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Target detection is one of the most important parts of high-frequency surface wave radar (HFSWR) signal processing, to find targets in noise or clutter and obtain targets' information. However, some factors, such as the influence of clutter and the increase of detection range, will degrade the signal-to-clutter ratio (SCR). In the low SCR scenario, the classical detector of HFSWR, which uses the amplitude of receipt signal only, could hardly detect targets. To solve this problem, this study proposes a novel detector called joint domain localised matrix constant false-alarm rate (CFAR) detector based on receipt signal's multi-dimensional information. This detector employs joint domain localised algorithm to get signal information in angle and Doppler domain, and use information geometry method to map them to Hermitian positive-definite (HPD) matrix space which can be depicted as Riemannian manifold. Then, based on HPD matrix, in the range domain the matrix CFAR detector is built to detect targets. The experiments' results verify that the detector can improve radar detection performance effectively. [ABSTRACT FROM AUTHOR]

Space-time adaptive detection (STAD) is an important research aspect of the bistatic multiple-input multiple-output (MIMO) radar system. The signal and clutter space-time models of the bistatic MIMO radar are described in this study. The knowledge-aided (KA) transform, which utilises the structural properties of the target-free data and the sample covariance matrix, is applied to increase the independent secondary data fourfold in the STAD frame. The performance assessment of the KA–STAD detectors with generalised likelihood ratio test (GLRT), adaptive matched filter (AMF), and Rao rules is presented. The simulation experiments show that the detection and constant false alarm rate performances of the presented KA–GLRT, KA–AMF, and KA–Rao detectors outperform the GLRT, AMF, and Rao detectors for the bistatic MIMO radar system STAD frame in clutter background with limited secondary data. [ABSTRACT FROM AUTHOR]

A novel bi-iterative dimension-reduced space–time adaptive processing (STAP) algorithm for clutter suppression and moving target detection in airborne radar system is proposed. A dimension-reduced processing in both space and time is firstly performed. Then a bi-quadratic cost function is constructed to estimate the dimension-reduced spatiotemporal filter coefficients, which are used for suppressing the clutter and keeping the target signal. An optimal solution to the cost function can be obtained efficiently by a bi-iteration algorithm. Theoretical analysis and computer simulation results illustrate that the algorithm has fast convergence, low computation load and sampling requirements. The results of experiment by using the measured data show the validity of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Clutter presents considerable heterogeneity in forward-looking airborne radar (FLAR) applications and conventional space-time adaptive processing (STAP) methods are sensitive to model mismatch. As a result, when a strong target signal contaminates the training samples, despite the use of guard cells, the performance of conventional STAP methods degrades significantly. In this study, a robust method, which involves reconstructing a target-free covariance matrix and correcting the presumed steering vector to prevent target cancellation in FLAR, is proposed. First, the target-free covariance matrix is reconstructed through integrating the spatial–temporal spectrum over a sector separated from the desired frequency and direction of targets. Subsequently, the mismatch between presumed steering vector and actual steering vector is corrected via quadratic optimisation. In addition, the processing scheme is applied to real-measured clutter data, and the experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Serious security risks and economic losses have increased rapidly under the threat of foreign object debris. Most detection methods employing optical devices are limited in severe weather for low visibility. This study proposes an applicable approach for moving foreign object debris on the basis of space-time adaptive processing. The geometrical model of an airport is introduced whereby the runway and lawn are divided into clutter cells. After calculating the complex amplitude of clutter in each cell, the authors employ the minimum variance power spectrum and degrees of freedom to describe the ground clutter characteristics. Then the space-time filter is designed under the linearly constrained minimum variance criterion to suppress the clutter and detects the moving target. Simulations show that the errors in azimuth and Doppler are both less than −15 dB when SCNR = −20 dB. [ABSTRACT FROM AUTHOR]

Ground clutter of looking-down airborne radar is spread in Doppler because of the platform motion, and thus the interested moving targets may be masked by the ground clutter. In order to enhance the performance of clutter suppression, the clutter characteristics should be analysed in detail. So here, a general clutter model which suits for the conventional-phased array radar, multiple-input-multiple-output (MIMO) radar, and hybrid MIMO-phased array radar, all with arbitrary array shape, whether in monostatic or bistatic applications, is developed. Clutter characteristics under two typical scenarios are presented by simulation examples, simultaneously; the effectiveness of the general clutter model is demonstrated. [ABSTRACT FROM AUTHOR]

