Robust short-range clutter suppression algorithm for forward looking airborne radar

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

We address the problem of training data corruption in space-time adaptive processing (STAP) for ground moving target indication (GMTI) radar scenarios characterized by high densities of ground targets. A site-specific clutter simulation is used to demonstrate the impact that target signals in the training data have on STAP performance. Measured MCARM data results are presented that reveal similar performance trends as those observed in the simulations. A strategy for mitigating the deleterious effects of targets in the training data using a priori knowledge of the radar environment (e.g., locations of roads) to edit the training data is presented.

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.

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.

Space-time adaptive processing (STAP) is an effective tool for moving target detection. Conventional STAP methodologies process the angular and Doppler two dimensional data vector. In practical applications, adjacent range cells are statistically dependent due to filtering, since the point spreading function of a target is not an ideal delta function. In this paper, a novel approach incorporating range (fast time) information in STAP is presented for clutter rejection, which we term space-time-range adaptive processing (STRAP). This method takes advantage of the correlation information of neighboring range cells. Therefore, the stationary clutter can be suppressed better compared with traditional STAP algorithms ignoring fast time information, resulting in more effective moving target detection. The validity of the STRAP algorithm is verified by the experiments of processing the real measured data of the three-channel X-band radar and MCARM radar systems.

Utilizing digital beam forming (DBF) on transmit and receive, the multiple-input multiple-output (MIMO) radar can be applied to ground moving target indication (GMTI) and can provide an efficient way to improve the GMTI performance. The operating pattern is very significant for the MIMO radar GMTI. For the traditional GMTI pattern, there is a conflict of the wider swath and narrower clutter bandwidth. The observation area can be broadened with MIMO techniques by scanning. This paper designs an effective pattern with the simultaneous transmission of multi-phase-centre multi-sub-band signals as well as beam scanning in each pulse, which provide an efficient way to settle the conflict. By beam scanning in pulse, the new pattern has wider azimuth swath, shorter revisiting period, larger coherent processing interval (CPI) number and higher SCNR. By MIMO technology, more equivalent phase centres and longer efficient baselines can be formed, so the performance of GMTI can be improved. The principle of space-time-frequency adaptive processing (STFAP) is analysed, simulation results are presented to prove the superiority of MIMO radar in clutter suppression and GMTI. Analysis of the results indicates improved detection performance with the new pattern compared to the conventional approach.

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.

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.

This chapter is concerned with the study of signal processing techniques and operational strategies for passive radar systems mounted onboard moving platforms and aimed at ground moving target indication (GMTI) applications. Specifically, the attention is focused on space-time adaptive processing (STAP) methodologies for systems equipped with multiple channels on receive. The spatial degrees of freedom are adaptively exploited for space-time clutter cancellation and slow-moving target detection and direction of arrival estimation. Specific solutions are devised and integrated into the typical signal processing architecture of passive radar to address the main limitations deriving from the passive bistatic framework.

This paper is concerned with issues and techniques associated with the development of both optimal and adaptive (data dependent) reduced-rank signal processing architectures. Adaptive algorithms for 1D beamforming, 2D space-time adaptive processing (STAP), and 3D STAP for joint hot and cold clutter mitigation are surveyed. The following concepts are then introduced for the first time (other than workshop and conference records) and evaluated in a signal-dependent versus signal independent context: (1) the adaptive processing region-of-convergence as a function of sample support and rank, (2) a new variant of the cross-spectral metric (CSM) that retains dominant mode estimation in the direct-form processor (DFP) structure, and (3) the robustness of the proposed methods to the subspace leakage problem arising in many real-world applications. A comprehensive performance comparison is conducted both analytically and via Monte Carlo simulation which clearly demonstrates the superior theoretical compression performance of signal-dependent rank-reduction, its broader region-of-convergence, and its inherent robustness to subspace leakage.

An adaptive coding-angle-Doppler clutter suppression approach with extended azimuth phase coding array

Traditional space-time adaptive processing (STAP) is a strategy for clutter suppression in airborne radar, which requires a large number of computational complexity and secondary data. In order to address the problem, reduced-dimension (RD) STAP is generally used. We propose a novel RD STAP through searching the best channels as the auxiliary channels to cancel the interference. Based on the estimation of the clutter Fourier basis vectors offline, a parameter named angle-Doppler correlation coefficient (ADC 2) is constructed to evaluate the capability of each auxiliary channel in clutter suppression, and the best sets of RD channels can be selected. The proposed algorithm can achieve the best detection performance with the fixed number of auxiliary channel. When the degrees of freedom (DOF) are restricted to a small value, only one auxiliary channel is needed to guarantee the SINR loss less than 3 dB. Therefore, the requirement of the training sample can be reduced, which makes the proposed approach more suitable for the heterogeneous clutter environments.

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 convergence nature of conventional conjugate gradient (CG) algorithm often result in degraded system performance. This paper presents a new knowledge-aided CG (KACG) approach to the cancellation of clutter echoes in modern AEW radar systems equipped with digital beamforming. KACG use finite precision knowledge of the radar system parameters and flight geometry knowledge to perform initialization. Results of experiments indicate that the improvement in convergence can be substantial. Compared to the original CG approach, KACG can be regarded as practical solutions for robust and effective space-time adaptive processing (STAP).

For airborne radar, Space-time adaptive processing (STAP) is the key technique to suppress both clutter and interference and detect moving target in strong ground clutter. Monopulse technique after STAP is used to estimate target angle. However, with this adaptive processing, the typical monopulse technique is no more effective because the main beams are distorted, that may lead to angle errors. This paper is concerned with the main beam distortion that results from clutter suppression by STAP. Generalized monopulse is extended to a space-time adaptive architecture to improve the accuracy of angle estimation. Simulation results show that performance of the proposed method is superior to that of the typical method.