Clutter cancellation and long time integration are two vital steps for global navigation satellite system (GNSS)-based bistatic radar target detection. The former eliminates the influence of direct and multipath signals on the target detection performance, and the latter improves the radar detection range. In this paper, the extensive cancellation algorithm (ECA), which projects the surveillance channel signal in the subspace orthogonal to the clutter subspace, is first applied in GNSS-based bistatic radar. As a result, the clutter has been removed from the surveillance channel effectively. For long time integration, a modified version of the Fourier transform (FT), called long-time integration Fourier transform (LIFT), is proposed to obtain a high coherent processing gain. Relative acceleration (RA) is defined to describe the Doppler variation results from the motion of the target and long integration time. With the estimated RA, the Doppler frequency shift compensation is carried out in the LIFT. This method achieves a better and robust detection performance when comparing with the traditional coherent integration method. The simulation results demonstrate the effectiveness and advantages of the proposed processing method.

Focusing on the inverse synthetic aperture radar (ISAR) imaging of maneuvering targets, this paper presents a new imaging method which works well when the target’s maneuvering is not too severe. After translational motion compensation, we describe the equivalent rotation of maneuvering targets by two variables—the relative chirp rate of the linear frequency modulated (LFM) signal and the Doppler focus shift. The first variable indicates the target’s motion status, and the second one represents the possible residual error of the translational motion compensation. With them, a modified Fourier transform matrix is constructed and then used for cross-range compression. Consequently, the imaging of maneuvering is converted into a two-dimensional parameter optimization problem in which a stable and clear ISAR image is guaranteed. A gradient descent optimization scheme is employed to obtain the accurate relative chirp rate and Doppler focus shift. Moreover, we designed an efficient and robust initialization process for the gradient descent method, thus, the well-focused ISAR images of maneuvering targets can be achieved adaptively. Human intervention is not needed, and it is quite convenient for practical ISAR imaging systems. Compared to precedent imaging methods, the new method achieves better imaging quality under reasonable computational cost. Simulation results are provided to validate the effectiveness and advantages of the proposed method.

Uniformly accelerated rotation of maneuvering targets can lead to quadratic phase terms, after translational motion compensation, which causes the cross-range signal in a certain range cell to be multicomponent linear frequency modulated (LFM) signals. Due to the time-varying Doppler frequencies of scatterers, ISAR image obtained by the conventional range-Doppler (RD) algorithm is blurred severely. To solve this problem, a new imaging method based on optimized matching Fourier transform (MFT) is proposed in this paper. In the new method, the equivalent rotation of targets is described by the rotation ratio (RR) which is the same for all scatterers on the target. The quadratic phase components caused by target's angular acceleration can be compensated by MFT, as soon as the RR is obtained. The estimation of RR is achieved according to the optimal imaging result with the image entropy as the cost function. As a result, the blurring of the ISAR image is eliminated effectively and a well-focused image is got. From the experimental results using simulated data, it can be concluded that the proposed method based on optimized MFT is effective in ISAR imaging of non-uniform rotating targets in terms of image quality and computational efficiency.

Translational motion compensation and azimuth compression are two essential processes in inverse synthetic aperture radar (ISAR) imaging. The anterior process recovers coherence between pulses, during which the phase autofocus algorithm is usually used. For ISAR imaging of maneuvering targets, conventional phase autofocus methods cannot effectively eliminate the phase error due to the adverse influence of the quadratic phase terms caused by the target’s maneuvering motion, which leads to the blurring of ISAR images. To address this problem, an iterative phase autofocus approach for ISAR imaging of maneuvering targets is proposed in this paper. Considering the coupling between translational phase errors and quadratic phase terms, minimum entropy-based autofocus (MEA) method and adaptive modified Fourier transform (MFT) are performed iteratively to realize better imaging results. In this way, both the translational phase error and quadratic phase terms induced by target’s maneuvering motion can be compensated effectively, and the globally optimal ISAR image is obtained. Comparison ISAR imaging results indicates that the new approach achieves stable and better ISAR image under a simple procedure. Experimental results show that the image entropy of the proposed approach is 0.2 smaller than the MEA method, which validates the effectiveness of the new approach.

A global navigation satellite system (GNSS)-based multistatic radar is explored for target localization and kinematic state estimation. Since any point on the earth can be illuminated by a minimum of four satellites of each GNSS constellation at any time, GNSS-based passive radars can be inherently considered multistatic radars. In this paper, a method for jointly estimating the target position and velocity by utilizing both the time delays and Doppler shifts has been proposed, and an analytical accuracy analysis is also provided. In the new method, the bistatic range and Doppler for each path are firstly estimated by the range-Doppler (RD) method, and then by using the bistatic ranges and Doppler shifts. The least squares method is applied to estimate the target position and velocity simultaneously. Compared with the precedent target localization and velocity estimation method, the proposed method achieves a better estimation result with simple procedures. Simulation results are provided to validate the effectiveness of the proposed method.

This paper presents a passive bistatic radar (PBR) configuration using a global navigation satellite system as an illuminator of opportunity for the rotor blade feature extraction of a helicopter. Aiming at the strong fixed clutter in the surveillance channel of the PBR, a novel iteration clutter elimination method-based singular-value decomposition approach is proposed. Instead of the range elimination method used in the classic extended cancellation algorithm, the proposed clutter elimination method distinguishes the clutter using the largest singular value and by remove this value. At the same time, the fuselage echo of the hovering helicopter can also be suppressed along with the ground clutter, then the rotor echo of this can be obtained. In the micro-motion feature extraction, the mathematic principle of the flash generation process in the time–frequency distribution (TFD) is derived first. Next, the phase compensation method is applied to achieve the time–frequency flash shift in the TFD. After this, the center frequencies of the standard flashes in the TFD are compared with the standard frequency dictionary. The mean l1 norm is utilized to estimate the feature parameters of the helicopter rotor. In the experiments, the scattering point model and the physical optics facet model demonstrate that the proposed method can obtain more accurate parameter estimation results than some classic algorithms.