STAP Algorithm Research Articles

Airborne Radar Space–Time Adaptive Processing Algorithm Based on Dictionary and Clutter Power Spectrum Correction

Sparse recovery space–time adaptive processing (SR-STAP) technology improves the moving target detection performance of airborne radar. However, the sparse recovery method with a fixed dictionary usually leads to an off-grid effect. This paper proposes a STAP algorithm for airborne radar based on dictionary and clutter power spectrum joint correction (DCPSJC-STAP). The algorithm first performs nonlinear regression in a non-stationary clutter environment with unknown yaw angles, and it corrects the corresponding dictionary for each snapshot by updating the clutter ridge parameters. Then, the corrected dictionary is combined with the sparse Bayesian learning algorithm to iteratively update the required hyperparameters, which are used to correct the clutter power spectrum and estimate the clutter covariance matrix. The proposed algorithm can effectively overcome the off-grid effect and improve the moving target detection performance of airborne radar in actual complex clutter environments. Simulation experiments verified the effectiveness of this algorithm in improving clutter estimation accuracy and moving target detection performance.

A fast off-grid stap algorithm based on marginal likelihood maximization

A fast off-grid stap algorithm based on marginal likelihood maximization

A novel dictionary-corrected sparse recovery stap algorithm based on prior knowledge

A novel dictionary-corrected sparse recovery stap algorithm based on prior knowledge

Dimensionality reduction bistatic radar clutter suppression technique based on three-dimensional cross-beam

Aiming at the problem that the non-stationary of airborne bistatic radar clutter reduces the performance of traditional clutter suppression algorithm, this paper first formulates airborne bistatic radar clutter model, and then analyzes the bistatic radar clutter characteristics in four typical combat scenarios. In addition, in order to reduce the computation burden of the STAP algorithm, a three-dimensional cross-beam clutter suppression algorithm is proposed, which uses the transformation matrix to convert the space-time two-dimensional data into azimuth-elevation-Doppler three-dimensional data. By eliminating auxiliary beam clutter data, only the main beam clutter data which plays a major role in dual-basis clutter suppression is retained to form a three-dimensional cross-beam for local adaptive clutter suppression. The proposed algorithm can greatly reduce the amount of calculation and the demand of training samples, while trying to ensure that the clutter suppression performance does not degrade. The effectiveness of the proposed algorithm is verified by analyzing the clutter suppression effect and computation amount of various algorithms in typical combat scenarios. Therefore, the algorithm has great potential in the applications of airborne bistatic and non-stationary clutter suppression.

An Improved Iterative Reweighted STAP Algorithm for Airborne Radar

In recent years, sparse recovery-based space-time adaptive processing (SR-STAP) technique has exhibited excellent performance with insufficient samples. Sparse Bayesian learning algorithms have received considerable attention for their remarkable and reliable performance. Its implementation in large-scale radar systems is however hindered by the overwhelming computational load and slow convergence speed. This paper aims to address these drawbacks by proposing an improved iterative reweighted sparse Bayesian learning algorithm based on expansion-compression variance-components (ExCoV-IIR-MSBL). Firstly, a modified Bayesian probabilistic model for SR-STAP is introduced. Exploiting the intrinsic sparsity prior of the clutter, we divide the space-time coefficients into two parts: the significant part with nontrivial coefficients and the irrelevant part with small or zero coefficients. Meanwhile, we only assign independent hyperparameters to the coefficients in the significant part, while the remaining coefficients share a common hyperparameter. Then the generalized maximum likelihood (GML) criterion is adopted to classify the coefficients, ensuring both accuracy and efficiency. Hence, the parameter space in Bayesian inference will be significantly reduced, and the computational efficiency can be considerably promoted. Both theoretical analysis and numerical experiments validate that the proposed algorithm achieves superior performance with considerably improved computational efficiency in sample shortage scenarios.

A fast and gridless STAP algorithm based on mixed‐norm minimisation and the alternating direction method of multipliers

A fast and gridless STAP algorithm based on mixed‐norm minimisation and the alternating direction method of multipliers

Iterative adaptive approach STAP algorithm based on arc antenna array

The radar with arc antenna array has the advantages of all-orientation space scanning, simultaneous estimation of pitch-azimuth angle and good antenna pattern. However, due to the special geometry of the antenna, the echoes from different range cells are not independently and identically distributed (IID) samples, resulting in performance degradation in space-time adaptive processing technology. In this paper, the model of airborne radar with arc antenna array is established, and the clutter distribution is analyzed. The conclusion is that the nonlinear variation of spatial angular frequency with array element number results in the non-uniform clutter distribution in different range cells. Therefore, the traditional space-time adaptive processing is not fit for the target detection of arc array radar. Hence, the arc array-based iterative adaptive approach (IAA) algorithm is studied. The IAA algorithm is utilized to estimate the space-time spectrum, which can distinguish clutter and targets. Simulation experiments verify the effectiveness of the IAA algorithm in arc array radar.

