Robust space-time adaptive processing for airborne radar in nonhornogeneous clutter environments

The performance of space–time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial–temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial–temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

Being equipped with a millimeter-wave radar allows a low-flying helicopter to sense the surroundings in real time, which significantly increases its safety. However, nonhomogeneous clutter environments, such as a multiple target situation and a clutter edge environment, can dramatically affect the radar signal detection performance. In order to improve the radar signal detection performance in nonhomogeneous clutter environments, this paper proposes a new automatic censored cell averaging CFAR detector. The proposed CFAR detector does not require any prior information about the background environment and uses the hypothesis test of the first-order difference (FOD) result of ordered data to reject the unwanted samples in the reference window. After censoring the unwanted ranked cells, the remaining samples are combined to form an estimate of the background power level, thus getting better radar signal detection performance. The simulation results show that the FOD-CFAR detector provides low loss CFAR performance in a homogeneous environment and also performs robustly in nonhomogeneous environments. Furthermore, the measured results of a low-flying helicopter validate the basic performance of the proposed method.

Space-time adaptive monopulse processing for airborne radar in non-homogeneous environments

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance.

In this paper, we consider moving target detection using a distributed multiple-input multiple-output (MIMO) radar on stationary platforms in nonhomogeneous clutter environments. Our study is motivated by the fact that the multistatic transmit-receive configuration in a distributed MIMO radar causes nonstationary clutter. Specifically, the clutter power for the same test cell may vary significantly from one transmit-receive pair to another, due to azimuth-selective backscattering of the clutter. To account for these issues, a new nonhomogeneous clutter model, where the clutter resides in a low-rank subspace with different subspace coefficients (and hence different clutter power) for different transmit-receive pair, is introduced and the relation to a general clutter model is discussed. Following the proposed clutter model, we develop a generalized-likelihood ratio test (GLRT) for moving target detection in distributed MIMO radar. The GLRT is shown to be a constant false alarm rate (CFAR) detector, and the test statistic is a central and noncentral Beta variable under the null and alternative hypotheses, respectively. Simulations are provided to demonstrate the performance of the proposed GLRT in comparison with several existing techniques.

The nonhomogeneous clutter is one of the biggest challenge for ocean remote sensing radar system such as high frequency hybrid sky-surface wave radar. Space-time adaptive processing (STAP) is a powerful method to suppress the clutter but the training samples in adjacent range gates are not always independent and identically distributed with the cell under test in the heterogeneous environment. So the estimation of the clutter covariance matrix will be inaccurate and degrade the STAP performance. In this paper, a novel training sample selection method based on information geometric means is proposed to choose the more homogeneous secondary data containing the precise clutter information of cell under test. The simulation results of selection performance is conducted to show the advantages of the Information geometric mean based estimators compared with the existing estimators. Combined with joint domain localized STAP, the effectiveness of the proposed method is verified by experimental data. The results show the proposed method outperforms conventional STAP method and the nonhomogeneous clutter can be greatly suppressed.

Polarimetric weather radars, such as the Weather Surveillance Radar-1988 Doppler (WSR-88D), improve weather forecasts and provide valuable data for operational and scientific applications. The polarimetric capability adds additional insight into storm microphysics and greatly improves precipitation estimates. Nevertheless, fast-evolving weather events require high-temporal resolution data, which conventional radar systems (mechanical and dish-based) cannot provide. Phased array radar (PAR) offers superior observation capabilities with electronic beam steering and enhanced scanning agility. Furthermore, digital PAR enables 1-D (space and time) processing and overcomes limitations in clutter mitigation compared with the traditional radar systems that only use Doppler processing. Doppler processing is traditionally used to effectively filtering out ground clutter with zero mean velocity. In contrast, space-time processing (STP) enhances clutter mitigation to filter out both stationary and moving clutter through the joint spatial and temporal spectrum. This study aims to apply STP (nonadaptive) and space-time adaptive processing (STAP) to weather radar data and explore their benefits to improve Doppler velocity estimation of meteorological returns. Furthermore, the performance of STP and STAP for different digital PAR back ends, including fully digital and subarray systems, is investigated. Preliminary findings underscore the critical role of radar scanning parameters and environmental conditions, such as sample quantity, clutter-to-signal ratio (CSR), and signal-to-noise ratio (SNR), in the Doppler velocity estimation. Data collected with the recently completed Horus radar system are evaluated using STP and STAP. Results demonstrate the potential for improving data quality, particularly in Doppler velocity estimation within cluttered environments, through the application of STP and STAP techniques. The filtering algorithm with STAP demonstrates a substantial reduction in error within the Doppler velocity estimation, achieving approximately an eightfold improvement compared with the estimation derived from STP with filtering.