As training samples are not identically distributed with cell under test (CUT) in heterogeneous environments, the performance of space–time adaptive processing (STAP) to suppress clutter degrades. To improve the performance of STAP, this study proposes a novel training sample selection method for STAP based on multi-frames. The key feature of the new method is to overcome the deficit of independent and identically distributed (IID) training samples in heterogeneous environments. First, multi-frames are selected according to similarity from continuous frames; second, training samples which are IID to the CUT from current frame are selected; finally, combine the training samples of the current frame and same range samples in reference frame into final training sample set. The proposed method is applied to real-radar data, and experimental results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional space–time adaptive processing (STAP) methods would suffer severely performance loss in the complex clutter environment of an airborne-phased array radar, especially when the estimated clutter covariance matrix (CCM) is corrupted by the interference targets (outliers). In order to improve the clutter suppression in a practical complex clutter, a robust knowledge-aided sparse recovery STAP method for non-homogeneity clutter suppression is proposed. In the proposed method, the spectral profiles of the clutter and outliers are firstly estimated by sparse recovery processing. Then based on the system prior parameters, the clutter mask is constructed to select the space–time steering vectors corresponding to the clutter components. Afterwards the clutter suppression is achieved based on the clutter subspace obtained from the selected space–time steering vectors. Since the clutter and outlier profiles are effectively estimated and distinguished by the knowledge-aided sparse recovery processing, robust clutter subspace estimation can be achieved for clutter suppression. Through the simulated and actual airborne-phased array radar data, it is verified that the proposed method can effectively improve the STAP performance in a non-homogeneous environment. [ABSTRACT FROM AUTHOR]

In the radar systems, such as frequency source, power amplifier, mixer, filter and A/D converter, ageing devices, temperature and environmental changes etc., it is inevitable to introduce wideband amplitude/phase error. These can cause seriously channel mismatch that will affect the performance of digital beam-forming, synthetic aperture radar image, space time adaptive processing (STAP), automatic target recognition (ATR), target detection, clutter/interference suppression, pulse compression results and cancellation ratio etc. So this study mainly discusses that the channel amplitude/phase inconsistency of wideband digital array radar affects the pulse compression, beam forming and so forth. It also researches and analyses channel equalisation method, and the simulation and experimental results based on the experimental data show that the method based on Fourier transform is effectively and convenient. The Fourier transform method is easier for hardware implementation that can be applied to practical engineering. [ABSTRACT FROM AUTHOR]

Detecting ground moving target in bistatic forward-looking synthetic aperture radar (BFSAR) has become increasingly important in military and civil field. Compared with pulse Doppler radar, the long observation time and high resolution of BFSAR will lead to three problems in the received signal: Doppler ambiguity, large range cell migration and Doppler cell migration, which tremendously aggravate the difficulty of ground moving target detection (GMTD). In this study, the authors propose a three-step ground moving target detection method for BFSAR to solve these problems and achieve good performance on GMTD. First, the bulk-deramp filtering and Keystone transform are exploited to suppress Doppler ambiguity and correct range cell migration, respectively. Then, time-division space–time adaptive processing is utilised for clutter suppression, which eliminates the effect of the Doppler cell migration. Finally, modified Wigner–Ville distribution method is applied to further improve the signal to clutter and noise ratio and the moving target can be detected in the integration domain. Simulations are carried out to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to improve the angle accuracy and meet the requirement of side-lobe blanking, the fire-control radar system usually adopts sum beam, azimuth difference beam, elevation difference beam and several protecting antennas simultaneously. A ΣΔG-STAP method based on sum, difference and protecting channels is proposed to improve the performance of ground moving target detection. Moreover, the target is located on the Doppler beam sharpening image by the monopulse angle estimation technique. The effectiveness of the proposed method is verified using the simulated data and measured data. [ABSTRACT FROM AUTHOR]