Comparison of the performance of adaptive space-time processing against the background of alternative methods

Description and successive stages of the STAP algorithm were characterized in this article. The ability to detect an object by using 6-element antenna array without space-time processing and using the STAP technique were compared and shown. The simulation results showed that the implemented STAP algorithm successfully coped with target detection. In addition, the possibilities of object detection using the STAP technique were compared and shown against the background of other DPCA and ADPCA algorithms. Keywords: space-time adaptive processing, STAP, DPCA, ADPCA, radar signal processing, radar

Space-Time Adaptive Processing Techniques (STAP) for Mitigation of Jammer Interference and Clutter Suppression in Airborne Radar Systems: A MATLAB Implementation-based Study

Radar Systems are used for the detection of objects by transmitting the electromagnetic waves in the free space. They operate in environments where there is a high possibility that the desired echo signal interferes with the signals from other sources. These signals include clutters and jammer signals. The jammer is a device that continuously emits the wideband radio signals in the radar environment, to saturate the receiver with noise or false information. Thus, the total received signal has three components- returns from target, clutter, and jammer combined i.e. it is a three dimensional signal. The use of conventional signal processing techniques is not desirable, as they cannot separate the desired echo signal from the other components, because the statistics of these components present in the received signal is not known. This problem needs to be accounted for, in airborne surveillance radars, as they have to identify and locate the targets in multiple interference environments. The Space-Time Adaptive Techniques (STAP) is a combination of spatial and temporal filtering that can nullify the jammer signal, and recognize the slow-moving targets. These techniques filter the signal in the angular and the Doppler domain for suppressing the unwanted signals. This paper presents a theoretical study of space-time adaptive coding techniques, and the MATLAB implementation of STAP algorithms; namely, SMI, DPCA, and ADPCA, to suppress clutter and jammer interference in the received pulse.

An Off-Grid STAP Algorithm Based On Local Mesh Splitting With Bistatic Radar System

The purpose of this letter is to solve the space-time off-grid problem caused by the extremely inhomogeneous clutter spectrum under the space/airborne bistatic system. For the estimation of inhomogeneous clutter spectrum, the method of sparse recovery (SR) is usually adopted. However, performance of sparse recovery may degrade since the real clutter point often does not fall in the center of the space-time grid. To deal with this issue, a local mesh splitting algorithm is proposed in this letter. First, select the space-time vector atoms which are most relevant to the residuals by subspace projection. Then, iterative local mesh splitting is performed around the selected atoms to match the real clutter points. Finally, the clutter subspace is reconstructed from all matched atoms selected by global and local iterations. The simulation results show that the method can obtain better accuracy with lower computational effort.

A Two-Stage Space-Time Adaptive Processing Method for MIMO Radar Based on Sparse Reconstruction

AbstractTo effectively suppress clutter and blocking interference for MIMO radar, a two-stage STAP method based on sparse reconstruction is proposed. As interference is sparse in spatial domain, the subspace of it is estimated with only one snapshot by using Orthogonal Matching Pursuit (OMP) algorithm, and the array data is projected onto the complementary subspace of interference. In the sequel, matched-filtering is applied to the output data followed by clutter suppression with temporal and spatial freedom. The clutter suppression is utilized directly to reduced-dimension STAP (RD-STAP) algorithms. Simulation results demonstrate that the proposed method outperforms traditional methods and reduces sample requirement.

Knowledge‐aided STAP with sparse‐recovery by exploiting spatio‐temporal sparsity

In this paper, novel knowledge-aided space-time adaptive processing (KA-STAP) algorithms using sparse representation/recovery (SR) techniques by exploiting the spatio-temporal sparsity are proposed to suppress the clutter for airborne pulsed Doppler radar. The proposed algorithms are not simple combinations of KA and SR techniques. Unlike the existing sparsity-based STAP algorithms, they reduce the dimension of the sparse signal by using prior knowledge resulting in a lower computational complexity. Different from the KA parametric covariance estimation (KAPE) scheme, they estimate the covariance matrix using SR techniques that avoids complex selections of the Doppler shift and the covariance matrix taper. The details of the selection of potential clutter array manifold vectors according to prior knowledge are discussed and compared with the KAPE scheme. Moreover, the implementation issues and the computational complexity analysis for the proposed algorithms are also considered. Simulation results show that our proposed algorithms obtain a better performance and a lower complexity compared with the sparsity-based STAP algorithms and outperform the KAPE scheme in presence of errors in prior knowledge.

