Adaptive Processing Algorithms Research Articles

On the Efficient Implementation of Sparse Bayesian Learning-Based STAP Algorithms

Sparse Bayesian learning-based space–time adaptive processing (SBL-STAP) algorithms can achieve superior clutter suppression performance with limited training sample support in practical heterogeneous and non-stationary clutter environments. However, when the system has high degrees of freedom (DOFs), SBL-STAP algorithms suffer from high computational complexity, since the large-scale matrix calculations and the inversion operations of large-scale covariance matrices are involved in the iterative process. In this article, we consider a computationally efficient implementation for SBL-STAP algorithms. The efficient implementation is based on the fact that the covariance matrices that need to be updated in the iterative process of the SBL-STAP algorithms have a Hermitian Toplitz-block-Toeplitz (HTBT) structure, with the result being that the inverse covariance matrix can be expressed in closed form by using a special case of the Gohberg–Semencul (G-S) formula. Based on the G-S-type factorization of the inverse covariance matrix and the structure of the used dictionary matrix, we can perform almost all operations in the SBL-STAP algorithms by 2-D FFT/IFFT. As a result, compared with the original SBL-STAP algorithms, even for moderate data sizes, the proposed algorithms can directly reduce the computational load by about two orders of magnitudes without any performance loss. Finally, simulation results validate the effectiveness of the proposed algorithms.

A Novel Sparse Bayesian Space-Time Adaptive Processing Algorithm to Mitigate Off-Grid Effects

Space-time adaptive processing (STAP) algorithms based on sparse recovery (SR) have been researched because of their low requirement of training snapshots. However, once some portion of clutter is not located on the grids, i.e., off-grid problems, the performances of most SR-STAP algorithms degrade significantly. Reducing the grid interval can mitigate off-grid effects, but brings strong column coherence of the dictionary, heavy computational load, and heavy storage load. A sparse Bayesian learning approach is proposed to mitigate the off-grid effects in the paper. The algorithm employs an efficient sequential addition and deletion of dictionary atoms to estimate the clutter subspace, which means that strong column coherence has no effect on the performance of the proposed algorithm. Besides, the proposed algorithm does not require much computational load and storage load. Off-grid effects can be mitigated with the proposed algorithm when the grid-interval is sufficiently small. The excellent performance of the novel algorithm is demonstrated on the simulated data.

A Fast Space-Time Adaptive Processing Algorithm Based on Sparse Bayesian Learning for Airborne Radar.

Space-time adaptive processing (STAP) plays an essential role in clutter suppression and moving target detection in airborne radar systems. The main difficulty is that independent and identically distributed (i.i.d) training samples may not be sufficient to guarantee the performance in the heterogeneous clutter environment. Currently, most sparse recovery/representation (SR) techniques to reduce the requirement of training samples still suffer from high computational complexities. To remedy this problem, a fast group sparse Bayesian learning approach is proposed. Instead of employing all the dictionary atoms, the proposed algorithm identifies the support space of the data and then employs the support space in the sparse Bayesian learning (SBL) algorithm. Moreover, to extend the modified hierarchical model, which can only apply to real-valued signals, the real and imaginary components of the complex-valued signals are treated as two independent real-valued variables. The efficiency of the proposed algorithm is demonstrated both with the simulated and measured data.

A low complexity STPAP algorithm based on an alternating polarization-sensitive array

BackgroundSpace–time adaptive processing (STAP) has been widely used in the fields of communication, radar, and navigation anti-jamming. However, the traditional scalar arrays used with STAP have limitations because they can only obtain spatial information. To improve the performance, joint space–time filtering is proposed using an alternating polarization-sensitive array (APSA). Compared to a dual-polarization sensitive array (DPSA), this array can provide polarization information and reduce the computational complexity.MethodsAn alternating polarization-sensitive array space–time polarization adaptive processing (APSA-STPAP) algorithm is proposed based on the linear constraint minimum variance (LCMV) criterion. Different from the traditional LCMV criterion, the space–time polarization joint steering vector of the desired and interference signals is used as the constraint matrix, and the “set 1” and “set 0” conditions are used as the constraint conditions to effectively suppress the interference signals and enhance the desired signals.ResultsSimulation results are presented which show the following. (1) Filtering with the proposed APSA-STPAP algorithm is similar to that with the DPSA-STPAP algorithm. From the perspective of the spatial, time, and polarization domains, it can form nulls in the directions of the interference and realize space–time-polarization adaptive processing. (2) The APSA-STPAP algorithm has lower computational complexity than the DPSA-STPAP algorithm. Moreover, the dipole of the alternating polarization sensitive array is halved, which reduces the coupling effect between electric dipoles and makes implementation easier. (3) The APSA-STPAP algorithm maintains good anti-interference performance even when the electric dipole and anti-jamming degrees of freedom are reduced by half, and the anti-jamming performance is similar to that of a polarization-sensitive array. There is little difference between the anti-interference performance of the APSA-STPAP and DPSA-STPAP algorithms when SNR ≥ − 10 dB.

