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Sparse recovery (SR) based space‐time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples. However, most of the existing SR‐STAP algorithms suffer from the off‐grid effect induced by the discretisation of angle‐Doppler plane. To eliminate this effect and improve the performance of clutter suppression, a novel gridless mixed‐norm minimisation (MNM) based STAP algorithm is proposed. Unlike previous grid‐based SR‐STAP methods, the clutter covariance matrix (CCM) is estimated in continuous domain by solving a semidefinite programme (SDP), which is established by utilising the properties of the CCM and the compact formulation of MNM. Furthermore, to reduce the computational burden of solving this SDP, a fast iterative algorithm via the framework of the alternating direction method of multipliers (ADMM) is also derived, where the unknown parameters are iteratively updated with closed‐form expressions. Simulation results show that our proposed algorithm achieves better performance of clutter suppression and target detection with lower computational complexity.

In order to avoid straddling loss, airborne phased array scanning radar transmits beams with small step angle, and then the same target can be detected again and again in multiple adjacent beams through azimuth scanning. Therefore, considering its long-range surveillance and high revisit rate, the authors may assume that the azimuth angle of the same target almost keeps unchanged during multiple beams. Furthermore, they can also model the target complex amplitude from each beam as an unknown deterministic signal with amplitude and phase difference that accounts for the antenna pattern and sampling starting time. A direction-of-arrival estimation algorithm that utilises multiple beams to obtain an accurate target angle is proposed. The signal model that jointly considers received data from multiple adjacent beams is introduced, and the maximum likelihood (ML) estimation of target angle in noise and jamming sceneries is obtained. The performance of proposed method is investigated through Monte-Carlo simulations and compared with both the Cramer-Rao lower bound and with conventional ML technique that utilises one beam. Simulation results validate the effectiveness of the presented algorithm.

Space time adaptive processing (STAP) is an excellent technique for improving ground moving target detection performance of airborne radar. However, covariance matrix estimation required by STAP is commonly corrupted by the presence of target-like signals (i.e. outliers), and thus resulting severe performance degradation. To overcome this problem, a robust non-homogeneity detector based on reweighted adaptive power residue is developed, where an adaptively reweighted scheme is employed to training data set. Therefore, the deleterious effect of outliers on the covariance estimation is eliminated and the robustness of the non-homogeneity detector is guaranteed. Performance analysis using the simulated and measured data validates that the proposed method can effectively remove outliers from the training data and improves the radar detection performance in a dense target environment.

The use of spaceborne synthetic aperture radar (SAR) for ground moving target indication (GMTI) has recently attracted a great deal of interest. In this study, an efficient imaging algorithm for spaceborne SAR/GMTI systems is proposed. The proposed imaging algorithm first implements the secondary range compression and the range cell migration correction in the two-dimensional frequency domain and then the azimuth compression in the range Doppler domain. The proposed algorithm can finely focus the stationary scene and coarsely focus multiple slow-moving targets simultaneously without a priori knowledge of targets’ motion parameters and position parameters. Moreover, the proposed algorithm is interpolation free and can be efficiently implemented by using only complex multiplications and fast Fourier transforms. These advantages make the proposed algorithm useful in practice. Experimental results from both simulated data and real data validate the proposed algorithm.

Clutter suppression is a challenging problem in non-side-looking airborne radar (NSLAR) because of the range-dependent clutter. It is more intractable when the NSLAR operates at high pulse repetition frequency (HPRF). In this study, a cascaded algorithm is presented to realise clutter suppression for NSLAR with HPRF. A novel orthogonal-waveform-based elevation adaptive beamforming method is proposed to suppress range-dependent near-range clutter at the first stage, and the residual range-independent far-range clutter is suppressed by azimuth-Doppler space-time adaptive processing (STAP) at the second stage. The proposed elevation beamformer can provide excellent performance on near-range clutter suppression because of the developed secondary data selection strategy. After suppression of the range-dependent near-range clutter, the range independency of clutter samples is enhanced evidently. Hence, the clutter suppression performance obtained by azimuth-Doppler STAP methods could be improved dramatically. Numerical examples are given to verify the validity of the proposed algorithm.

