test, this method directly estimates the CCM using the clutter of the cell under test, which can avoid CCM estimation mistakes in heterogeneous clutter environment. The clutter can be whitened and depressed more effectively. Additionally, with the accurate threshold acquired from the CCM, the detection probability can be effectively improved under a certain false-alarm criterion. Based on simulation data, GMTI experiments in a heterogeneous environment such as clutter with strong pollution, junction zone of hot and cold clutter, and clutter with nonuniform power are carried out; the results show that the moving targets can be effectively detected with the proposed method.

The effects of array errors on the clutter spectra are discussed. It is pointed out that, just like that in pattern recognition problems, the training method has significant effects on the performance of space-time adaptive processing (STAP) techniques. A reasonable training method is given, which exhibits strong generalizing ability. Simulation results based on both the steady-state clutter covariance matrix and the high fidelity radar clutter data confirm the above conclusions.

Space-time adaptive processing (STAP) is widely applied in airborne planar array phased radar to suppress clutter or interference, especially range ambiguous clutter. This approach requires an accurate estimation of clutter covariance matrix(CCM) by the secondary data snapshots. However, in the forward-looking scenario angle-Doppler relationship is severely range-dependent(i.e clutter nonstationarity), even worse in down-looking scenario. This phenomenon makes the CCM inaccurate, and the STAP performance will be degraded badly. In this paper, we perform a planar adaptive angle-Doppler compensation(P-A2DC) on the clutter training snapshots. “Adaptive” means it's unnecessary to have a prior knowledge of system measurements, which maybe erroneous or missing. Besides, the planar array has an extra elevation resolution, so the compensation is three-dimensional. The simulations follows show that P-A2DC can receive promising performance.

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.

Space-time adaptive processing (STAP) is a powerful technique for detecting slowly moving targets in strong clutter. However, STAP performance is limited when used on radars with small apertures. Past research suggests polarization's potential to effectively discriminate between targets and clutter, thereby improving detection performance. We evaluate two methods of incorporating polarization into STAP, modeled after the polarimetric matched filter (PMF) and the polarimetric whitening filter (PWF). Our analysis serves to unify various proposed approaches to combining STAP and polarization, and benchmarks the benefits of polarization to GMTI detection. Using measured clutter and target characteristics from published sources as a baseline, we found that the PMF technique offers up to 6-dB improvement over STAP. This significant advantage is realized in the center of the clutter spectrum, and the impact on minimum detectable velocity (MDV) is modest. However, the PWF offers increased performance over the entire Doppler spectrum, and can be easily implemented as a STAP post-processor.

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.

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.

Power efficiency - performance relative to power - is one of the most important concerns when designing RADAR processing systems. This paper analyzes power and performance trade-offs for a typical Space Time Adaptive Processing (STAP) application. We study STAP implementations for CUDA and OpenMP on two architectures, Intel Haswell Core I7-4770TE and NVIDIA Kayla with a GK208 GPU. We analyze the power and performance of STAP's computationally intensive kernels across the two hardware testbeds. We discuss an efficient parallel implementation for the Haswell CPU architecture. We also show the impact and trade-offs of GPU optimization techniques. The GPU architecture is able to process large size data sets without increase in power requirement. The use of shared memory has a significant impact on the power requirement for the GPU. Finally, we show that a balance between the use of shared memory and main memory access leads to an improved performance in a typical STAP application.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

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.

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.

A novel training samples selection method is proposed to improve the performance of space-time adaptive processing in non-homogeneous environments, where training samples are contaminated by interference target signals (outliers). First, the outlier component is removed from the sample covariance matrix based on clutter covariance matrix reconstruction, which utilises the clutter Capon spectrum integrated over a sector separated from the location of outlier. Secondly, the integral is calculated approximately by discrete sum method. Finally, the reconstructed clutter covariance matrix is combined with the generalised inner products algorithm to form the proposed statistics and the contaminated training samples are eliminated. Simulation results validate the effectiveness of the proposed 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 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.