Pre-Compensation Clutter Range-Dependence STAP Algorithm for Forward-Looking Airborne Radar Utilizing Knowledge-Aided Subspace Projection

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.

Clutter suppression in an forward-looking airborne radar is different from that of an side-looking airborne radar. The clutter model of forward-looking airbore radar with amplitude and phase errors is proposed. Investigations show that the rank and spectrum of the clutter covariance matrix depends on the amplitude and phase error which decreases the performance of reduced-dimension space-time adaptive processing(STAP) algorithm. The effect of the amplitude and phase errors on clutter spectrum is that random two-dimensional spectrum perturbation is added to the idea clutter spectrum. The numeric results proof the proposed theorem is reasonable.

It is known that the knowledge of sparsity of data is valuable to improve the convergence of space-time adaptive processing (STAP) algorithm in airborne radar. Recently, a STAP algorithm was proposed where the sparsity of beam-Doppler pattern is exploited to achieve good performance. However, a matrix inversion operator is involved in this algorithm, which results in high computational burden. In this paper, we present a novel STAP algorithm which is free of matrix inversion. Precisely, the filter weight vector is first expressed by introducing an intermediate vector. The intermediate vector is then derived iteratively by employing CG techniques under some simple assumptions. The filter weight vector can thus be computed by the derived intermediate vector. Therefore, the proposed algorithm avoids the matrix inversion and achieves low computational complexity. Numerical simulation results are provided to illustrate the performance and superiority of the proposed algorithm.

The performance of the optimal and reduced-dimension Space-Time Adaptive Processing (STAP) algorithms for the airborne phased array radar is investigated in this paper. The clutter suppression in the mainbeam and the sidelobe clutter regions and the computation load are analyzed and compared between the optimal and two kinds of typical reduced-dimension STAP algorithms, i.e. the 2-D temporal-spatial adaptive algorithm (TSA) and the m-bins Doppler transform-space adaptive processing (mDT-SAP) algorithm. Based on the performance analysis of these STAP algorithms, a new clutter suppression strategy is proposed with consideration of actual clutter environment and the spatial channel and temporal pulse restricted system, which can detect the target more effectively and implement more easily for the airborne phased array radar.

The clutter spectrum depends highly on range for forward-looking radars, which results in heterogeneous ground clutter in space-time adaptive processing (STAP). A new method for the range-dependence of clutter spectrum is proposed. The method firstly obtains a transform matrix by fitting the spatial steering vector of the range gate under test with that of secondary range gate. The transform matrix is then applied to spatial data at a given Doppler gate to compensate clutter spectrum, thus achieving an identical clutter spectrum. The conventional STAP could exploit the compensated data for the clutter suppression. Simulation results show a good improvement in processor performance. (4 pages)

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.

Interference covariance matrix estimations and computational complexity are two main concerns in space-time adaptive processing (STAP). This paper deals with the two problems by exploring a structured covariance matrix. First, a conjugate gradient iterative algorithm (CGIA) with reduced computational complexity is presented, which is based on FFT by using a block Toeplitz with Toeplitz block (BTTB) structure of the interference covariance matrix. To ensure the convergence of CGIA, an iterative BTTB (IBTTB) covariance matrix approximation is also proposed. Based on the approximated BTTB matrix, the corresponding STAP algorithm provides superior and robust performance both in limited sample support and in the presence of system errors.

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.

This research presents two new space-time adaptive processing (STAP) algorithms; a two-dimensional non-statistical method and a hybridisation of this approach with statistically based methods. The non-statistical algorithm developed here allows filtering of uncorrelated interference, such as discrete interferers, within the range cell of interest. However, the performance of these algorithms in homogeneous correlated interference scenarios is inherently inferior to traditional statistical STAP algorithms. The proposed hybrid algorithm alleviates this drawback by implementing a second stage of statistical adaptive processing. This paper illustrates the advantages of using a two stage adaptive process to combine the direct data domain and statistical algorithms. The work presented in this paper brings together two different aspects of STAP research: statistical and direct data domain processing. In doing so, this research fulfils an important need in the context of practical STAP processing.

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.

A robust STAP method for airborne radar based on clutter covariance matrix reconstruction and steering vector estimation

Clutter suppression for airborne phased radar with conformal arrays by least squares estimation

The nonhomogeneous clutter is one of the biggest challenge for ocean remote sensing radar system such as high frequency hybrid sky-surface wave radar. Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the training samples in adjacent range gates are not always independent and identically distributed with the cell under test in the heterogeneous environment. So the estimation of the clutter covariance matrix will be inaccurate and degrade the STAP performance. In this paper, a novel training sample selection method based on information geometric means is proposed to choose the more homogeneous secondary data containing the precise clutter information of cell under test. The simulation results of selection performance is conducted to show the advantages of the Information geometric mean based estimators compared with the existing estimators. Combined with joint domain localized STAP, the effectiveness of the proposed method is verified by experimental data. The results show the proposed method outperforms conventional STAP method and the nonhomogeneous clutter can be greatly suppressed.

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.

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.