Adaptive Processing Algorithms Research Articles

Design and evaluation of symmetric space–time adaptive processing of an array antenna for precise global navigation satellite system receivers

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.

Low complexity 2D adaptive image processing algorithm and its hardware implementation

Digital image processing (DIP) algorithms used in consumer electronics products have high computational complexities. Therefore, in this paper, we propose a novel low complexity 2D adaptive DIP algorithm. The proposed algorithm reduces computational complexities of 2D DIP algorithms by exploiting pixel correlations in input image without reducing quality of output image. We also designed a low energy 2D adaptive DIP hardware implementing the proposed algorithm. The proposed hardware is verified to work correctly on an FPGA board. It has significantly less energy consumption than original 2D DIP hardware. Therefore, it can be used especially in portable consumer electronics products.

Space–time adaptive processing by enforcing sparse constraint on beam‐Doppler patterns

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.

Selective detection of individual gases and CO/H2 mixture at low concentrations in air by single semiconductor metal oxide sensors working in dynamic temperature mode

Highly selective detection of various individual gases (CO, H2, CH4, C3H8, NO, NO2, H2S, SO2) at low concentrations (0.01–667ppm) in air by a single SnO2-based metal oxide sensor (MOX-sensor) is presented. The sensor operates in dynamic temperature mode combined with a number of adaptive signal processing algorithms. Artificial neural networks were proven to be more effective among the other adaptive algorithms implemented in this study. Identification of individual gases by a single sensor, averaged over all the gases and concentrations, resulted in only 13.2% false recognitions. Most of the failures were attributed to NO2 detection in 0.01–0.1ppm concentrations range.The ability of a single sensor to identify gas mixtures in a complex background was tested on the example of CO+H2 mixture in air, which simulates smoldering in the early stages of fire. The algorithm showed the ability to identify and quantify CO+H2 mixture with less than 10% error rate, even in constant presence of background gas (NO2 1.4ppm). Chemical modification of SnO2, increasing sensor response and sensitivity to individual components of the mixture, was proven to be beneficial for improvement of identification and quantification of gas mixture. Significant improvement in quantification accuracy (decrease in relative error from 7 to 2.5%) was achieved by utilizing a 3 sensor array in combination with an adaptive data processing algorithm, compared to the use of a single sensor alone. The prominent negative effect of humidity (Rh 30%, 25°C) on the performance of adaptive algorithms, sensor signal processing, system selectivity, and gas mixture identification is demonstrated.

Sparsity‐based STAP algorithm with multiple measurement vectors via sparse Bayesian learning strategy for airborne radar

To improve the performance of the recently developed parameter-dependent sparse recovery (SR) space-time adaptive processing (STAP) algorithms in real-world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain-Top data demonstrate the fast convergence and good performance of the proposed algorithm.

A Moving Target Imaging Algorithm for HRWS SAR/GMTI Systems

For a target illuminated by a high-resolution and wide-swath synthetic aperture radar system, the velocity is estimated based on sparse direction-of-arrival estimation. Then, the space-time adaptive processing algorithm is utilized to suppress the clutter and reconstruct the spectra of the target, followed by the traditional target imaging. The performance of the proposed algorithm is analyzed in detail. The proposed algorithm can retain the power of the moving target, and is free of searching.

Fast ℓ1-regularized space-time adaptive processing using alternating direction method of multipliers

Motivated by the sparsity of filter coefficients in full-dimension space-time adaptive processing (STAP) algorithms, this paper proposes a fast l1-regularized STAP algorithm based on the alternating direction method of multipliers to accelerate the convergence and reduce the calculations. The proposed algorithm uses a splitting variable to obtain an equivalent optimization formulation, which is addressed with an augmented Lagrangian method. Using the alternating recursive algorithm, the method can rapidly result in a low minimum mean-square error without a large number of calculations. Through theoretical analysis and experimental verification, we demonstrate that the proposed algorithm provides a better output signal-to-clutter-noise ratio performance than other algorithms.

Research and Implementation of Adaptive Control Method Based on EEG

The appearance of BCI (Brain-Computer Interface) [2] provides people with a new foreign exchange of information and control method. The project we studied is just the application of this technology. We use TGAM module, a dry electrode detecting weak EEG from brain and making result of attention, meditation and so on, to design our car control system. Based on numerical analysis and researches about the raw EEG signals and attention, we propose an adaptive EEG processing algorithm to design the car by a complete set of control scheme. Finally, we test the system and get a good result that the car moves under the control as we expected.