Conventional 3-dimensional (3D) space-time adaptive processing (STAP) has achieved good performance for non-stationary clutter suppression. However, the performance of 3D STAP rapidly degrades when applied to heterogeneous clutter environment due to the requirement of a large number of independent and identically distributed training samples to estimate the clutter covariance matrix. This study applies an efficient sparse recovery algorithm, i.e. multiple sparse Bayesian learning (MSBL), with tuning, to solve the limited sample problem of 3D STAP in airborne radar. Differing with the conventional 2D sparsity-based STAP, the proposed method utilises the sparsity of clutter in elevation-azimuth-Doppler domain and recovers the 3D clutter spectrum. Whereas in its' large computational load, a fast algorithm extending the relevance vector machine is also considered. [ABSTRACT FROM AUTHOR]

The scattered-wave sweeping frequency jamming occupies many space-time adaptive processing (STAP) degrees of freedom, which results in a serious decline in STAP performance. To solve this problem, an anti-jamming method of STAP based on a bi-phase random-coded signal is proposed. This method utilises the phase-coded signal's ability of waveform agile and anti-jamming. Also, the phase coding is designed as a random sequence, which distinguishes and suppresses the interference by white noise characteristic. Simulation results show that, compared with the STAP method based on a chirp signal, the proposed method improves beamforming robustness and clutter suppression capability under an interfering background. [ABSTRACT FROM AUTHOR]

Conventional space-time adaptive processing (STAP) achieves clutter suppression by combining spatial signals and the temporal pulses. However, extreme slow-moving target cannot be effectively detected by space-time joint filtering in ambiguous and non-broadside clutter environment for airborne-phased array radar. By increasing the polarisation information in clutter suppression, improved moving target detection performance can be obtained by the polarisation-space-time adaptive processing (P-STAP). In this study, the performance of P-STAP for airborne-phased array radar is analysed. The polarised signal model of airborne-phased array radar is firstly established and then clutter suppression principle of P-STAP is derived in detailed. Afterwards, the performance of P-STAP is investigated and compared with conventional STAP in ambiguous clutter environment. It is verified that the P-STAP can outperform the conventional STAP for slow moving target detection by three-domain joint processing, especially for ambiguous and non-broadside clutter environment for airborne-phased array radar. [ABSTRACT FROM AUTHOR]

The most challenging task in phased array radar system is the mitigation or suppression of noise and interferences to enhance the useful signal in radar received signals. Multiple solutions have been proposed by researchers for suppression of unwanted signals or interferences which have highly degraded the overall system performance. Nowadays, space-time adaptive processor (STAP) which is a two-dimensional filtering technique is commonly used in phased array antenna with multiple spatial channels. A detailed study is performed on the well-known adaptive algorithms least mean square (LMS), normalized least mean square (NLMS) and recursive least square (RLS), to suppress the high state sea clutter by updating the adaptive STAP weight vector. However, RLS is considered as the suitable adaptive algorithm in STAP because of its good convergence rate, but has high computational complexity. To overcome this issue, two variants of RLS (QRD-RLS and Inverse QRD-RLS) are also discussed which reduce the computational complexity of the algorithm. MATLAB simulations are done to verify the performance of LMS and RLS algorithms in terms of accuracy and convergence rate. Finally, hardware implementations of STAP-QRD-RLS and STAP-IQRD-RLS adaptive beamformers are done and evaluated in terms of latency, throughput and efficiency. The selected platform is the Virtex-5 field-programmable gate array. [ABSTRACT FROM AUTHOR]