Performance analysis of some CFAR detectors in homogeneous and non-homogeneous Pearson-distributed clutter

In future there will be a growing requirement to use high levels of spatial and temporal sampling to enable the use of space time adaptive processing (STAP) to improve target detection in complex interference environments. However, in practice, the processing power and training data requirements will restrict the degrees of freedom (DOF) that can be used. A number of algorithms have been developed to take advantage of phased array architectures employing many DOF without suffering from slow processing, poor beam pattern control or inadequate training data. This can be achieved using either adaptive or non-adaptive DOF reduction techniques. We evaluate a number of partially adaptive techniques via simulations with airborne element digitised array radar (EDAR).

Space-time adaptive processing (STAP) is a well-known and effective method to detect targets, obscured by interference, from airborne radars that works by coherently combining signals from a phased antenna array (spatial domain) with multiple radar pulses (temporal domain). As widely demonstrated, optimum STAP, in the sense of maximizing the output signal to interference plus noise ratio (SINR), is a coherent, linear, transversal filter (i.e., tapped delay line), that can be synthesized by a complex-valued weight vector. This paper extends previous work that focused on adaptive spatial-only nulling; it derives the optimum phase-only STAP, namely, the optimal weight vector that maximizes the SINR subject to the constraint it belongs to the $N$ -torus of phase-only complex vectors, where $N$ is the number of spatio-temporal degrees of freedom. Because this problem does not admit a closed-form solution, it is solved numerically using the phase-only conjugate gradient method (CGM). The effectiveness of phase-only STAP is demonstrated using both SINR values and receiving beampattern shape, comparing it with the optimum fully-adapted STAP and the nonadapted beam former responses as well as other possible counterparts. Additionally, several analyses of practical utility also demonstrate the benefits provided by phase-only STAP.

Novel acousto-optic systems for spectrum analysis and phased array radar signal processing are proposed. The pulsed source 2-D multiplicative time and space integrating spectrum analyzer is proposed and experimentally demonstrated. This architecture is simpler, physically smaller, and less sensitive to mechanical vibrations than the pulsed source 2-D additive Mach-Zehnder interferometer based spectrum analyzer. Simple and compact optical disk based systems for 1-D and 2-D spectrum analysis are presented. The high data packing density, angular motion, and parallel read out capability of optical disks is uniquely exploited to generate the very high time bandwidth product reference signals required for temporal spectrum analysis, that are otherwise difficult to generate using electronics. A continuous wave 1-D time integrating disk spectrum analyzer is successfully demonstrated in the laboratory. The limited dynamic range due to huge bias levels in interferometric time integrating processors is alleviated by using photorefractive crystals as real time bias removers. Continuous wave 1-D and 2-D bias free acousto-optic architectures for spectrum analysis are theoretically and experimentally investigated from a systems viewpoint. Simple, compact, and powerful acousto-optic systems for control and signal processing in phased array radars are proposed that eliminate many system components like phase shifters, mixers, power dividers, and complex antenna feed networks, that are encountered in typical electronically controlled phased array radars. Via a single control parameter, namely frequency, the optical technique provides appropriately phased signals for transmission and reception in phased array antennas. The system is capable of continuous beam scanning, simultaneous multiple beam formation, and multi-target tracking, and can be integrated on a substrate to provide smaller and lighter phased array radars. Also, the system is resistant to electromagnetic interference (EMI), and signal phase shifts are independent of the radar carrier frequency employed. A 1-D linear phased array radar acousto-optic beam steerer is successfully demonstrated in the laboratory, and performance issues such as detector phase sampling error and phase linearity are highlighted. The linear array design is extended to planar and multiple linear arrays.