Cascaded Least Square Algorithm for Strong Clutter Removal in Airborne Passive Radar

Owing to the continuous random and nonperiodic characteristics of the passive signal, the random range sidelobe coupling (RRSC) effects of the strong clutter (i.e., the direct path signal and the strong multipath signal) will exacerbate the performance of space-time adaptive processing (STAP) algorithm in airborne passive radar (APR). This article analyzes the influence of RRSC caused by strong clutter on detection range cell of interest and illustrates that strong clutter should be suppressed before applying the STAP algorithm. A cascaded least square (CLS) algorithm based on the directional dependence of clutter Doppler frequency is proposed for side-looking APR, where the first least square (LS) algorithm is performed to achieve the amplitude estimation of the Doppler-shift echo signal, and the channel amplitude and phase error correction factor can be obtained by the estimated amplitude information of the direct patch signal and the APR geometric configuration. The second LS is employed to further estimate the amplitude information of the strong clutter, and the resulting weight vector can be utilized for clutter cancelation. To make the CLS algorithm more practical, a batch version of the CLS (CLS-B) algorithm is proposed in this article. The advantage of the method is that it cannot only cancel clutter along the clutter ridge but also effectively prevent the suppression of targets falling in the clutter Doppler band. In addition, the proposed CLS algorithm performs well in the presence of the channel amplitude and phase error. A series of simulations are conducted to test the proposed algorithms.

DOA Estimation for Heterogeneous Wideband Sources Based on Adaptive Space-Frequency Joint Processing

For direction-of-arrival (DOA) estimation of heterogeneous wideband sources, we propose a new adaptive space-frequency joint processing algorithm. The algorithm is implemented in the sparse Bayesian learning (SBL) DOA framework, which is named as ASF-SBL algorithm in this paper. The traditional SBL-based DOA methods suffer from the problem of erroneous DOA estimation due to the structural mismatch between the invariant prior and the sparse coefficients. To solve this problem, the ASF-SBL algorithm employs a new space-frequency correlation prior model that can be adaptively changed to fit heterogeneous DOA scenarios. Specifically, nine alternative space-frequency structural patterns are constructed to represent the joint space-frequency characteristics of spatial sparse signals. By evaluating the space-frequency correlation of the sparse coefficients updated in each iteration under SBL framework, a suitable pattern is selected from the nine choices to determine the adaptive prior of each coefficient. This adaptive method leads to accurate DOA estimation in different wideband sources scenarios. In addition, we introduce a distributed processing method to extend the ASF-SBL algorithm to two-dimensional DOA estimation. This extension is achieved by decoupling the DOA estimation into two one-dimensional estimations. The decoupling avoids the problems of a huge redundant dictionary and excessive computational complexity caused by the combination of azimuth and elevation angles. Numerical simulations show that the ASF-SBL algorithm is superior to existing algorithms in DOA estimation of heterogeneous sources.

A General Framework for Slow and Weak Range-Spread Ground Moving Target Indication Using Airborne Multichannel High-Resolution Radar

Airborne multichannel high-resolution radar (HRR)-ground moving target indication (GMTI) is of great significance to wide-area surveillance, traffic monitoring and target recognition. The Aerospace Information Research Institute, Chinese Academy of Sciences, produced an advanced airborne digital array radar with high resolution and conducted an experiment with slow and weak cooperative moving targets in 2021. In this paper, an overall processing framework for target detection, parameter estimation and target tracking using this system is introduced. For HRR detection, the echo energy of targets is spread into multiple range units, so-called range-spread targets; thus, using detectors designed for point-like targets will severely degrade the detection performance, especially for slow and weak targets. To address the adaptive detection of such range-spread targets embedded in Gaussian clutter with an unknown covariance matrix, a novel two-step generalized space-time adaptive processing (GSTAP) algorithm is proposed, which offers an enhanced clutter suppression capability compared with natural competitors. Moreover, a tracking method is implemented in the range-Doppler domain to avoid track loss caused by azimuth relocation error and reject discrete false alarms. Both simulation and experimental results are presented to demonstrate the effectiveness of the proposed method and provide a paradigm for further research.

