Space-time Adaptive Processing Research Articles

Robust STAP With Reduced Mutual Coupling and Enhanced DOF Based on Super Nested Sampling Structure

In practical airborne radar, mutual coupling between array elements usually leads to space-time steering vector distortion for space-time adaptive processing (STAP), which seriously degrades radar performance. In this paper, a robust STAP algorithm coupling the second order super nested arrays with pulses (2OSNAP) sampling structure is developed. The proposed method applies difference operation to construct virtual space-time snapshots in the virtual domain. To solve the clutter plus noise covariance matrix (CNCM) corresponding to the virtual space-time snapshots, the space-time smoothing method is used in this paper. Furthermore, the proposed method can directly give a set of virtual and robust STAP weight vector, which is convenient for the subsequent signal processing. Extensive numerical experiments were performed to verify the effectiveness and superiority of the proposed method. The test results also show that our proposed method provides more degrees of freedom (DOF), and eliminates the mutual coupling effect between array elements compared to most existing methods.

An Intelligent Sample Selection Method for Space-Time Adaptive Processing in Heterogeneous Environment

In the heterogeneous interference and target-rich environment, the clutter-plus-noise covariance matrix (CCM) estimate with a good match for the cell under test is significant for space–time adaptive processing. In this paper, an intelligent sample selection method is proposed to estimate the CCM and, thus, to suppress the clutter in the heterogeneous background. First, the proposed method applies the sample amplitude and the spatial relationship between the sample and the antenna beam center to describe the characteristics of samples, which preliminarily selects the crucial homogeneous and heterogeneous samples based on the joint histogram property. Then, the generalized inner product value and the spatial included angle are used to work as two representative features for the training samples as well as the testing samples. Based on the feature vector, the multi-dimensional feature projection based on the kernel function is carried out to achieve the offline training classifier, and the testing samples are classified with intelligence in the multi-dimensional feature space by utilizing the trained support vector machine. Due to the utilization of space–time property in representative feature extraction and intelligent strategy for the decision-making process, the proposed method acquires more reliable and efficient sample selection results compared with most of conventional sample selection methods. The experimental results based on the simulated and measured data show the effectiveness of the proposed method.

Cascaded Interference and Multipath Suppression Method Using Array Antenna for GNSS Receiver

The space-time adaptive processing method has been widely used for both narrowband and wideband interference suppression in GNSS receiver equipped with array antenna. However, the use of space-time filter may cause satellite signal distortion and lead to bias in pseudo-range measurement. In addition, multipath is one of the main error sources that will have a serious influence on the performance of the GNSS receiver. For precise applications, the error induced by space-time filter and multipath should be reduced. In this paper, a cascaded interference and multipath suppression method using an array antenna are proposed. In the first stage, the interference is suppressed by projecting the received array signal into the interference orthogonal subspace using subspace projection method. In the second stage, the directions of arrival (DOA) of the direct signal and the multipath signal are estimated using sparse recovery method. The DOA parameters enable the receiver to construct a space-time distortionless beamformer, which is used to null the multipath signals and maximize the signal-to-noise ratio. The method does not require the prior knowledge of the direction of the direct signal and achieves unbiased measurement while suppressing the interference and multipath. The simulation results demonstrate that the proposed method can achieve the interference and multipath suppression effectively, and have a distortionless response for the direct signal.

Clutter Suppression Approach for End-Fire Array Airborne Radar Based on Adaptive Segmentation

End-fire array antenna is a kind of special radar antenna with beam pointing perpendicular to the normal direction of the array. Because of its low profile and directional radiation characteristics, it is especially suitable for blind zone compensation of airborne radar. However, there are few publicly available pieces of literature on clutter suppression for end-fire array radar. In this paper, we firstly simulate and analyze the clutter spectrum characteristics for end-fire array airborne radar (EAAR), and then propose a clutter suppression method for EAAR based on adaptive segmentation. In the proposed method, the concept of Riemann mean distance of temporal covariance matrix is introduced to quantify the clutter nonstationarity, and in this way we realize adaptive segmentation of the clutter range-Doppler spectrum. Then the different clutter suppression methods including pulse Doppler (PD) processing, two-dimensional space-time adaptive processing (2D STAP), and three-dimensional STAP (3D STAP) are used to suppress clutter in different regions. The theoretical analysis and simulation results demonstrate that compared with 3D STAP, the proposed method not only guarantees clutter suppression performance, but also reduces the computational complexity to an extent.