Joint domain localisation (JDL) is a popular reduced-dimension space-time adaptive processing (STAP) technique for clutter suppression in an air-borne radar system. Here, the authors develop an improved JDL method by exploiting persymmetric covariance matrix, referred to as persymmetric joint domain localisation (Per-JDL), in order to make maximum use of training samples and further improve the STAP performance under small training data support. The proposed algorithm is verified to be efficient in training-limited scenarios by simulation results. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) for airborne radar with co-prime arrays is shown to have excellent superiority compared to traditional STAP with uniform linear array radar. However, high arithmetic computational complexity and large amount of training data are required in this approach. This motivates the authors to present a new approach which is relatively low computational load and fast convergence with satisfactory performance. Specifically, a reduced-dimension transformation in Doppler domain is incorporated into the existing approach. The reduced-dimension interference covariance matrix and target steering vector are then achieved by performing the reduced-dimension process, and hence two reduced-dimension STAP filters are designed using the derived interference covariance matrix and target-steering vector. Numerical simulations are carried out to reveal the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper aims to address grating-lobe clutter suppression in a uniform subarray (US) for the airborne radar system. Due to the sparsity at the subarray level and different scanning angles between the inner subarray and the subarray, grating lobes are produced. The grating-lobe clutter then enters the main lobe, defined as main grating-lobe clutter (MGLC). When the target falls into the MGLC with the same Doppler frequency, the clutter cannot be effectively suppressed by the conventional space-time adaptive processing (STAP) algorithm. Hence, we design a transmit beamforming (TB)-STAP algorithm based on a modified phased array (MPA) radar system, where TB suppresses the MGLC, while STAP suppresses the main-lobe and sidelobe clutter; the goal is to maximize the output signal-to-clutter-plus-noise ratio (SCNR). The optimal solution of TB-STAP can be obtained through several iterations, and in each iterative algorithm, the TB is a quadratically constrained quadratic problem (QCQP) solved via the semidefinite relaxation (SDR) technique, and the STAP can obtain a closed solution by the minimum variance distortionless response (MVDR) algorithm. The simulation results show that compared with traditional STAP in PA and multiple-input multiple-output (MIMO) radars, the proposed algorithm in MPA radar greatly improves the output SCNR. [ABSTRACT FROM AUTHOR]

A non‐adaptive space‐time clutter canceller (NSCC) for multi‐channel (MC) synthetic aperture radar (SAR) was proposed. First, a new three‐part range equation was derived on the basis of the two‐dimensional Taylor series expansion. Then, each part of the model was analysed. By compensating of the high‐order coupling part and the compression of the Doppler extension part of the derived equation, the interlaced signal of a moving target and a clutter patch was easily separated in a space‐time domain. The clutter signal in different pulses only contained a constant phase difference. Using radar parameters, the authors constructed a non‐adaptive clutter canceller that prevented traditional space time adaptive processing (STAP) issues, such as secondary sample support, computational complexity burden, and unknown moving target information. Compared with the representative non‐adaptive method, that is displaced phase centre antenna (DPCA), NSCC is robust to a small degree of parameter error. It can be applied when DPCA condition is not satisfied. The effectiveness of the proposed method was validations through simulation. [ABSTRACT FROM AUTHOR]

Highlights • Optimum beam-Doppler region for RD-STAP is data related rather than fixed. • Proposed SCBDS-RD-STAP converges faster than conventional RD-STAP algorithms. • Proposed SCBDS-RD-STAP is more robust to array errors. Abstract State-of-the-art space-time adaptive processing (STAP) algorithms devised in the beam-Doppler domain are confined by fixing the beam-Doppler cells used for adaptation, which may suffer from performance degradation. To overcome this drawback, a novel STAP algorithm in the beam-Doppler domain is proposed. The proposed algorithm adopts a generalized sidelobe canceller structure, and the filter design is formulated as a sparse representation problem by imposing a sparse constraint on the weight vector. As the sparse constraint enforces most of the elements in the weight vector to be zero (or sufficiently small in amplitude), the proposed algorithm can adaptively select the best beam-Doppler cells for adaptation, and thus it falls under the category of reduced-dimension approach. Simulation results illustrate that the proposed algorithm outperforms the existing STAP approaches with fixed beam-Doppler localized processing. [ABSTRACT FROM AUTHOR]

For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condition. It is because of the realistic heterogeneous environment faced by airborne radar. Moreover, one should eliminate contaminated training samples before CCM estimation. Aiming at the problems of the computational complexity and susceptibility to the outlier of the traditional generalized inner product (GIP) method, a clutter subspace-based training sampling selecting method is proposed combined with specific distribution in the space-time plane of clutter spectrum. Theoretical analysis and simulation results verified the proposed method and indicate that the proposed method is easy to construct CCM and has lower computational complexity and sensitivity to outliers.

The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells.