A Novel Dimension-Reduced Space–Time Adaptive Processing Algorithm for Spaceborne Multichannel Surveillance Radar Systems Based on Spatial–Temporal 2-D Sliding Window

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data.

Vision Sensor for Measuring Aerial Refueling Drogue Using Robust Method

In this research, a moving target position measurement system based on vision sensors is developed, which can be used for remote monitoring of UAV refueling docking under complex environment. The developed system uses two vision sensors and embedded devices placed on a fixed point of the UAV to realize a synchronous acquisition of given drogue images, and transmits it to the computer to calculate the drogue position in real time. In this paper, an adaptive processing algorithm for abnormal regions based on stereo vision is proposed, which can determine the illumination inconsistent regions that affect the matching in the image pair according to the statistical distribution. A fast stereo rectification algorithm for image pair is proposed to deal with sensor micro jitter. A multi-cost fusion robust matching algorithm based on “ship” search is also proposed to further improve the position measurement accuracy. The robustness and effectiveness of the measurement system in moving target remote measurements are verified by simulation flight and field flight tests.

Reduced‐rank space‐time adaptive processing algorithm based on multistage selections of angle‐Doppler filters

A new reduced-rank (RR) space-time adaptive processing (STAP) algorithm based on multistage selections of angle-Doppler filters is proposed in the form of a generalised sidelobe canceller. First, two types of the RR auxiliary angle-Doppler filters are designed based on the discrete Fourier basis functions. Then, a novel multistage method is implemented to select the angle-Doppler filters by using the cross-correlation observed at the current stage and the residual output observed at the previous stage. Finally, the asymptotic statistic performance of target detection is evaluated in terms of the conventional framework of STAP for the cases of both known and unknown covariance, which shows that the proposed algorithm is capable of providing the constant false alarm rate. Numerical examples are provided, and they demonstrate that the proposed algorithm is able to offer better performance than the existing sparsity-based and the conventional reduced-dimension algorithms under the intrinsic clutter motion and array gain and phase errors.

The Robust Ground Clutter Canceller Based on Inaccurate Prior Knowledge in Airborne Radar

Two-dimensional pulse-to-pulse canceller (TDPC) of ground clutter can effectively suppress the clutter along the clutter trace, and therefore the moving target detectability of the following space-time adaptive processing (STAP) algorithm can be improved after TDPC as the clutter prefilter. However, TDPC may greatly impair the energy of moving target when inaccurate knowledge is exploited, which is detrimental to target detection. Aiming at this problem, a robust two-dimensional pulse-to-pulse canceller (RTDPC) of ground clutter is proposed. In order to enhance the TDPC’s robustness with inaccurate radar system parameters, which are mainly the platform velocity and crab angle, the errors of estimated platform velocity and crab angle are taken as the prior knowledge and added into the design of the clutter filter coefficient matrix. By exploiting RTDPC as the clutter prefilter, the moving target detectability of the following nonadaptive detection algorithm or STAP algorithm can also be enhanced. The simulated and MCARM data are utilized to verify the clutter suppression performance of RTDPC with inaccurate platform velocity and crab angle.

Aperture-type terahertz near-field imaging with a cylindrical frustum-shaped plastic probe.

We designed an aluminum-coated plastic probe made from the combination of cylinder and a frustum of a cone for terahertz near-field imaging. The bunching width of the probe is 12 µm and the electric field intensity enhancement factor just outside the probe tip reaches up to 25. An imaging resolution obtained through near-field imaging experiments on a variety of targets, including narrow metals and narrow media, is 6 µm (λ/450) at 0.11 THz. By adopting an adaptive threshold segmentation image processing algorithm that combines Hilbert scanning and wavelet transform, the influence of a large area of metal near the imaging area on the imaging result is successfully suppressed. Imaging of a scratch on the plastic substrate makes the new probe feasible for quality identification of nonmetallic cultural relics.