Discrete Interference Suppression Method Based on Robust Sparse Bayesian Learning for STAP

Discrete interference influences the performance of existing space-time adaptive processing methods in practical scenarios. In order to effectively suppress discrete interference in real clutter environment, a discrete interference suppression method based on robust sparse Bayesian learning (SBL) is proposed for airborne phased array radar. In the proposed method, the estimation of spatial-temporal spectrum and the calibration of space-time overcomplete dictionary are carried out iteratively. During one iteration, the prominent components of clutter and discrete interference in the spatial-temporal plane are first estimated by SBL, and then the overcomplete dictionary is calibrated by calculating the error matrix. Because of the robust estimation of spatial-temporal spectral distribution, both the discrete interference and the homogeneous clutter profiles can be effectively suppressed with a small number of space-time data. The effectiveness of the proposed method is verified in the nonhomogeneous environment by utilizing simulated and actual airborne phased array radar data.

Low-Complexity Off-Grid STAP Algorithm Based on Local Search Clutter Subspace Estimation

Space–time adaptive processing (STAP) based on sparse recovery (SR-STAP) techniques exhibits significantly better performance than conventional STAP algorithms within a very small number of snapshots. However, when the clutter patches do not locate exactly on the discrete space–time grid points, the performances of SR-STAP algorithms degrade severely. In this letter, a low-complexity off-grid STAP algorithm based on local search clutter subspace estimation is proposed to overcome this issue. In the proposed algorithm, the global atoms are first selected from the reduced-dimension global STAP dictionary using the design selection criterion. Then, the optimal atoms are searched from the local STAP dictionary. Finally, these space–time steering vectors corresponding to the optimal atoms are used to construct the clutter subspace iteratively, and the STAP weight is obtained by projecting the snapshot on the subspace orthogonal to the clutter subspace. Numerical experiments with both simulated and Mountain-Top data are carried out to demonstrate the effectiveness of the proposed algorithm.

Reduced‐dimension STAP using a modified generalised sidelobe canceller for collocated MIMO radars

This study addresses the problem of beam-space post-Doppler reduced-dimension (RD) space-time adaptive processing (STAP) using a modified generalised sidelobe canceller (GSC) in an airborne collocated multiple-input multiple-output (MIMO) radar. Steering vectors corresponding to the nulls of beamforming are used to construct the blocking matrix (BM) of the GSC processor with optimal signal to clutter plus noise ratio. However, it is difficult to determine the beampattern nulls for a collocated MIMO radar which is equivalent to a virtual array with coherent amplitude weights. To build a valid BM of the GSC-based STAP processor, the authors propose a BM modification method based on the modified Gram-Schmidt orthogonalisation algorithm. The authors prove that the modified RD-GSC processor can achieve the performance equal to that of the fully optimal processor conditionally. The authors also show that the proposed method has reduced computational complexity and fast convergence rate. Numerical simulations are presented to demonstrate the effectiveness of the proposed methods.

Low-Complexity Reduced-Dimension Space–Time Adaptive Processing for Navigation Receivers

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.

Observed results on optimal sample support in space time adaptive processing in radar system

Observed results on optimal sample support in space time adaptive processing in radar system

Reduced-dimension space-time adaptive processing with sparse constraints on beam-Doppler selection

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.

METHODS OF JAMMING INTERFERENCE

The article investigates the problem of providing high noise immunity radar under the influence of passive and intentional interference. The purpose of radio operation of the radar is to create conditions that would impede the operation of systems and minimize its effectiveness. The main method of radio transmission is still creating (staging) interference. Modern radar systems must solve the tasks in terms of electronic suppression using, including intentional interference and under severe time constraints. It is shown that the most effective way to improve the noise immunity of radar systems designed to operate in multipoint space, including non-stationary, interference is adaptive space-time processing of the received signals, based on the angular selection of targets, due to the formation of zeros in the directional diagram in the direction of interference sources. This problem is solved by determining the accuracy of the direction finding of interference sources and is achieved by the joint operation of the antenna array and multi-channel signal processing devices, namely the separation of interference signals on different receiving channels.