In this study, the effects of non-sidelooking airborne radar clutter dispersion on space-time adaptive processing (STAP) is considered, and an efficient adaptive angle-Doppler compensation (EAADC) approach is proposed to improve the clutter suppression performance. In order to reduce the computational complexity, the reduced-dimension sparse reconstruction (RDSR) technique is introduced into the angle-Doppler spectrum estimation to extract the required parameters for compensating the clutter spectral center misalignment. Simulation results to demonstrate the effectiveness of the proposed algorithm are presented.

In this paper, we propose a low-complexity adaptive reduced-rank interference suppression scheme based on alternating low-rank decomposition techniques for navigation receivers. The proposed scheme, which employs a generalized sidelobe canceler structure, makes use of a projection matrix for rank reduction followed by a reduced-dimension receive filter. An alternating optimization strategy based on recursive least squares is devised to compute the basis vectors of the projection matrix and the receive filter adaptively. Simulation results show that the proposed algorithm achieves a better performance than existing methods with a reduced computational complexity. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of detecting a subspace signal in the presence of spatially and temporally colored disturbance. A new subspace parametric signal model that takes into account a multi-rank subspace structure for the target signal and employs a multi-channel auto-regressive process for the disturbance signal is proposed. Following this model, a new subspace parametric Rao detector (SP-Rao) is developed for training-limited scenarios. Unlike conventional parametric detectors that are designed for only rank-one signal detection, the SP-Rao has a new multi-rank structure with a pairwise successive spatio-temporal whitening and cross-correlation process between the observed signal and each subspace basis vector. Additionally, a non-parametric subspace detector (NSD) is derived based upon a frequency-domain representation of the SP-Rao test statistic. The NSD is distinctively different from conventional subspace detectors, in which the former involves pairwise whitening and cross-correlation between the test signal and each subspace basis vector but the latter employs the whole subspace matrix. Numerical results are presented to illustrate the performance of the proposed subspace detectors in comparison with several leading existing methods, especially in the case of limited data. [ABSTRACT FROM AUTHOR]

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This paper proposes a space-time adaptive processing (STAP)-based approach to enhance the performance of single target tracking in the presence of strong interference. Waveform agile sensing approach is used to improve the performance. The waveform library consists of linear frequency modulation waveforms with varying pulse repetition frequency and pulse width. Multidimensional filtering approach of STAP is applied, to mitigate the clutter and jamming effect. A waveform is selected based on Cramer-Rao lower bound (CRLB) from a bank of waveforms for next scan so as to minimize the mean square error. Stand-off jammer is considered as an electronic counter measure technique. Interacting multiple model probabilistic data association filter is engaged to track single targets. It is evident from the simulation results that proposed approach accomplishes an average position RMSE 62.79 and 56.01% higher compared to CRLB for maneuvering target and benchmark trajectory-2, respectively. [ABSTRACT FROM AUTHOR]

Existing sparse recovery based space-time adaptive processing (SR-STAP) methods discretize the angle-Doppler plane to generate the space-time steering dictionary, which will cause the off-grid problem, resulting in performance loss. This paper proposes an alternative processing model established in the continuous domain. Based on the positive semidefinite (PSD), block-Toeplitz, and low rank properties of the clutter covariance matrix (CCM), the subspace of clutter can be estimated by solving an atomic norm minimization problem. Then, the CCM is directly calculated by the Eigen-decomposition based process. If multiple independently and identically distributed (IID) training samples are available, the proposed method can be easily extended to the multiple measurement vector (MMV) model. With the joint sparsity and same subspace assumptions of different samples, MMV based atomic norm minimization STAP (ANM-STAP) method can further increase the estimation accuracy of the clutter subspace, and thus improve the clutter suppression performance. Simulation results demonstrate that, in comparison with typical SR-STAP methods, the proposed method can avoid the off-grid problem, achieve more accurate CCM estimation, and enjoy better clutter suppression performance with fewer training samples than the statistic STAP method. [ABSTRACT FROM AUTHOR]

For the degradation of target detection performance in space-time adaptive processing (STAP) due to non-homogeneous training samples contaminated by target-like signals, a non-homogeneous training sample detection method based on prior information and sparse recovery is proposed. The sparse representation coefficient of cell under test (CUT) is recovered using focal underdetermined system solver (FOCUSS). A "sparse filter" is constructed based on radar system parameters.The target and "pseudo point" signals are filtered out by "sparse filter", and the clutter covariance matrix is estimated. The generalized inner product (GIP) method is integrated to eliminate the contaminated training samples. Simulation analyses show that the proposed method can effectively eliminate the contaminated training samples and improve the target detection performance of STAP in non-homogeneous environment. [ABSTRACT FROM AUTHOR]