Ground moving target indication and parameter estimation algorithm using deramp space-time adaptive processing

Deramp space-time adaptive processing (Deramp-STAP) algorithm has been validated in its effectiveness in reducing the required channels to enable ground moving target indication (GMTI) for multi-channel synthetic aperture radar (SAR) systems. However, the parameter estimation for the moving target, including the critical radial velocity, is not accurate enough, and the radial velocity is always approximated by the equivalent velocity obtained by the Deramp operation. For the indication and imaging of ground moving targets, the accurate estimation of the radial velocity is of importance to improve the signal-clutter-noise-ratio of STAP and estimate the initial azimuth position of the moving target. A method for GMTI and the corresponding parameter estimation using Deramp-STAP is proposed. Compared with the traditional Deramp-STAP algorithms, the proposed algorithm utilizes the equivalent velocity estimated by Radon transform to remove the linear range migration before STAP and figure out where the moving target is located in the azimuth frequency domain. To estimate the radial velocity more accurately, the fast Fourier transform along the spatial direction is performed on the pixel where the moving target is located in the azimuth frequency domain after the Deramp operation, and then the radial velocity can be obtained through the spatial spectrum analysis. Finally, the estimated radial velocity is applied to construct the azimuth compression function for the moving target imaging, and then the initial azimuth position can be estimated. Several simulation experiments have been implemented to validate the effectiveness of the proposed algorithm.

A Study on Radar Target Detection using Space Time Adaptive Processing Algorithm and LCMV Algorithm

In this paper, we study the directionof arrival estimation of the desired target in adaptive array MV algorithm to update the weight, and the optimized weight removes the interference signal. The target signal is estimated using the optimized weight vector and the high resolution the direction of arrival estimation MUSIC algorithm. We calculate the inverse of the correlation matrix using the QR method to reduce the processing power consumption of the optimized weight. The optimal weight vector is applied to the proposed algorithm to estimate the desired target direction from the output power spectrum. The performance of the proposed method is compared with the existing method by simulation. The experimental method estimates three targets from the antenna received signal. The existing method did not estimate the three desired targets at [-30o,-20, -10o]. The proposed method accurately estimates the desired three targets at [-30o,-20, -10o]. In the [-10o, 0, 10o] target estimation, the existing method reduces the estimated resolution of the target, but the proposed method accurately estimates the target. We proved that the proposed method in the simulation was superior to the existing method.

A Robust Space-time Adaptive Processing Algorithm based on Particle Swarm Optimization for Non-stationary Clutter Suppression

A novel robust sparse recovery (SR) space-time adaptive processing (STAP) algorithm based on particle swarm optimization (PSO) for non-stationary clutter suppression is presented in this paper. A cost function for PSO in the presence of parameter errors is theoretically derived. An improved estimation process of clutter spectrum based on this cost function which is called PSO-SR is proposed and analyzed. A more accurate estimation result of clutter spectrum could be provided by this algorithm than the previous proposed algorithms in the presence of considerable parameter errors. Simulation results demonstrate the robust performance of this algorithm.

Adaptive spectral processing algorithm for staggered signals in weather radars

A spectral algorithm for processing staggered-pulse repetition time (SPRT) signals in weather radar is introduced. It includes new approaches for ground clutter filter and hydrometeor spectral moments estimation. The algorithm uses ideas similar to GMAP but applied to non-uniform sampled signals. This work is focused on staggered sequences 2/3, but can be extended to other staggered sequences. Monte Carlo experiments were used to evaluate the performance of the spectral moments estimators for simulated weather signal, in scenarios with and without the presence of ground clutter. When clutter is present, a study using different clutter-to-signal ratios was carried out, showing that the method can deal with a wide range of situations and is appropriate for implementation in real scenarios. A comparison against GMAP-TD was performed, showing similar estimation results for both algorithms and a fivefold processing speed improvement for the proposed method. The performance was also validated using real weather data RMA-12 from a radar located in San Carlos de Bariloche, Argentina. The proposed algorithm has an easy implementation and is a good candidate for real-time implementations.