A Novel STAP Algorithm for Airborne MIMO Radar Based on Temporally Correlated Multiple Sparse Bayesian Learning

In a heterogeneous environment, to efficiently suppress clutter with only one snapshot, a novel STAP algorithm for multiple-input multiple-output (MIMO) radar based on sparse representation, referred to as MIMOSR-STAP in this paper, is presented. By exploiting the waveform diversity of MIMO radar, each snapshot at the tested range cell can be transformed into multisnapshots for the phased array radar, which can estimate the high-resolution space-time spectrum by using multiple measurement vectors (MMV) technique. The proposed approach is effective in estimating the spectrum by utilizing Temporally Correlated Multiple Sparse Bayesian Learning (TMSBL). In the sequel, the clutter covariance matrix (CCM) and the corresponding adaptive weight vector can be efficiently obtained. MIMOSR-STAP enjoys high accuracy and robustness so that it can achieve better performance of output signal-to-clutter-plus-noise ratio (SCNR) and minimum detectable velocity (MDV) than the single measurement vector sparse representation methods in the literature. Thus, MIMOSR-STAP can deal with badly inhomogeneous clutter scenario more effectively, especially suitable for insufficient independent and identically distributed (IID) samples environment.

An Efficient STAP Algorithm for Nonsidelooking Airborne Radar Based on Mainlobe Clutter Compensation

An Efficient STAP Algorithm for Nonsidelooking Airborne Radar Based on Mainlobe Clutter Compensation

Determining shoal membership using affinity propagation

We propose using the affinity propagation (AP) clustering algorithm for detecting multiple disjoint shoals, and we present an extension of AP, denoted by STAP, that can be applied to shoals that fusion and fission across time. STAP incorporates into AP a soft temporal constraint that takes cluster dynamics into account, encouraging partitions obtained at successive time steps to be consistent with each other. We explore how STAP performs under different settings of its parameters (strength of the temporal constraint, preferences, and distance metric) by applying the algorithm to simulated sequences of collective coordinated motion. We study the validity of STAP by comparing its results to partitioning of the same data obtained from human observers in a controlled experiment. We observe that, under specific circumstances, AP yields partitions that agree quite closely with the ones made by human observers. We conclude that using the STAP algorithm with appropriate parameter settings is an appealing approach for detecting shoal fusion–fission dynamics.

A Joint Transmitter-Receiver Adaptive STAP Algorithm of Clutter Suppression for Airborne MIMO Radar

In airborne MIMO radar, distributing characteristic of clutter have some relation with transmitted waveform diversity and Waveform Synthetic Structure. In this paper , we use the predominance of Transmitted waveform diversity on airborne MIMO radar, A joint transmitter-receiver adaptive dispose algorithm is proposed, and STAP dispose is done in wave, air and time dimension for airborne MIMO radar. The result of simulations show the proposed algorithm achieved the aim of clutter joint suppression, sequentially, which increase the performance of clutter suppression on airborne MIMO radar.

STUDY STAP ALGORITHM ON INTERFERENCE TARGET DETECT UNDER NONHOMOGENOUS ENVIRONMENT

In conventional statistical STAP algorithms, the existence of interference target in training samples will lead to signal cancellation, resulting in the output SCR falling and the moving target detection performance degrading. The nonhomogeneity detector is an efiective way to restrain the outlier, which can improve the covariance matrix estimation by detecting the samples containing outliers and rejecting them, and improve the STAP performance. A new interference target detection algorithm is proposed in this paper, the outlier detection is realized by using the samples' data phase information. Compared with traditional method, the improved algorithm is more sensitive to interfering target with difierent azimuth and intensity. Simulation results demonstrate the validity of this improved method.

Evaluation of Surrogate and Bootstrap Tests for Nonlinearity in Time Series

The validity of any test for nonlinearity based on resampling techniques depends heavily on the consistency of the generated resampled data to the null hypothesis of linear stochastic process. The surrogate data generating algorithms AAFT, IAAFT and STAP, as well as a residual-based bootstrap algorithm, all used for the randomization or bootstrap test for nonlinearity, are reviewed and their performance is compared using different nonlinear statistics for the test. The simulations on linear and nonlinear stochastic systems, as well as chaotic systems, reveals a variation in the test outcome with the algorithm and statistic. Overall, the bootstrap algorithm led to smallest test power whereas the STAP algorithm gave consistently good results in terms of size and power of the test. The performance of the nonlinearity test with the resampling techniques is evaluated on volume and return time series of international stock exchange indices.