A Low Complexity Space-Time Adaptive Processing with Sparse Constraint based on Conjugate Gradient Techniques

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 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.

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.

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.

Space-time adaptive processing (STAP), previously developed in the field of sensor array processing and applied to radar signal processing, has already been shown to exhibit potential when applied on a small scale to multidimensional functional magnetic resonance imaging (fMRI) data. In this study, a recursive version of the STAP algorithm, the method of steepest descent, is introduced for application to fMRI data. It is believed to be more suitable for adaptation to larger data sets than is the previous STAP implementation using matrix inversion. Computer simulations incorporating actual MRI noise indicate that it exhibits an improvement over the previous matrix inversion method in detecting activations.

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.

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.

Space-time adaptive processing (STAP) algorithms typically consist of a data transformation step to reduce the number of degrees of freedom and a sampling step wherein radar returns from adjacent range bins are used to estimate interference statistics. The reduction in the number of degrees of freedom, inadequate sample support, presence of target in sampled data, and range dependence of interference are some of the main reasons for STAP performance loss. In this paper, we present an approach to target detection and localization that mitigates these performance losses using the well-known stochastic realization algorithm from system identification theory. We first identify a state space model from the radar return data in range-pulse domain for a given range bin, and then perform detection and localization using the identified state space matrices. As interference statistics are not directly computed and since there is no sampling from adjacent range bins, this approach is more robust to sample support issues, target in training and range dependence of clutter. A numerical comparison of the approach with beam-space post-Doppler STAP using simulated data is given.

The space-time adaptive processing(STAP) technique adaptively suppresses clutter jointly in the space domain and time domain, but in practice, due to the lack of sample quantity, the performance of this technique suffers a serious loss. In contrast, the knowledge-aided space-time adaptive processing(KA-STAP) algorithm can improve the estimation accuracy of the clutter covariance matrix by using a priori knowledge. In this paper, by using the cycle characteristic of the clutter covariance matrix as the priori knowledge, the spatial circular matrix, the temporal circular matrix and the spatial-temporal circular matrix are constructed. And use the Least-Mean-Square(LMS) criterion to calculate the coefficients and then integrate the above matrices. Compared with the traditional STAP method, the KA-STAP using the cyclic property as a priori knowledge has about 1 dB of output signal-to-clutter-noise(SCNR) improvement.

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.

In this paper, a space-time clutter model with non-Gaussian statistics for varying airborne bistatic radar scenarios is presented, which can be widely employed to assess the performance of bistatic space-time adaptive processing (STAP) algorithms in realistic clutter environments of interest. To extract the fundamental properties of bistatic clutter, i.e. the range dependency of bistatic clutter spectrum and the clutter power variation in range-angle cells, a new approach of coordinate system conversion is initiated into formulating bistatic geometrical model by us, and the bistatic non-Gaussian amplitude clutter representation method based on compound model is introduced. The bistatic configuration parameters of Multi-Channel Airborne Radar Measurement (MCARM) experiment and the experimental non-Gaussian clutter data are used to validate the provided models, and the results manifest that the proposed bistatic clutter model is reliable and apt to accurately shape the space-time clutter spectrum tied up with specific airborne bistatic radar scenario and characterize the heterogeneity of clutter amplitude distribution in practical clutter environments.

The Mountaintop Program is an ARPA/NAVY sponsored initiative started in 1990 to study advanced processing techniques and technologies required to support the mission requirements of next generation airborne early warning (AEW) platforms. Central to the effort is a surveillance radar measurements program executed from various mountaintop locations including field sites at the White Sands Missile Range (WMSR), New Mexico and the Pacific Missile Range Facility (PMRF), Hawaii. The program is collecting data to support the evaluation of space-time adaptive processing (STAP) algorithms and the characterization and modeling of monostatic and bistatic scattering. Some of the data collected is hosted in CREST, the Common Research Environment for STAP, at the Maui High Performance Computing Center (MHPCC) and is accessible to the digital signal processing community via the World Wide Web. A subset of that data has been provided for inclusion in the IEEE Signal Processing Information Base at Rice University. This paper includes a discussion of program objectives and test segments and a description of the program's assets, field sites, and data product. A companion off-line demonstration of AEW signal processing concepts, using Mountaintop data available at the MHPCC, is planned.

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

In this Letter, the author proposes a novel space–time adaptive processing (STAP) algorithm by enforcing sparse constraint on the beam-Doppler patterns for clutter mitigation when the number of training data is limited. By exploiting the sparsity of the beam-Doppler patterns of the STAP filter, the proposed algorithm formulates the filter design as a mixed l 2-norm and l 1-norm minimisation problem. Moreover, the proposed algorithm develops an adaptive approach to update the regularisation parameter. Simulation results illustrate that the proposed algorithm outperforms the traditional STAP algorithms in a limited sample support.

Stealth target becomes serious growing threats to the military radar, especially with the recently sub-Nyquist scattering wave deception jammer (SN-SWDJ) which has not countering solution yet, because of the fast sampling rate. This paper engages in improving detection of stealth target (F-117A) under SN-SWDJ effect by using stratospheric balloon-borne bistatic radar system with higher aspect vision. The potential problems of proposed scheme come from heterogeneity caused by sudden gusts of wind, and on the other hand, stealth target is always included in the array data due to the coupling of SN-SWDJ with target motion. To tackle these problems, we introduce an optimal suppression technique with a novel combination of knowledge-aided space-time adaptive processing (KA-STAP) and fast time robust adaptive beamforming (FTRAB) algorithms. FTRAB is applied to remove stealth target components from the array data, as the initial guess of KA-STAP covariance matrix. However, our proposed scheme has a higher complexity of the covariance matrix inversion. To reduce this complexity, we propose the linear affine combination of the inverse covariance matrices' estimation instead of the covariance matrix itself. Simulations demonstrate that the proposed scheme gives much higher location accuracy and reduces complexity comparing with other algorithms.