Space-Time Adaptive Processing for noise-radar

In this letter, the local degree-of-freedom (LDOF) theorem for reduced-dimension space-time adaptive processing (STAP) methods is presented, and a rigorous proof is provided. LDOF is more valuable for practical STAP methods than conventional full degrees of freedom. The effectiveness of the LDOF theorem is verified, and the influences of some operations in practice on the LDOF are analyzed by simulations.

The space-time adaptive processing (STAP) in the airborne multiple-input multiple-output (MIMO) radar with nested structure is studied. According to the special structures of arrays and transmitting pulse interval, a virtual space-time snapshot vector with longer length is constructed. Both the virtual antenna array and the virtual pulse train formed by the nested array and nested pulse train are successive and hole-free. Then a Hermitian matrix is built to estimate the full rank clutter covariance matrix in airborne MIMO radar with nested structure. For the increasing DoFs, the STAP method can achieve higher upper bound in the airborne MIMO radar with nested structure than the STAP in the traditional airborne MIMO radar, which facilitates the clutter suppression and target detection. Simulation results verify the proposed STAP method.

A method that further exploits the sparsity of weight vector in space-time adaptive processing (STAP) and decreases the training sample demanding is proposed. In the proposed method, STAP with the generalized sidelobe canceler (GSC) structure by using the L p -norm regularization, instead of the conventional L 1 -norm regularization, is considered. The core idea behind the method lies in minimizing the cost function that combines the output error and the L p -norm of weight vector, which is a more accurate sparsity representing operator than L 1 -norm. For solving the desired weight vector, the recursive least squares (RLS) algorithm with respect to the gradient of L p -norm is deduced. In addition, since the diagonal loading factor cannot be clearly defined, the L p -norm GSC STAP with parallel structure is designed by choosing the branch that maximizes the output signal to clutter ratio. In the end, the simulation experiments are conducted to verify the performance of the proposed L p -norm GSC STAP.

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.

Although space-time adaptive processing (STAP) is recognized as the optimal clutter suppression way for synthetic aperture radar (SAR) in theory, the deficient of independent and identically distributed range samples in real scenario limits its application. The reduce-dimension STAP methods can decrease the demand for range samples, but the assumption of moving target-free is always unsatisfied. The direct data domain methods only use the data of the range cell under test (RCUT) to avoid the assumption, but they are conducive to interference suppression than clutter suppression and have huge computational burden. Thus, in this letter, a single range data-based STAP method is proposed not only exploring the space-time statistical properties of clutter to suppress it, but also operating solely on the RCUT without recourse to range samples. Theoretical analyses and simulation results verify the effectiveness of the proposed method.

Space-time adaptive processing (STAP) for range-dependent clutter rejection in airborne radar is considered. Indeed, radar antenna architectures or configurations that are different from the conventional uniform linear antenna array (ULA) and side-looking (SL) configuration have consequences on the clutter properties. The authors here investigate the use of Taylor series expansions (TSEs) of the space–time covariance matrix in the classical sample matrix inversion (SMI) STAP method in order to mitigate the range non-stationarity of the clutter and they compare it to the derivative-based updating (DBU) already proposed in the literature. The authors also propose a new algorithm based on a TSE of the clutter plus noise subspace in conjunction with the eigencanceler-based (EC) STAP, which improves the performance in term of signal-to-interference plus noise ratio (SINR) loss, compared to the DBU method. In this study, the particular cases of a ULA and a uniform circularly curved antenna (UCCA) array in SL and non-SL monostatic configurations as well as a ULA in some bistatic configurations are considered for the test and the comparison of the presented algorithms.

For a general conformal array, the angle-Doppler spectrum of surface clutter in monostatic radar systems is range dependent This results in a mismatch of the clutter covariance matrix (computed from a secondary set of range-cell data) relative to the clutter covariance matrix of a test cell, with attendant degradation of space-time adaptive processing (STAP) performance. Unlike for a line array, the clutter covariance matrix of a range cell depends on both its elevation and azimuth angles. This work develops a least-squares method for estimating the system configuration parameters from the secondary data. The method uses a multiple complex sinusoid model whose parameters are related to the system configuration parameters which are then optimized to match the radar return spatial-temporal data in a least-squares sense. The estimated configuration parameters are then used to predict the clutter covariance matrix in the test cell which is then used with STAP methods. Computer simulation results are presented to show the improved STAP performance compared to standard compensation methods.

Space-time adaptive processing (STAP) is an effective method for detecting moving target in airborne/spaceborne radar. However, the detection of fast air moving targets from high speed platform is challenged by the range walk of both the clutter and moving targets. It is well known that keystone formatting can be used to compensate for the range walk of multiple moving targets simultaneously without using the knowledge of the motion parameters. However, in the presence of serious Doppler ambiguity of fast air moving targets, distribution of the clutter will be affected by the keystone formatting matched to the ambiguity number of targets, and as a result the STAP performance will degrade. In this paper, a novel STAP method is proposed for the detection of fast air moving targets from high speed platform, which can deal with the range walk of both clutter and targets. Effectiveness of the new method is verified via simulation examples.

