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The principle and cost analysis of Constraint-Based Space-Time Adaptive Monopulse (C-STAM) are given. Based on the idea of cognitive radar, a novel Knowledge-Aided Constraint-Based Space-Time Adaptive Monopulse (KA-C-STAM) is proposed. With the knowledge given by a tracking filter in data processing, the KA-C-STAM improves the performance of angle estimation. Numerical examples verify the validity of the novel method.

This study focuses on estimation of clutter degrees of freedom (DOFs) for airborne multiple-input multiple-output (MIMO)-phased array radar. Firstly, two equivalent clutter models are presented, according to the transmitting subarray beampattern and synthetic-phased centre, and based on the basic steering vector. Secondly, a convenient estimating rule of clutter DOFs for airborne MIMO-phased array radar under arbitrary transmitting subarray structures is presented and proofed strictly. Finally, the simulated results indicate that airborne MIMO-phased array radar is effective both in spatial selectivity and clutter suppression, and the clutter DOFs under arbitrary transmitted subarray structures can be estimated by the proposed rule exactly. The proposed models and rule are suitable for both conventional-phased array radar and airborne MIMO radar with uniform or non-uniform transmitting subarray and sparse array, and they are valuable for the improvement of space-time adaptive processing algorithm performance.

This paper addresses the issues of clutter characteristics of airborne MIMO radar in presence of degradation of transmit waveforms orthogonality. Firstly, synthesis subarray space and synthesis transmit signal space caused by the degradation are illustrated. Clutter model of airborne MIMO radar is developed under the orthogonal degradation. Clutter models with arbitrary waveform are unified in this model via the transform of array space and signal space. Secondly, an estimation rule for the number of clutter DOFs is presented based on band-limited signal theory. It shows that clutter DOFs is dependent on both the number of inner elements in synthetic subarray and synthetic signal. Finally, clutter characteristics under the influence of degradation are analyzed compared with the ideal MIMO radar model and phase array radar, and the effectivity of the estimation rule is verified in the numerical examination.

To solve the problem of nonhomogeneous clutter suppression for moved target detection in the airborne phased array, this paper presents a new space time adaptive processing (STAP) method which is named D3AR. This method firstly filters the target information of cell under test (CUT) based on the phase information of target. Then constructs the AR model to obtain the clutter characteristic. Finally gets the space orthogonal to the clutter subspace based on the AR filter coefficients. This method doesn't require the adjacent training data and is suited for nonhomogeneous clutter environment. The simulation results demonstrate its effectiveness.

A novel knowledge-aided reduced rank (RR) space-time adaptive processing (STAP) method is presented in this paper. Firstly, a clutter model is constructed by priori knowledge such as flying parameters, radar system parameters, mapping and terrain parameters. Secondly, the clutter is performed via the pre-whiten processing of the constructed clutter model. Finally, RR STAP method is used to suppress the residual clutter. The simulation result shows the KA-RR method has better robustness in nonhomogeneous clutter environment compared with conventional RR STAP method. (5 pages)

The non-sidelooking array is different from the sidelooking array in that its Doppler frequency of the clutter spectrum varies with the range and its clutter degree of freedom (DOF) is increased, so that the conventional STAP methods are not feasible. The clutter distribution of airborne non-sidelooking array radar is analyzed and investigated in detail, and a new clutter suppression approach to airborne phased radar with non-sidelooking array based on covariance matrix taper (CMT) is proposed. The simulation results verify the effectivity of the method

Suboptimum performance may be obtained by partially adaptive processing at the subarray stage where the problem of how to group the subarrays is an important task. In this paper, an effective subarray partition method based on genetic algorithm is proposed, which divides a uniform linear array employing amplitude tapering into contiguous subarrays of unequally sized. In the genetic algorithm, the crossovers are prevented by including an appropriate penalty term in the cost function. The simulation results are given to show that the subarray configuration has few obvious grating lobes while its clutter suppression performance is close to that at the element stage