Compensation of clutter spectrum for forward-looking radar based on the spatial steering vector fitting

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 authors consider space-time adaptive processing (STAP) when the radar returns are recorded by a conformal antenna array (CAA). The statistics of the secondary data snapshots used to estimate the optimum weight vector are not identically distributed with respect to range, thus preventing the customary STAP processor from achieving its optimum performance. The compensation of the range dependence of the secondary data requires precise knowledge of the array response for any direction of arrival (DOA), and, thus, of the spatial steering vectors (SVs). The authors propose a novel registration-based range-dependence compensation algorithm that gives an accurate estimate of the interference-plus-noise covariance matrix under the hypotheses that calibrated spatial SVs are available only for a small set of DOAs, and that the errors in the model giving the true spatial SV for each DOA are DOA dependent. The performance in terms of signal-to-interference-plus-noise ratio loss is promising.

A generalized sample weighting method in heterogeneous environment for space-time adaptive processing

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 paper describes a multiple-input-multiple-output (MIMO) generalization of space-time adaptive processing (STAP) to mitigate radar clutter subject to multipath propagation between transmit and receive arrays. Multipath clutter can occur when surface returns are multiply scattered, causing significant Doppler frequency and wavenumber spreads. In the worst case, multipath propagation may cause Doppler spread clutter to return via the receiver mainlobe. In such situations, conventional STAP cannot mitigate Doppler spread mainlobe clutter without also suppressing the target. MIMO radar techniques using orthogonal waveforms have been proposed for increasing radar coverage rate often at the expense of employing significantly larger radar time-bandwidth products. In this paper, we consider a MIMO approach wherein conventional radar waveforms are phase-coded to be orthogonal after Doppler processing at the receiver, i.e. in "slow-time". Slow-time MIMO has the advantages of being bandwidth efficient and easily implemented without the need to modify the receiver before range pulse-compression. In this paper, the mathematical formulation of MIMO STAP is developed for both direct-path and multipath clutter scenarios. At each slant range, the MIMO STAP processor operates on a receive sensor, transmitted pulses, and slow-time phase code channel data cube. Coherent combination of the decoded slow-time channels at the receiver permits effective control of the transmit array pattern by receive post-processing as a function of slant range. Simulation results demonstrate SNR gains when a transmit null is steered in the outbound direction towards the multiply scattered clutter.

Space-Time Adaptive Processing (STAP) enables detection of a moving object against the background of strong interference by radar. The fundamental principles of the STAP technique for radar signal processing in particular, detecting slow moving objects against interference. are presented in this paper. The parameter (improvement factor), determining the performance of any linear processor was analysed. A disturbance model has been proposed, for which the dependence determining the performance of any STAP processor has been derived. The results of simulation of the optimal processor performance against the suboptimal processor for three different values of noise to interference ratios in reference channels were presented in the paper.

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.

In this paper, we study the problem of detecting slow-moving targets using space-time adaptive processing (STAP). The construction of the optimum weights at each range implies the estimation of the interference-plus-noise covariance matrix. This is typically done by straight averaging of snapshots at neighboring ranges. However, in most bistatic configurations, snapshot statistics are range dependent. Straight averaging results thus in poor performance. This paper proposes a new method of obtaining an estimate of the covariance matrix using power spectrum registration based on nonuniform frequency sampling technique. Simulation results show a significant improvement in processor performance as compared to conventional STAP methods.

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.

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.

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.

The author addresses the impact of diverse polarisations on clutter statistics in the context of waveform diversity for multi-functional operation from a specific platform as well as for multiple sensing from multiple platforms. A key issue in this context is that of clutter mitigation via the use of diverse waveforms. Classical space–time adaptive processing (STAP) methods for radar target detection can be viewed in the context of a whiten and match filter. To this end, efficient waveforms that lend themselves for such processing are sought. The author specifically considers a statistical analysis of experimental data collected at low grazing angles to validate the fact that vertical transmit–vertical receive (VV) polarised data conform to Rayleigh scatter, whereas horizontal transmit–horizontal receive data do not. Consequently, VV data are suitable for whiten and match processing adopted in conventional radar STAP.

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

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.