Space-time adaptive processing (STAP) is an effective technology in clutter suppression and moving target detection for airborne radar. Because airborne radar moves at a constant acceleration, and there is a lack of independent and identically distributed (IID) training samples caused by the heterogeneous environment, using the conventional STAP methods directly cannot ensure a good performance. To eliminate these effects and improve the performance of clutter suppression, a STAP method based on a sparse Bayesian learning (SBL) framework for uniform acceleration radar is proposed here. This paper introduces the signal model of the uniform acceleration radar. To promote the sparsity, a generalized double Pareto (GDP) prior is introduced into our method, and the estimation of hyper parameters via expectation maximization (EM) is given. The effectiveness of the proposed method is demonstrated by simulations.

This research presents two new space-time adaptive processing (STAP) algorithms; a two-dimensional non-statistical method and a hybridisation of this approach with statistically based methods. The non-statistical algorithm developed here allows filtering of uncorrelated interference, such as discrete interferers, within the range cell of interest. However, the performance of these algorithms in homogeneous correlated interference scenarios is inherently inferior to traditional statistical STAP algorithms. The proposed hybrid algorithm alleviates this drawback by implementing a second stage of statistical adaptive processing. This paper illustrates the advantages of using a two stage adaptive process to combine the direct data domain and statistical algorithms. The work presented in this paper brings together two different aspects of STAP research: statistical and direct data domain processing. In doing so, this research fulfils an important need in the context of practical STAP processing.

In this paper, STAP applied on signal from uniformly spaced real elements using D³LS. The mutual coupling between real elements will affect on the estimation of complex amplitude of Signal of Interest (SOI). It is necessary to use a transformation matrix to compensate for the strong mutual coupling that exists between the antenna elements. The transformation matrix converts the voltages that are induced at the loads corresponding to the feed point of the array to an equivalent set of voltages that will be induced by the same incident wave in Uniform Linear Virtual Array (ULVA). Then, we will apply D³LS STAP on the compensated voltages. Numerical simulations are done using the three main methods of D³LS namely forward, backward, and forward-backward methods.

Traditional space-time (ST) adaptive processing (STAP) theory is based on the assumption of narrowband or “zero-bandwidth,” where the decorrelation within the ST snapshot is ignored. However, with radar bandwidths increasing, this assumption becomes invalid due to the deteriorated decorrelation of the received signals within the ST snapshot. The decorrelation directly causes the dispersion of the received signals in both spatial and temporal domains, leading to the spreading of the clutter spectrum in the 2-D frequency (Doppler-spatial frequency) domain. With the spreading of the clutter spectrum, the clutter suppression notch in the traditional STAP filters is widened, resulting in a relative poor ability to detect slow-moving targets. In this article, we focus on the clutter suppression for wideband radar STAP. A generalized signal model of the ground clutter is first established for the wideband array radar. Using this outcome, we analyze the influence of bandwidth on the characteristics of the ground clutter and quantitatively describe the 2-D spreading of the ground clutter on the Doppler-spatial frequency plane. Moreover, the model of clutter covariance matrix for wideband STAP (W-STAP) is established. Finally, a 2-D keystone transform (KT) algorithm, referred to as ST KT (ST-KT), is proposed to eliminate the spreading of the ground clutter in the 2-D frequency domain caused by increasing bandwidths. Simulation results are employed to validate the theoretical analysis and verify the overperformance of the ST-KT based W-STAP method in terms of the output signal-to-clutter-plus-noise ratio (SCNR) of moving targets.

Both fully adaptive and partially adaptive processing algorithms can be implemented using linear and nonlinear conjugate gradient (CG) methods. This approach can be computationally attractive when a family of continuous time-varying adaptive problems must be solved. An iterative approach that starts with the previous solution can converge quickly to the new solution. This paper considers linear and nonlinear CG methods for adaptive processing. The example used is space-time adaptive processing (STAP) and clutter interference subspace tracking within the context of an airborne radar with a rotating array. It is seen that the linear CG method for adaptive filtering in this context can be several times less costly than sample matrix inversion if the interference subspace has sufficient eigenvalue structure, and that a nonlinear CG algorithm is capable of tracking the principal clutter interference subspace.

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

A Direct Data Domain Least Squares (D³LS) approach is developed for multiple target detection in Space Time Adaptive Processing (STAP) and is applied on signal received from different configurations of real elements. We are interested in three different kinds of antenna configuration which are non-uniformly spaced linear array, semicircular array and sinusoidal spaced array. The mutual coupling between real elements will affect on the estimation of complex amplitude of Signal of Interest (SOI). It is necessary to use a transformation matrix to compensate for the strong mutual coupling that exists between the antenna elements. Then, we will apply the multiple constrained STAP D³LS on the compensated voltages. Numerical simulations are done using the three main methods of D³LS namely the forward, backward, and the forward-backward methods and it show that multiple beams are directed towards target directions maintaining their gains constrained along those directions.