It is challenging to achieve bistatic inverse synthetic aperture radar (Bi-ISAR) imaging and scaling for maneuvering targets. In the Bi-ISAR system, high-order translational and spatial variant (SV) rotational motion errors induced by the target’s maneuvering characteristics and time-varying bistatic angle would severely blur the imaging result. Moreover, both range and cross-range scaling (2-D scaling) are needed to exploit the size information of the target in practical applications. By parametric global modeling and extracting the coupling relationship between the target’s rotational motion and time-varying bistatic angle, this article presents a new Bi-ISAR imaging framework to achieve the integration of high-order motion compensation and 2-D scaling (IHOMC-2S) for maneuvering targets. First, a multidimensional motion errors signal model is developed. Based on the established parametric global model, a joint high-order translational motion compensation and SV autofocus method (JHTSVA) is presented via parametric minimum entropy optimization with the quasi-Newton solver. Then, with the estimated optimal parameters, the effective rotational velocity (ERV) and distortion coefficient can be estimated simultaneously by solving a 1-D unconstrained optimization problem. In addition, in order to successfully perform the 2-D scaling, a data-driven initial bistatic angle estimation method based on the linked feature scatterers is given. It is worth noting that the linear geometric distortion must be corrected before 2-D scaling, otherwise the sheared Bi-ISAR image may lead to an unreliable target recognition result. Finally, underpinned by the efficient and robust approach, IHOMC-2S can achieve high-resolution Bi-ISAR imaging and scaling for maneuvering targets avoiding the selection of prominent scatterers. Several experiments confirm the feasibility and robustness of the proposed algorithm.

The imaging quality and efficiency are equally important in inverse synthetic aperture radar (ISAR) imaging. The high-order spatial variant (SV) phase errors induced by the target’s nonuniform rotational motion would seriously defocus the ISAR imaging results. The focused image can be obtained by exhaustive parameters estimation or optimization processing. However, the high-computational complexity limits its application in real-time imaging. To overcome this constraint, we propose an efficient ISAR imaging and scaling method for highly maneuvering targets by the integration of integrated cubic phase function (ICPF) and parametric spatial variant autofocus (PSVA) in this article. A novel rotational motion parameter estimation method based on ICPF, which only utilizes second-order phase term coefficients, is presented. Then, a parametric global model is established, which can achieve spatial variant (SV) autofocusing of defocused images based on estimated rotational motion parameters. Meanwhile, the cross-range scaling can also be realized using estimated effective rotational velocity (ERV). Without exhaustive parameters estimation and optimization search, the proposed ICPF-PSVA method not only achieves high-precision ISAR imaging but is also computationally efficient compared with the existing methods. Experiments using simulation data and measured data confirm the high efficiency of the proposed method in generating focused images of maneuvering targets.

Traditionally, when multiple targets appear within the radar beam at the same time, the range profiles of different targets are coupled together, the existing algorithms usually image each target separately due to the different motion states of the targets, making it impossible to image multiple targets simultaneously. To overcome this problem, this paper proposes a joint translational motion compensation and imaging method for multiple targets based on an integrated Kalman filter (IKF), which can realize the integration of tracking and imaging for multiple targets. Firstly, an integrated Kalman filter for wideband radar tracking is employed to predict as well as accurately estimate the next-moment motion state of multiple targets simultaneously. Then, with the precisely estimated motion state of the next moment, a joint translational compensation method with a blocked Fourier compensation matrix (BFCM) is proposed in order to compensate for the translational motion of multiple targets simultaneously, which uses the characteristics of the multi-target’s echo signal separated in the range time domain. Finally, by using the IKF and BFCM, the sequential translational motion compensation for multiple targets can be achieved, and the well-focused ISAR images for multi-target are obtained. Finally, the effectiveness of the method is verified by simulated and real data.

The interrupted sampling repeater jamming (ISRJ) can effectively degrade the image quality and affect the subsequent target recognition by creating deceptive multiple false targets on synthetic aperture radar (SAR) images. A time-domain filtering method based on pulse coding to counter ISRJ is proposed in this article. First, this coding method requires the radar to transmit a full pulse signal consisting of multiple subpulse signals several times in the original pulse repetition interval (PRI), and there is a difference in the time distribution of the subpulses transmitted at different moments. Then, use the observation matrix determined by the echo conditions contained in each receiving window to filter the echo in the time domain to obtain the echo corresponding to each subpulse. Finally, the subpulse echo of the jammer sampling section is discarded and the remaining uninterfered subpulse echoes are segmented for pulse compression and subsequent imaging processing to obtain SAR images with a low jamming-to-signal ratio (JSR). Several groups of simulations show that the proposed method is effective against different kinds of ISRJ, and this time-domain filtering method can improve the effect of radar anti-ISRJ and has a high freedom of waveform design.

Bistatic inverse synthetic aperture radar (Bi-ISAR) can obtain complementary information of moving targets and overcome the inherent imaging limitations of monostatic ISAR. However, the complex motion of maneuvering targets invalidates the assumption that the imaging projection plane (IPP) is constant in conventional Bi-ISAR imaging. The 2-D spatial variant phase errors would be induced. Moreover, the phase errors have a high-order form due to the time-varying bistatic angle and the high maneuvering characteristics of the target. Meanwhile, the linear geometric distortion induced by the bistatic configuration seriously challenges target identification and classification. In this paper, we propose a novel method to compensate for the 2-D spatial variant phase errors and correct the geometric distortion simultaneously for Bi-ISAR imaging based on parametric minimum entropy optimization. First, the signal mode for maneuvering target in the bistatic configuration is developed. Second, based on the developed signal model, we analyze the coupling relationship between the 2-D high-order spatial variant phase errors and the bistatic angle, and establish a parametric minimum entropy optimization model for high-order spatial variant phase errors compensation. Then, an efficient Broyden–Fletcher–Goldfarb–Shanno (BFGS) method is adopted to obtain the optimal solution of spatial variant coefficients. Finally, with the estimated optimal parameters, the integrated processing of 2-D spatial variant phase errors compensation and distortion correction can be realized. This method can simultaneously obtain well-focused and restored Bi-ISAR images of maneuvering targets without selecting prominent scatterers. Experiments based on scattering point simulation data and electromagnetic data verify the effectiveness of the proposed method.