Adaptive Lattice Filters for Systems of Space-Time Processing of Non-Stationary Gaussian Processes

Adaptive systems protecting pulse radars from non-stationary in time (range) clutter echoes are usually tuned using training vectors composed of complex amplitudes of input signals and comprising a finite-length “sliding window” of data. From any current range gate to a subsequent one, a training sample is partially updated (or modified) by means of excluding the “old” training vectors (correspond to the current range gate) and including the “new” ones (correspond to the next range gate). As a consequence, respective estimates of adaptive system parameters are corrected according to a modified sample correlation matrix (CM), which is typically a sum of an initialCMand a modifying matrix of rank K ≥ 1. In this case it is possible to avoid re-computing these parameters based on a new training sample of full size and, instead of this, we correct them in an “economical” way employing K-rank modification of a matrix inverse to the CM estimate. This paper is devoted to comparative analysis of various (K ≥ 1)-rank modification algorithms that correct the parameters of adaptive lattice filters (ALF). Main attention is paid to synthesis as well as theoretical and experimental study of algorithms of direct (K > 1)-rank modification of the ALF parameters. These algorithms attain the said objective omitting the K-fold application of known rank-one (K = 1) modification algorithms. We also synthesize a combined algorithm (CA) of (K ≥ 1)-rank modification of the ALF parameters that is more computationally simple and more numerically robust compared to known algorithms. The ALF employing the CA can serve as an effective tool for solving various tasks of space-time adaptive signal processing in pulse radars of different purpose.

Multimodel Shrinkage for Knowledge-Aided Space-Time Adaptive Processing

Space-time adaptive processing gets its adaptivity from estimating the second-order statistics of the clutter from secondary data. In practice, the amount of available secondary data may not be sufficient for the sample covariance estimate to be accurate. Existing techniques exploit assumed properties of the clutter covariance, such as rank, to reduce the need for secondary training data. More recently in radar, single-model shrinkage estimators have been shown to reduce the reliance on training data given that a good a priori model for the covariance is available. We extend shrinkage estimation to a regularized multimodel approach that incorporates inexact knowledge of a digital elevation map to reduce the need for large amounts of secondary data. Using both simulation and the KASSPER I dataset, performance of the proposed approach is compared to low-rank estimation and various single-model shrinkage estimation approaches. The proposed approach offers the practical ability to reliably estimate the clutter covariance from a number of training data less than the rank of the clutter covariance matrix.

Parametric Adaptive Matched Filter for Multistatic MIMO Radar

A fully distributed MIMO radar system can be treated in terms of all bistatic pairs. If a bistatic pair in a distributed MIMO radar system employs multiple transmit and receive elements in a phased array configuration, this increases the dimensionality of the data received over a coherent processing interval, which in turn increases the training data needed to reliably estimate the covariance matrix. This problem is exacerbated by the heterogeneity of training data arising from the bistatic geometry, which further degrades the quality of the covariance matrix estimate used for adaptive detection, resulting in a deleterious impact on space-time adaptive processing performance. To address these issues, we develop a parametric approximation to a physics-based clutter model, which approximates the physical characteristics of a variety of different real world clutter environments, and is especially useful in modeling clutter scenarios encountered in distributed MIMO radar. We show that clutter can be approximated as a multichannel autoregressive process of model order 4. This allows us to reduce the training data requirements and minimize the effects of training data heterogeneity on the detection performance. We demonstrate our results through simulation.

Ground Moving Target Detection Using Beam-Doppler Image Feature Recognition

An innovative moving target detection approach termed as Beam-Doppler Image Feature Recognition (BDIFR) is introduced based on the distinction between moving target and ground clutter features in the beam-Doppler domain. A novel minimum-distance-based region-growing method is developed for radar target feature extraction and detection. The proposed BDIFR algorithm is advantageous over conventional space-time adaptive processing in detecting ground moving targets in inhomogeneous clutter environments since it does not require secondary training data for clutter estimation.

Clutter suppression for airborne FDA-MIMO radar using multi-waveform adaptive processing and auxiliary channel STAP

Abstract Range dependent clutter suppression is a challenging problem in non-side-looking airborne radar, especially in the presence of range ambiguity. To deal with this problem, this paper proposes a two-stage adaptive clutter suppression method. It utilizes the degrees of freedom in range, angle and time domains provided by a frequency diverse array multiple-input multiple-output (FDA-MIMO) radar. In the first stage, a novel multi-waveform based adaptive beamforming in joint transmit-receive (Tx–Rx) domain is proposed for range-ambiguous clutter suppression. Analysis on second-order statistic property shows that the clutter data obtained from multiple waveforms with disjoint frequency bands are (approximately) independent and identically distributed (IID) in joint Tx–Rx spatial domain. By exploiting this characteristic, a target-free Tx–Rx covariance matrix is estimated by the IID data, which are extracted from carefully designed secondary waveforms. Therefore, a near-optimal Tx–Rx response is achieved, and range-ambiguous clutter can be suppressed effectively. In the second stage, a new auxiliary channel based space-time adaptive processing approach incorporated with range dependence compensation is devised for residual clutter suppression. It takes full advantage of the prior knowledge of clutter spectrum. Thus, excellent clutter suppression performance can be obtained by the proposed approach. Simulation results demonstrate the effectiveness of the proposed method.