Clutter suppression for short-range ground moving target indication in forward looking airborne radar is a challenging problem because of the range-dependent clutter. If the pulse repetition frequency is chosen so high that radar clutter is range ambiguous, then additional blind velocities at short-range induced by far-range clutter will occur. To eliminate these additional blind velocities, an efficient beamspace-based adaptive prefiltering method in elevation is proposed in this study to suppress ambiguous far-range clutter. The proposed beamformer can provide nearly optimal adapted elevation pattern because of the improved training data selection strategy. After eliminating the impact of ambiguous far-range clutter, the Doppler-compensation-based space–time adaptive processing method could be carried out to suppress residual range-dependent short-range clutter. In addition, the proposed prefiltering method is computationally efficient, and hence it is suitable for engineering application.

For non-sidelooking airborne radar operating in medium pulse repetition frequency mode, the space–time adaptive processing (STAP) technique suffers from a significant performance loss due to the range dependence of clutter. Here, an efficient clutter suppression approach is presented to improve STAP performance. Utilising the characteristics of clutter, the near-range clutter, the major reason for the performance degradation, is extracted from raw returns using a specially designed oblique projector firstly, and then is mitigated by an adaptive beamformer estimated from the extracted components. Subsequently, an azimuth-Doppler STAP beamformer is employed to further suppress the residual clutter. To enhance the robustness of the approach, a simple steering vector calibration technique is taken into account. The proposed approach can eliminate the near-range clutter effectively, and therefore alleviate the clutter range dependence greatly. The STAP performance can be improved significantly. Simulation results demonstrate the effectiveness of the presented method.

Multichannel synthetic aperture radar (SAR) provides a powerful means for ground moving target indication (GMTI) purpose. The channels are often mismatched because of various kinds of errors. However, perfectly calibrated channels are always needed to perform an effective GMTI. In this study, the authors present a two-stage channel calibration technique to compensate the channel errors. The authors find that the channel errors can be classified into two categories, that is, the so called convolutional and multiplicative errors. The convolutional errors are more suitable to be compensated in two-dimensional (2-D) frequency domain, whereas the multiplicative errors should be compensated in image domain. The authors show that the convolutional errors can be effectively compensated by a Stage 1 calibration filter, in which the conventional 2-D frequency domain calibration methods can be employed. To compensate the multiplicative errors, the authors introduce a median filter as a Stage 2 calibration filter in the image domain. The proposed two-stage technique is tested with real SAR data obtained from terribly mismatched channels and is found to work well.

Clutter suppression for airborne non-sidelooking radar is a challenging problem due to the range-dependent clutter. In this study, an algorithm combining elevation robust Capon beamforming (ERCB) and two-dimensional azimuth-Doppler space-time adaptive processing (STAP) is presented to realise clutter suppression for airborne non-sidelooking radar. The presented algorithm can place range-dependent nulls to eliminate the range-dependent clutter and the residual range-independent clutter can be suppressed by two-dimensional azimuth-Doppler STAP methods. Numerical examples are given to demonstrate the effectiveness of the presented algorithm.

The clutter characteristics of bistatic airborne radar are more complex than those of monostatic airborne radar. The clutter spectra not only vary severely with range, but also vary with bistatic configuration. The problem of range dependence is more serious in monostatic airborne radar. In this paper, the geometry of arbitrary bistatic airborne radar configuration is firstly analysed, and a formula for Doppler frequency calculation with the variables of azimuth angle and bistatic range is deduced, which is an efficient tool for bistatic clutter analysis. Because of the severe clutter range dependence, the processing of compensation is indispensable in space time adaptive processing (STAP). However, when range ambiguity occurs, the compensation is difficult to be applied to each clutter range cell. To solve this problem, a range ambiguity resolving approach is further proposed by utilising azimuth elements in phased array. Because this approach will result in spatial degrees of freedom (DOF) loss, the overlapped subarray processing is introduced in order obtain enough spatial DOF for STAP. By doing so, the compensation for mitigating range dependence can be applied effectively to bistatic clutter.