Detection algorithms in a nonhomogeneous clutter environment have to contend with the problem of excessive false alarm rates when the clutter is not sufficiently suppressed. Prior to signal detection, adaptive receivers must either "null" or "whiten" the clutter. The former approach requires estimates of the basis vectors of the clutter subspace while the latter requires estimates of the covariance matrix of the clutter. It may be preferable to null the clutter when the dimensionality of the clutter subspace (M) is small in comparison to the length of the data vector coherently processed (N) and also when the signal to be detected is not substantially within the clutter-plus-noise subspace. Depending on the scenario, radar clutter data may have a variety of effects such as clutter discretes, data dropouts due to terrain masking that cause shadowing, multipath effects, and non-Gaussian amplitude statistics. We consider a statistical model which can account for some of these effects and apply the model to design an algorithm to obtain a maximum likelihood estimate (MLE) of the orthonormal basis vectors of the clutter-plus-noise subspace. We show that the problem can be reformulated to develop a recursive approach for MLE based on the expectation-maximization (EM) algorithm. Results of the analysis show that each training vector must be scaled by a nonnegative scale factor from the interval [0,1] prior to subspace estimation. The scale factor applied to each training vector increases monotonically with the subspace clutter-plus-noise power of the vector. Conditions for the existence and uniqueness of the solution are considered. A statistical interpretation of the solution is provided along with sample simulation results.

Due to the need for adequate statistically homogeneous training, full-dimensional space-time adaptive processing (STAP) is well accepted to be impractical. Several previous works have addressed this issue by reducing the adaptive degrees of freedom (DoF), in turn reducing the required training. In this paper, we introduce a new multistage STAP approach that significantly reduces the required sample support while still processing all available DoF. The multistage fast fully adaptive (FFA) scheme draws inspiration from the butterfly structure of the fast Fourier transform. It uses a “divide-and-conquer” approach by creating several smaller STAP problems but then combines the outputs of each problem adaptively as well. The reduction in required sample support rivals currently available reduced DoF algorithms. We also develop three variants of the algorithm, including one that uses random subdivisions of the original STAP problem.We test the efficacy of the algorithms developed via simulations based on simulated airborne radar data and measured high-frequency surface wave radar data. The results show that for simulated homogeneous data, the performance of the FFA approaches is comparable to that of available STAP algorithms; however, with measured data, the FFA approach provides significantly better performance.

Conventional Space-time adaptive processing (STAP) requires large numbers of independent and identically distributed (i.i.d.) training samples to ensure the clutter suppression performance, which is hard to be achieved in nonhomogeneous environment. In order to obtain improved clutter suppression with small training support, an iterative sparse recovery STAP algorithm is proposed in this paper. In the proposed method, the clutter spectrum sparse recovery and the calibration of space-time over-complete dictionary are implemented iteratively, modified focal underdetermined system solution (FOCUSS) with recursive calculation is used to alleviate the recovery error and reduce the computational cost, meanwhile the mismatch of space-time overcomplete dictionary is calibrated by minimized the cost function. Based on the simulated and the actual data, it is verified that the proposed method can not only converge with much smaller training samples compared with conventional STAP methods, but also provide improved performance compared with existing sparsity-based STAP methods.

This paper presents a novel concept for knowledge-based control of space-time adaptive processing. Firstly use a-prior knowledge to pre-adaptive, and then use non-homogeneity detector (NHD) to distinguish homogeneous range cells and non-homogenous ones. Secondly respectively use statistical algorithm and two-stage hybrid approach or other non-statistical approach to process homogeneous range cells and non-homogeneous range cells, to detect and track target. Lastly Mountaintop data simulation results show that this knowledge-based space-time adaptive processing approach significantly enhances the processing performance for airborne surveillance application in practical complex environment.