Space-time-range three dimensional 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.

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

Due to the need for adequate statistically homogeneous training, full-dimensional space-time adaptive processing (STAP) is well accepted to be impractical. Several previous works have addressed this issue by reducing the adaptive degrees of freedom (DoF), in turn reducing the required training. In this paper, we introduce a new multistage STAP approach that significantly reduces the required sample support while still processing all available DoF. The multistage fast fully adaptive (FFA) scheme draws inspiration from the butterfly structure of the fast Fourier transform. It uses a “divide-and-conquer” approach by creating several smaller STAP problems but then combines the outputs of each problem adaptively as well. The reduction in required sample support rivals currently available reduced DoF algorithms. We also develop three variants of the algorithm, including one that uses random subdivisions of the original STAP problem.We test the efficacy of the algorithms developed via simulations based on simulated airborne radar data and measured high-frequency surface wave radar data. The results show that for simulated homogeneous data, the performance of the FFA approaches is comparable to that of available STAP algorithms; however, with measured data, the FFA approach provides significantly better performance.

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.

The problem of space-time adaptive processing (STAP) using a circular array is considered. A key part of STAP is the estimation of the space-time covariance matrix of the received data. The conventional method of doing this can be shown to cause performance degradation at short ranges. We present a method based on steering vector interpolation to remedy this problem. The method applied linear transformations to the data from adjacent range cells. the transformed data are then used to form the sample covariance matrix. Numerical examples illustrate significant performance improvement when using the transformed rather than the original data.

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 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 spectral moments (average Doppler frequency and Doppler spectral width) of radar echoes are closely related to the type of meteorological targets. Pulse Pair Processing (PPP) method is used to estimate the spectral width in the conventional airborne weather radar (WXR). However, the PPP method has a large estimation error when the signal-to-noise ratio (SNR) of the radar echoes is low in the scenarios clear air turbulence (CAT). Thus the current WXR cannot detect CAT effectively. The problem can be solved by introducing Space Time Adaptive Processing (STAP), in which the SNR is improved via space-time integration. However, the full-dimension optimum STAP suffers from the curse of dimensionality. In the paper, we reduce the dimension of the problem by random sampling, i.e. by projecting the data into a random d-dimensional subspace. Accordingly, it offers significant computational savings permitting possible real time solutions. The simulation results show that the random projected STAP estimator is better than PPP when the SNR is low. And it has lower computation load but only with minor performance degradation compared with the optimal STAP.

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.

ABSTRACTThis study investigated and compared the practical methods used for the efficient Field- Programmable Gate Array (FPGA) implementation of space-time adaptive processing (STAP). The most important part of calculating the STAP weights is the QR decomposition (QRD), which can be implemented using the modified Gram-Schmidt (MGS) algorithm. The results show that the method that uses QRD with less computational burden leads to a more effective implementation. Its structure was parameterised with the vector size to create a trade-off between the hardware and performance factors. For this purpose, QRD-MGS algorithm was first modified to increase the speed, and then the STAP weight vector was calculated. The implementation results show that decreasing the vector size decreases the resource utilisation, computational burden and the consumption power. While the computation time increases slightly, the updated rate of the STAP weights is maintained. For example, the STAP weights in a system with 6 antenna arrays, 10 received pulses and 200 range samples computed in 262 µs using a vector size of 17 on the Arria10 FPGA that has a maximum of 155 µs correlates to the QRD-MGS algorithm and 107 µs correlates to the other parts. Therefore, QRD-MGS algorithm is the most important component of the calculation of the STAP weight vector, and its simplification leads to efficient implementation.

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.

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.

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

Multichannel synthetic aperture radar ground moving target indication has been widely used and get great attention nowadays. Traditional adaptive methods like space-time adaptive processing (STAP) need to calculate and search the maximum of the test statistic pixel by pixel over a large range of radial velocity, which will cause great computational burden. Nonadaptive methods like displaced phase center antenna (DPCA) will cause signal loss of moving targets, thereby degrading the performance of moving targets detection and radial velocity estimation. To improve the problem mentioned above, this article proposes a novel two-step scheme based on greatest of (GO)-DPCA and Local STAP. In step I, GO-DPCA utilizes the complex images of the channels with different baselines for joint moving target detection. Then, in step II, only the local data of moving targets detected in step I needs to be extracted from the original image domain for clutter suppression and radial velocity estimation simultaneously by STAP. Since the processing gain of moving targets depends on the radial velocity and baseline length, the detection performance of moving targets with different radial velocity can be improved by proposed GO-DPCA compared to conventional DPCA. Considering the moving targets usually distribute sparsely in the scene, step II can avoid most computational burden caused by processing pixel by pixel. Finally, the experimental results of both simulated and acquired data are given to validate the effectiveness of the proposed method.

High-dimensional multi-sensor radar data suffers from the well known curse of dimensionality. For example, in radar space time adaptive processing (STAP), training data from neighboring range cells is limited, since the statistical properties vary significantly over range and azimuth. Therefore, precluding straightforward implementation of standard detectors, for example, the whitening minimum variance distortionless response filter. Using random projections, we can reduce the dimension of the radar problem by random sampling, i.e. by projecting the data into a random d-dimensional subspace. The Johnson-Lindenstrauss (JL) theorem provides theoretical guarantees which explicitly states that the low dimensional data after random projections is only very slightly perturbed when compared to the data from the original problem in an l 2 norm sense. Random projections offers significant computational savings permitting possible real time solutions, however, at the cost of reducing the clairvoyant SINR for radar STAP. To alleviate this issue of SINR loss, we use localized random projections where the random projection matrix incorporates the look angle information, thereby minimizing the noise and interference effects from other angles, and increasing the SINR. We show that the resulting detector is CFAR, and the transformation matrix satisfies all the necessary conditions for the the JL theorem to hold.