State Space System Identification Approach to Radar Data Processing

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.

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.

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.

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.

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.

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.

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.

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.

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.

We consider space-time adaptive processing (STAP) in a bistatic radar configuration and 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 STAP processor from achieving its optimum performance. The compensation of the range-dependence of the secondary data requires the knowledge of the clutter power spectrum locus. This paper proposes a new RANSAC-based method for estimating, in the case of CAAs operating in arbitrary bistatic configurations, the clutter power spectrum locus or, equivalently, the flight configuration parameters. Based on the knowledge of this locus, we can perform a range-dependence compensation of the snapshots to obtain an accurate estimate of the clutter covariance matrix. End-to-end performance analysis in terms of signal to inference-plus-noise ratio loss shows that the method yields promizing 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.

Multistatic moving target detection in unknown coloured Gaussian interference

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.