A Minimum-Energy-Based Algorithm for Multichannel Reconstruction

For the azimuth multichannel synthetic aperture radar (SAR), nonuniform sampling is common since the pulse-repetition frequency cannot be selected arbitrarily, which leads to the contradiction between ambiguity and noise suppression in the azimuth signal reconstruction. This letter constructs a novel multichannel signal model by taking the coherence among different Doppler bins into account and proposes a minimum-energy-based multichannel reconstruction algorithm, which can obtain the unambiguous signal spectrum without increasing noise in the nonuniform sampling cases. By comparing the simulation results formed by digital beamforming (DBF), improved DBF (IDBF), and space-time adaptive processing (STAP) algorithms, the proposed algorithm shows better performance in balancing ambiguity and noise.

M-Sweeps multi-target analysis of new category of adaptive schemes for detecting χ2-fluctuating targets

ABSTRACT With the technology of clutter excitation developing up, adaptive processing algorithms have become critical in high performance radar systems. Target echoes can be obscured and false alarms can occur in the presence of such type of interference. This clutter can be mitigated by appropriate strategy of adaptive processors, in which the false alarm rate holds constant (CFAR) by providing a detection threshold of definite value above clutter. Therefore, the CFAR mechanism becomes an indispensable technique for fluctuating target detection; especially in heterogeneous environments. There are many scenarios to achieve such indigence property. However, difficulties in finding a single procedure to deal with diverse noise backgrounds are the prime thrust for developing a new structure based on composite architecture. In this regard, fusion of cell-averaging (CA), order-statistics (OS) and trimmed-mean (TM) strategies within fusion adaptive detectors results in higher detection performance. This paper is devoted to the analysis of this new model in the case where the radar receiver is supplemented by a non-coherent integrator of M-pulses. The tested as well as the spurious targets are assumed to follow χ2-distribution with two degrees of freedom in their fluctuation. Closed-form expression is derived for the detection performance. Our simulation results illustrate the significant advantage of the new model in both homogeneous and multi-target performances. In ideal situation, its performance outweighs that of the classical Neyman-Pearson (N-P) detector which is commonly regarded as the reference model for comparing new implementations in the CFAR world.

Research on FFT Algorithm Use SMP System

Based on the theoretical research of a series of algorithms of adaptive beamforming and space-time adaptive processing, it is necessary to discuss the implementation of related algorithms on the machine hardware platform. Fast Fourier transform (FFT) is an essential process in this implementation. With the development of array antennas, the number of points to be calculated by the FFT has also increased significantly. Therefore, the ultra-large point FFT has become a key technology for current antenna signal processing. Most of the existing FFT algorithms are researched based on single-core processors, and there is very little literature on the research of super-large-point FFT algorithms suitable for Symmetric Multiprocessor (SMP). In this paper, through analyzing the characteristics of a symmetric multi-processor (SMP) parallel processing system, the very large FFT fast algorithm (VLFFT) is proposed. This algorithm significantly reduces the dependence on memory and improves the FFT's performance using the limited rules of the one-dimensional sequence split, changing the twiddle factor calculation method, and optimizing the data distribution and storage access. And the research's optimized very large FFT algorithms is also summarized in the paper. Experiment results show that the algorithm is suitable for the SMP platform and can effectively solve the problem of the very large FFT, which make single-core processors harder to realize.

Clutter suppression algorithm for non‐side looking airborne radar with high pulse repetition frequency based on elevation–compensation–prefiltering

Clutter suppression for non-side-looking airborne radar operating at high pulse repetition frequency is an intractable problem because the clutter statistical properties vary with the range, especially in the near range. In addition, the near-range clutter is always coupled with the far-range clutter. Elevation prefiltering is recognised as an effective method to suppress the range-dependent near-range clutter, but the elevation training samples required must be independent and identically distributed (IID) and not contaminated by the far-range clutter. Here, an elevation-compensation-prefiltering space-time adaptive processing (STAP) algorithm is developed. In the proposed algorithm, the elevation training samples are selected from the elevation snapshots corresponding to the first pulse, which are not contaminated by the far-range clutter. An elevation compensation method is presented to alleviate the range dependence, and then there are adequate IID training samples to estimate the elevation covariance matrix. The performance of the near-range clutter suppression is improved. As for the residual range-independent far-range clutter, it can be suppressed by the cascaded azimuth-Doppler STAP. Numerical examples demonstrate the effectiveness of the proposed algorithm.