Space-time Adaptive Processing Clutter Research Articles

A STAP clutter suppression algorithm based on sparse representation for small-array HFSWR

ABSTRACT In high-frequency surface wave radar (HFSWR) systems, the main-lobe of the receiving angle spectrum suffers from severe broadening due to a small-array aperture effect. This broadening causes more clutter components to be mixed into the main-lobe, resulting in target vessels more prone to be submerged in the clutter. This problem severely affects the target detection performance. A reduction in the number of array elements decreases the degrees of freedom (DOF), making clutter suppression more difficult. Space-time adaptive processing (STAP) has recently become a crucial tool for clutter suppression in advanced HFSWR systems, using two-dimensional data to increase DOF. Focusing on low DOF and typical nonhomogeneous clutter with high energy, this study proposes a STAP clutter suppression method by estimating clutter components accurately in the space-time domain based on sparse representation in few snapshots. The experimental results verify that the proposed algorithm provides high suppression performance for measured clutter.

Sparse recovery-based STAP method using prior information of azimuth-elevation

By exploiting the prior information of azimuth-elevation (AE), a sparse recovery (SR)-based space-time adaptive processing (STAP) method called AESR-STAP is proposed. First, the supercomplete properties matrix in the SR is built based on flight configuration prior information such as elevation and azimuth to estimate the azimuth-elevation spectrum of the ground clutter in different range cells. Then, the range ambiguity STAP clutter can be eliminated according to the difference of the elevation of the clutter in different range cells. Finally, the AESR-STAP utilizes the spatial and temporal coupling relation of the ground clutter, namely, the nonlinear relation among the azimuth, the elevation, Doppler frequency, and spatial frequency, to estimate the clutter distribution in time and space dimensions and to calculate the clutter covariance matrix. Theoretical analysis and simulation experiments have shown that the proposed method can estimate the temporal and spatial distribution more accurately, eliminate the range ambiguity STAP clutter to improve the clutter suppression of the airborne radar, and effectively detect ground low-speed targets.

Benefits of space–time adaptive processing (STAP) in bistatic airborne radar

The paper investigates the merits of using STAP for clutter suppression in bistatic airborne radar. Particular emphasis is placed on the clutter angle-Doppler inter-relationships in bistatic geometries, and it is demonstrated that there can be ground regions where small changes in arrival angle imply large changes in the Doppler shift. STAP clutter rejection methods are not ideally suited to these cases. It is also shown that the angle-Doppler inter-relationships vary with the ground topography, but the effects of this are usually small. Finally, it is shown that the clutter-suppression performance is dependent on the transmit and receive beam mainlobe/sidelobe interactions.

Space-time adaptive processing for manoeuvring airborne radar

Space-time adaptive processing (STAP) techniques provide simultaneous rejection of jamming and clutter in airborne radar. The greatest benefits over conventional MTI (moving target indication) approaches are in terms of a capability to detect slow-moving targets which possess the same Doppler frequency as mainlobe clutter returns. This paper examines the effects of platform manoeuvre on STAP clutter and jamming rejection performance for a forward-facing array (i.e. where the array is orientated transversally to the direction of travel). It is shown that STAP slow-target detection performance is not sensitive to the radar platform orientation. It is also demonstrated that, under conditions of manoeuvre, STAP can provide better jammer rejection performance than architectures which cascade conventional clutter filtering and spatial adaptive beamforming.