Distributed radar array brings several new forthcoming advantages in aerospace target detection and imaging. The two-dimensional distributed array avoids the imperfect motion compensation in coherent processing along slow time and can achieve single snapshot 3D imaging. Some difficulties exist in the 3D imaging processing. The first one is that the distributed array may be only in small amount. This means that the sampling does not meet the Nyquist sample theorem. The second one refers to echoes of objects in the same beam that will be mixed together, which makes sparse optimization dictionary too long for it to bring the huge computation burden in the imaging process. In this paper, we propose an innovative method on 3D imaging of the aerospace targets in the wide airspace with sparse radar array. Firstly, the case of multiple targets is not suitable to be processed uniformly in the imaging process. A 3D Hough transform is proposed based on the range profiles plane difference, which can detect and separate the echoes of different targets. Secondly, in the subsequent imaging process, considering the non-uniform sparse sampling of the distributed array in space, the migration through range cell (MTRC)-tolerated imaging method is proposed to process the signal of the two-dimensional sparse array. The uniformized method combining compressed sensing (CS) imaging in the azimuth direction and matched filtering in the range direction can realize the 3D imaging effectively. Before imaging in the azimuth direction, interpolation in the range direction is carried out. The main contributions of the proposed method are: (1) echo separation based on 3D transform avoids the huge amount of computation of direct sparse optimization imaging of three-dimensional data, and ensures the realizability of the algorithm; and (2) uniformized sparse solving imaging is proposed, which can remove the difficulty cause by MTRC. Simulation experiments verified the effectiveness and feasibility of the proposed method.

Conventional three-channel monopulse systems necessitate prior knowledge of the power ratio between two unresolved targets to facilitate angle estimation. When a significant power disparity exists, the estimated angle of the weaker target exhibits a bias toward the stronger target. This paper proposes a robust three-channel dual-target angle resolution method that operates without relying on polarization information or expanding to a four-channel configuration. By leveraging readily available prior angle information of one target, the proposed method resolves the angles of two targets within the 3 dB beam mainlobe. This feature enhances its applicability to traditional sum-difference monopulse radar systems with constrained radio-frequency front-end resources. The processing pipeline consists of two key steps: separation and validation. In the separation step, an oblique projection matrix is constructed based on the presumed angle vector of the unknown target and the prior angle vector of the known target to effectively isolate the components of the two targets in the received data. Then, a cost function is designed to evaluate the separation outcome by exploiting the rank-2 characteristic of the echo. Theoretically, the cost function achieves its global minimum if and only if the two target components are correctly isolated. To reduce the computational complexity associated with searching for the unknown target's angle vector, a pre-projection operation is applied to the received data. This operation constrains the solution to a defined line within the angle domain. The angle corresponding to the minimum value of the cost function along this line is then identified as the estimated angle for the unknown target. Comprehensive performance evaluations, conducted through extensive numerical simulations and real-data processing, demonstrate the superiority of the proposed method over existing state-of-the-art approaches, particularly in scenarios with significant power disparities.

The space-time adaptive processing of the airborne distributed flexible array radar is a forefront technology that has attracted much discussion. In this letter, we consider three types of distributed flexible arrays mounted on different aircraft parts: the wing array, the aircraft nose array, and the fuselage array. According to the deformation structure, three types of flexible arrays' space-time signals are modeled. Then, a non-stationary clutter suppression method based on angle- Doppler compensation is proposed. The clutter eigenspectrum, spatial- Doppler spectrum, and STAP performance of three kinds of arrays are compared in numerical simulations, from which the proposed compensation STAP method has a positive effect on the improvement factor for the flexible array, and the aircraft nose array has better STAP performance than the others.

Traditional angle estimation for sum-and-difference monopulse radar systems is predominantly designed for non-coherent sources or relies on fixed closed-form solutions. However, in the presence of coherent sources, these methods often suffer from performance degradation due to data rank deficiency or unavoidable suppression of target power. To address these limitations, this paper presents a single-snapshot angle estimation method for coherent sources by leveraging the angular super-resolution and ranging capabilities of monopulse radar to achieve 3D imaging in the range-angle domain. The approach utilizes the monopulse ratio spatial steering vector as a search vector and projects the received data onto its orthogonal subspace. By exploiting the coupling characteristics between signal polarization and angle, a cost function is constructed to validate the feedback of the search vector. Theoretical analysis demonstrates that for dual-target scenarios, the cost function reaches its minimum precisely when the search vector aligns with a target’s steering vector, enabling the accurate estimation of both targets’ angles. Furthermore, the polarization-angle coupling constraint reduces the 2D angular search space to a 1D line, significantly lowering computational complexity. Simulation results indicate that the method effectively resolves dual targets under single-snapshot conditions and maintains robust performance even with significant energy disparities. Finally, 3D localization of multiple airborne point targets is achieved by integrating 2D angular information with range data, validating the potential of the method for advanced radar imaging and positioning.

High-resolution three-dimensional (3D) radar imaging of non-coherent point target clusters faces significant challenges, particularly severe angular glint induced by the simultaneous presence of dual targets or co-channel interference (CCI) within the antenna mainlobe. Conventional monopulse systems often struggle to resolve such overlapping sources, particularly under conditions of high power disparity between signal components. To overcome the Rayleigh resolution limit, this paper proposes a polarimetric 3D imaging framework for three-channel monopulse radar by leveraging joint polarimetric-angular diversity. By exploiting the intrinsic instability of spatial parameter estimates induced by snapshot-to-snapshot echo envelope fluctuations, a cost function based on fluctuation minimization is constructed. Furthermore, an optimized oblique projection (OP) strategy is developed to decouple overlapped echoes in the joint domain, thereby effectively extracting stable angular features of non-coherent sources under various stochastic scattering scenarios (e.g., Swerling models). Extensive simulations demonstrate that, compared with traditional MPV, Seung, and Blair methods, the proposed approach consistently achieves superior estimation precision and robustness, especially in challenging scenarios characterized by low signal-to-noise ratios (SNR), limited snapshots, and restricted polarimetric diversity. Moreover, experimental validation using real-world data from a 45-m civilian vessel and an active non-cooperative radio frequency (RF) source confirms the practical effectiveness of the algorithm in resolving extended targets in the presence of strong non-coherent background emissions. This work provides a reliable solution for high-fidelity 3D imaging of point target clusters in environments characterized by dense targets and complex electromagnetic interference.

To reduce the weight and cost of radar systems on small aircraft, maintain high angular resolution, and avoid the occurrence of grating lobes, a MIMO sparse array design approach is proposed in this paper. Firstly, the equivalent virtual receiving array is obtained based on the principle of equivalent phase center. Then, the particle swarm optimization algorithm is utilized to continuously adjust the positions of the transmitting and receiving antenna elements to achieve a lower peak sidelobe ratio of the beam pattern. Finally, the robustness of the obtained array against element position errors is investigated. The results indicate that the MIMO sparse array designed by the proposed method can obtain a virtual receiving array with lower sidelobes and is robust against element position errors.