Space-time adaptive processing algorithm for airborne MIMO radar with nonside-looking array using temporally correlated multiple sparse Bayesian learning

Space-time adaptive processing algorithm for airborne MIMO radar with nonside-looking array using temporally correlated multiple sparse Bayesian learning

Clutter Rank Estimation for Reduce-Dimension Space-Time Adaptive Processing MIMO Radar

Estimation of clutter rank in the reduce-dimension (RD) domain has great importance for RD space-time adaptive processing (STAP) algorithms. In this letter, clutter rank estimation rules of RD STAP algorithms for multiple input multiple output (MIMO) radar with different parameters are developed. The accuracy of the proposed rules is verified and the way to design RD MIMO STAP processor under the guidance of the proposed rules is presented.

Clutter nulling space‐time adaptive processing algorithm based on sparse representation for airborne radar

Traditional clutter subspace estimation algorithms try to solve the problem by estimating the clutter subspace eigenvectors from training samples, which are inevitably contaminated by noise. Actually, this can never be the best estimate, especially when the size of the training set is small. Furthermore, the omnipresent noise precludes the seeking of a pure clutter subspace. To cope with above drawbacks, the authors appeal to the sparse representation/recovery technique. From a mathematical viewpoint, the pure clutter subspace is spanned by the space-time steering vectors corresponding to the clutter component, and it can be constructed by a suitable set of space-time steering vectors selected from an overcomplete space-time steering dictionary. A criterion for selecting these space-time steering vectors is devised. Moreover, a clutter nulling type space-time adaptive processing algorithm is derived based on the proposed criterion. The resulting algorithm only needs the knowledge of the noise power rather than the clutter rank, which is quite troublesome to estimate in practice. Numerical results with both simulated and the Mountain-Top data demonstrate that the proposed algorithm has superior clutter suppression performance even with limited training samples.

Sparse Representation Based Algorithm for Airborne Radar in Beam-Space Post-Doppler Reduced-Dimension Space-Time Adaptive Processing

An efficient and training-sample-reducing space-time adaptive processing (STAP) algorithm based on sparse representation for ground clutter suppression in airborne radar is proposed in this paper. First of all, the principle and problems of sample matrix inversion-based STAP and sparse representation (SR)-based STAP algorithms are reviewed. Then, the conception of the local space-time spectrum (LSTS) of clutter is considered by exploiting the intrinsic sparsity nature of clutter in local beams and the Doppler domain. To estimate the LSTS using the sparse representation technique in a cost-effective way, a variable space-time mask matrix is designed. Finally, the reduced-dimension clutter plus noise covariance clutter matrix and the corresponding adaptive weight vector are calculated based on the estimated LSTS. Numerical results with both simulated data and Mountain-Top data demonstrate that the new algorithm provides an excellent performance of clutter suppression and moving target detection with only one training range cell and significant computational savings compared with existing SR-based STAP algorithms.

基于FPGA的自适应光学波前处理算法

基于FPGA的自适应光学波前处理算法

Enhanced knowledge-aided space–time adaptive processing exploiting inaccurate prior knowledge of the array manifold

The accuracy of the prior knowledge of the clutter environments is critical to the clutter suppression performance of knowledge-aided space–time adaptive processing (KA-STAP) algorithms in airborne radar applications. In this paper, we propose an enhanced KA-STAP algorithm to estimate the clutter covariance matrix considering inaccurate prior knowledge of the array manifold for airborne radar systems. The core idea of this algorithm is to incorporate prior knowledge about the range of the measured platform velocity and the crab angle, and other radar parameters into the assumed clutter model to obtain increased robustness against inaccuracies of the data. It first over-samples the space–time subspace using prior knowledge about the range values of parameters and the inaccurate array manifold. By selecting the important clutter space–time steering vectors from the over-sampled candidates and computing the corresponding eigenvectors and eigenvalues of the assumed clutter model, we can obtain a more accurate clutter covariance matrix estimate than directly using the prior knowledge of the array manifold. Some extensions of the proposed algorithm with existing techniques are presented and a complexity analysis is conducted. Simulation results illustrate that the proposed algorithms can obtain good clutter suppression performance, even using just one snapshot, and outperform existing KA-STAP algorithms in presence of the errors in the prior knowledge of the array manifold.