Joint Design of Space-Time Transmit and Receive Weights for Colocated MIMO Radar.

Compared with single-input multiple-output (SIMO) radar, colocated multiple-input multiple-output (MIMO) radar can detect moving targets better by adopting waveform diversity. When the colocated MIMO radar transmits a set of orthogonal waveforms, the transmit weights are usually set equal to one, and the receive weights are adaptively adjusted to suppress clutter based on space-time adaptive processing technology. This paper proposes the joint design of space-time transmit and receive weights for colocated MIMO radar. The approach is based on the premise that all possible moving targets are detected by setting a lower threshold. In each direction where there may be moving targets, the space-time transmit and receive weights can be iteratively updated by using the proposed approach to improve the output signal-to-interference-plus-noise ratio (SINR), which is helpful to improve the precision of target detection. Simulation results demonstrate that the proposed method improves the output SINR by greater than 13 dB.

Robust Waveform Optimization for MIMO-OFDM-Based STAP in the Presence of Environmental Uncertainty

The issue of robust orthogonal frequency division multiplexing (OFDM) multi-input multi-output (MIMO) radar waveform optimization is considered here with clutter uncertainty to enhance the worst-case detection probability of the MIMO-OFDM radar-based space–time adaptive processing (STAP). Robust optimization is necessary on account of the fact that waveform design is generally sensitive to the initial parameter estimation errors. According to the max–min approach, the issue of robust waveform design is constructed with the criterion of maximizing the worst-case output SINR, namely signal-to-interference-noise ratio, under the constraints of the clutter covariance matrix (CCM) errors and constant modulus to ease this sensitivity systematically, and hence the robustness of STAP’s detection to the uncertainty in the CCM estimate can be enhanced. To handle the acquired nonlinear and sophisticated issue, an iterative approach based on diagonal loading (DL) is presented, in which the inner and outer optimization issues can be recast into semidefinite programming ones by employing DL method, and thus both of them can be tackled in an efficient way. As compared to uncorrelated waveforms and the non-robust algorithm, numerical simulations reveal that the developed approach can enhance the robustness of STAP’s detection to the uncertainty in the CCM considerably.

Knowledge-Aided Bayesian Space-Time Adaptive Processing

Ground moving target indicator (GMTI) radar processing attempts to distinguish between radar returns emanating from moving targets and stationary ground clutter. The task is confounded by the relative motion between the radar platform and the scene, as well as by the strength of clutter returns. Techniques such as space-time adaptive processing require an unknown interference covariance describing clutter, jammers, and thermal noise. The covariance is estimated from training data not under test, but, heterogeneous, contaminated, or limited training data degrade the covariance estimate and reduce the detection performance. State-of-the-art techniques for interference covariance estimation reduce the required amount of training data by imposing assumed structure on the covariance matrix. Here, a Bayesian signal model is adopted for jointly estimating targets and clutter in a single cell under test, allowing GMTI processing without training data. The approach incorporates the knowledge of an approximate digital elevation map, platform kinematics (velocity, crab angle, and antenna spacings), and the belief that moving targets are sparse in the scene. Low-complexity computation with the Bayesian model is enabled by recent algorithm developments for fast inference on linear mixing models. Results from the KASSPER I dataset show improved detection performance compared to existing techniques using scores or even hundreds of training bins.

Robust waveform design for MIMO–OFDM‐based STAP in the presence of target uncertainty

The issue of robust orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) radar waveform design is investigated here with target uncertainty to enhance the worst-case space-time adaptive processing (STAP) detection performance of MIMO–OFDM radar. Based on the max–min method, the robust waveform optimisation problem is constructed under the criterion of maximising the worst-case output SINR, namely, signal–interference–noise ratio, with constraints of constant modulus and target estimation errors to enhance the robustness of STAP's detection performance to target uncertainty. To handle the obtained non-linear and complicated issue, an approach based on diagonal loading (DL) is presented. By employing DL method, the optimisation issue can be relaxed to semidefinite programming one, and thus it can be tackled very efficiently. When compared with uncorrelated signals, the state-of-the-art robust and non-robust waveform design methods for MIMO–STAP, numerical simulations reveal that the developed approach can enhance the robustness of the detection performance of STAP significantly.