In this letter, the clutter rank estimation formulas for end-fire array airborne radar (EFAAR) in two typical scenarios are derived. In comparison to the airborne radar with a traditional broadside array, the clutter rank of the EFAAR in the forward-looking scenario still maintains a reasonably accurate estimation criterion. The effectiveness of the proposed clutter rank formulas is validated, and the impacts of certain parameters on the clutter rank are analyzed through simulations. Simulation results indicate a direct proportionality between clutter rank and the number of array elements, coherent processing pulses, and clutter ridge slope.

For nonside-looking airborne radar (NSLAR), the range-dependent near-range clutter degrades the performance of space–time adaptive processing (STAP), especially in the high-pulse-repetition-frequency mode. To solve this problem, a novel algorithm using sparse recovery (SR) and orthogonal projection (OP) is proposed in this letter. First, the elevation angle of short-range clutter is estimated by the off-grid SR method. Second, the near-range clutter is suppressed in the elevation domain by the OP method. Finally, the azimuth–Doppler STAP is carried out to suppress the residual range-independent far-range clutter. The proposed algorithm can obtain the elevation angle of short-range clutter more precisely with only one training snapshot and it is robust since no prior knowledge is needed. The simulation results validate the effectiveness of the proposed algorithm.

As training samples are not always target-free in heterogeneous environments, the generalized inner product (GIP) method is usually used to censor the training samples contaminated by targetlike signals (outliers). However, the GIP method incurs significant performance degradation when there are multiple outliers in the original training sample set. To deal with this problem, this letter proposes a novel GIP method. First, the principal component, which results in performance degradation of the GIP method, is obtained via extracting the maximum of cross-spectral metric (CSM) between the target steering vector and the eigenspace of the GIP's test covariance matrix (TCM). Second, taking a sample covariance matrix (SCM) as the initial TCM, a new TCM is reconstructed by setting the eigenvalue of SCM that corresponds to the largest CSM between the target steering vector and the SCM's eigenspace to be noise variance. Finally, the new TCM is combined with the conventional GIP method to form a novel GIP statistic to eliminate the contaminated training samples. Numerical results with both simulated and mountain-top data confirm the improvement of the proposed method.

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.

Subspace space-time adaptive processing (STAP) algorithms are able to eliminate clutter completely. However, when the number of training samples is smaller than the clutter rank, the performances of subspace STAP algorithms degrade severely due to the inaccurate estimate of clutter subspace. To remedy this problem, a novel subspace STAP algorithm is proposed. In the proposed algorithm, the entire clutter subspace is constructed by two portions. The direct portion is estimated by a conventional method from limited training samples, while the supplemented portion is constructed by some space-time steering vectors selected from an overcomplete space-time steering dictionary. Clutter suppression is achieved by projecting the data into the subspace orthogonal to the clutter subspace. Numerical results with both simulated and mountain-top data demonstrate that the proposed algorithm has superior performance in a finite-training-sample situation.

In this letter, we consider the problem of detecting a distributed target with unknown signal steering in the Gaussian noise. We derive the Rao and Wald tests. It is found that the Rao test coincides with the so-called modified two-step generalized likelihood ratio test (M2S-GLRT), while the Wald test is equivalent to the plain two-step GLRT (2S-GLRT). We also give some intuitive interpretations about the Rao and Wald tests, as well as other existing detectors.

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.

In this paper, in light of the imaging characteristics of missile-borne MMW spotlight SAR for ground target, some main problems including the imaging resolution and choice of pulse repetition frequency are discussed. Then a new scheme of motion compensation which consists of range alignment, phase adjustment and compensation for the movement with non-constant velocity is presented. Image reconstruction algorithm by using superresolution spectrum estimation is given based on linear spectrum extrapolation technique. Finally the simulation results prove its effectivity.