Adaptive Beamspace Processing for Phased-Array Weather Radars

The next generation of weather radars, which may also support other missions, is likely to be based on phased arrays that will utilize simultaneous receive beams to achieve the required update times. Some of the disadvantages of using simultaneous receive beams, such as higher two-way antenna radiation pattern sidelobes, can be mitigated by using adaptive beamforming. A majority of the existing adaptive beamforming algorithms are designed for point targets, and direct application to distributed scatterers (e.g., hydrometeors) can lead to significantly biased estimates of key radar variables. This paper presents an adaptive beamspace processing algorithm specifically designed for weather-surveillance radar applications. Through both simulated and real data, it is shown that the proposed adaptive beamspace processing algorithm can produce accurate and calibrated estimates of radar variables while also automatically rejecting interference signals.

A Robot-Assisted Cell Manipulation System with an Adaptive Visual Servoing Method.

Robot-assisted cell manipulation is gaining attention for its ability in providing high throughput and high precision cell manipulation for the biological industry. This paper presents a visual servo microrobotic system for cell microinjection. We investigated the automatic cell autofocus method that reduced the complexity of the system. Then, we produced an adaptive visual processing algorithm to detect the location of the cell and micropipette toward the uneven illumination problem. Fourteen microinjection experiments were conducted with zebrafish embryos. A 100% success rate was achieved either in autofocus or embryo detection, which verified the robustness of the proposed automatic cell manipulation system.

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.

Sparsity-Based Direct Data Domain Space-Time Adaptive Processing with Intrinsic Clutter Motion

In this paper, we propose a sparsity-based direct data domain space-time adaptive processing (D3-STAP) algorithm for airborne radar that considers the intrinsic clutter motion (ICM). The proposed D...

Space time adaptive processing algorithm for multiple‐input–multiple‐output radar based on Nyström method

In this study, a new space-time adaptive processing (STAP) algorithm based on Nystrom (Nystrom-STAP) method is proposed to adaptively suppress the clutter and jammer in multiple-input-multiple-output (MIMO) radar system. The proposed method can reduce the number of training data and computation complexity with less performance loss. In the algorithm, by exploiting the low-rank characteristic with Nystrom extension strategy and column sampling technique, the Nystrom-based covariance estimator is obtained to approximate the clutter subspace with high accuracy, and it only need small clutter homogenous training data support. Then, combining block-diagonal feature of the jammer covariance matrix and the low-rank property of clutter subspace, the STAP filter can be formed by the inversions of low dimension matrices, which has less computation complexity. In order to further improve the convergence performance of the proposed method, with the help of adaptive displaced phase centre array technique, the reduced-rank Nystrom approaches are proposed. Simulation results demonstrate that the proposed methods exhibit better signal-to-interference-plus-noise ratio and probability of detection performance than conventional STAP algorithms with fewer training data and lower computation complexity.

L 1 ‐regularised joint iterative optimisation space‐time adaptive processing algorithm

In this study, two novel joint iterative optimisation space-time adaptive processing (STAP) algorithms are proposed based on L 1-regularised for airborne radar. Both of them are implemented in generalised sidelobe canceller (GSC) architecture. When the rank of the interference covariance matrix is far fewer than the system degrees of freedom, the STAP filter weight will be sparse. In this case, a sparse constraint is further imposed to the minimum variance criterion of GSC. To solve this optimisation problem, a joint iterative recursive least squares (RLS) algorithm (L 1-JI-RLS) and a joint iterative least mean square (LMS) algorithm (L 1-JI-LMS), both of which are based on L 1-regularised, are proposed. The L 1-JI-RLS algorithm achieves the minimum output power by adjusting the penalty parameter and filter weight adaptively, while the L 1-JI-LMS algorithm does it by adjusting the penalty parameter, step and filter weight adaptively. The computational complexity of the L 1-JI-LMS algorithm is far lower than the conventional and some sparse STAP algorithms. Monte Carlo experiments validate that both the L 1-JI-RLS and L 1-JI-LMS algorithms outperform other L 1-based and reduced-rank STAP algorithms, such as a faster convergence rate, improved output signal-to-interference-plus-noise-ratio and target detection performance.