• Joint allocation approach of the transceiver spatial-temporal resource is developed. • A joint model of transceiver array, pulse, waveform and receive weight is formulated. • The resultant nonlinear issue is decomposed into four independent sub-problems. • The transmit selection matrix and receive selection vector are acquired with SCA. • The transmit WCM is optimized by exploiting the charnes-cooper transformation. Aiming to address the issue of target detection performance degradation due to limited resources of airborne multiple-in multiple-out (MIMO) radar, a joint allocation method of transmit-receive spatial-temporal resources is developed to ameliorate the detection performance of MIMO radar-based space-time adaptive processing (STAP). A MIMO-STAP model containing space-time resources to be configured is firstly constructed. Then, with the criterion of maximizing the output signal-to-clutter-noise ratio (SCNR), a problem to jointly design the transmitting and receiving elements, transmitting pulses, baseband waveform, and receiving weight is formulated. To tackle the resultant complex nonlinear joint optimization issue, it is decomposed into four independent sub-problems based on the alternating iteration strategy, then the sub-problems are efficiently solved by corresponding optimization techniques, i.e., the optimal antenna pulse subset can be selected by employing the sequential convex approximation (SCA) method, the transmitting waveform is optimized using the semi-positive definite program (SDP) and randomization method, and the receiving weight is designed under the minimum variance distortionless response (MVDR) criteria, which work iteratively to achieve an effective solution to the joint optimization problem. Extensive simulation results verify the effectiveness of the developed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results.

The nonhomogeneous clutter is a major challenge for ship detection in high-frequency mixed-mode surface wave radar. In this paper, a geometric barycenter-based reduced-dimension space-time adaptive processing method is proposed to suppress the clutter. Given the measured dataset, the range correlation of sea clutter is first investigated. Then, joint domain localized processing is applied to solve the training samples starve scenario in a practical system. The geometric barycenter-based training data selector is presented to select valid training samples and improve the accuracy of the clutter covariance matrix estimation. Finally, the validity of the proposed method is verified using the experimental data and the results show that it outperforms the conventional method in the nonhomogeneous environment of a practical system.

In space-time adaptive processing (STAP), the selected training samples should have the same covariance matrix as the clutter of the cell under test (CUT). The traditional methods usually select samples whose waveforms are similar to that of the CUT. We notice that completely dissimilar waveforms may have the same covariance matrix. As a result, many valid samples are lost in traditional methods. So we propose a training samples selection method based on system identification. The proposed methods select samples with similar covariance matrices instead of similar waveforms. First, a samples selection model based on system identification is proposed. Then, the neural network is used to identify the clutter model of the CUT. Finally, samples are selected according to the output variance. Compared with the methods in [1, 2, 3, 4], the proposed method has the following advantages: (1) More than twice the valid training samples can be obtained; (2) The clutter suppression performance can be improved more than 2 dB for the measured data. [ABSTRACT FROM AUTHOR]

The most effective method for overcoming the interference vulnerability of global navigation satellite system (GNSS) receivers is to use an adaptive array antenna which has the capability of nulling or beamforming to a certain direction. The space–time adaptive processing (STAP) algorithm, which is very effective in signal processing of the array antenna for anti‐interference, is studied. The phenomenon of pseudorange error in STAP is analysed with a new superposition method of linear line correlation functions. From this analysis, a new symmetric STAP algorithm, which uses an appropriate constraint condition for GNSS signals, is proposed to completely prevent this pseudorange error. The new STAP algorithm performance is verified and confirmed by simulations and experiments with a four‐element array antenna. According to the simulation results, the new STAP algorithm has no pseudorange error, whereas the already existing STAP algorithm has an error of 1.6 m. Also experiments with four RF channels analogue‐to‐digital converter data and a real‐time receiver confirm the effectiveness of their precise STAP algorithm which is easy to implement. [ABSTRACT FROM AUTHOR]