Main lobe interrupted sampling repeater jamming (ISRJ) is a typical form of coherent jamming that achieves both deception and suppression effects, significantly degrading the detection and tracking performance of radar systems. On the basis of the periodic sampling mechanism of the ISRJ scheme and the consistent frequencies of jamming slices with the same repeater order after dechirping, we construct a time-frequency (TF) plane to suppress the ISRJ signals in the radar echoes. First, the dechirped echo signals are segmented and reshaped on the basis of the jammer's sampling interval to form a signal matrix. The energies of jamming slices with the same repeater order in different sampling intervals are then accumulated by applying a discrete Fourier transform to generate a TF plane. This plane, characterized by high time and frequency resolution and high energy concentration, facilitates the separation of target echoes from jamming slices and an accurate estimation of jamming parameters. The theoretical resolutions are derived, and the distribution characteristics of the target echoes and jamming slices in the TF plane are analyzed. On the basis of these characteristics, jamming slices are distinguished from target echoes, and key parameters such as the slice location and width are estimated. By zeroing the elements containing jamming signals in the TF plane, the ISRJ signals are suppressed. The numerical simulation results verify the feasibility of the proposed method, which shows stable performance under different jamming-to-signal ratios, signal-to-noise ratios and jamming parameters.

The stripmap mode is a basic and important mode for spaceborne synthetic aperture radars (SARs). Estimating the time-varying area observed by spaceborne SARs operating in stripmap mode is a practical but challenging field of research. In this article, we propose a novel method that parameterizes the time-varying area observed by the spaceborne SAR operating in the boresight stripmap mode into a fixed antenna attitude. Based on the principle of minimizing the dihedral angle between the plane containing the ideal estimated scatterers and the plane containing the actual parabolic antenna edge of a spaceborne SAR, an objective function is established for estimating the area observed by a spaceborne SAR operating in the boresight stripmap mode. Then, simulation experiments are designed to validate the feasibility and the robustness of the proposed method. The experimental simulation results show that the proposed method is feasible, and even under low signal-to-noise ratio (SNR) conditions of 10 dB, the proposed method still has good robustness.

The problem of initial orbit determination (IOD) for Low Earth Orbit (LEO) objects using bistatic radar too-short arc (TSA) observations is addressed. For TSA observations, the traditional IOD methods suffer low accuracy. For LEO objects with stable attitude, the high order kinematic parameters can be obtained from the time derivatives of the radar echo phase. In this paper, an analytical IOD method is presented using bistatic radar TSA observations, which contain the position measurements (bistatic range, azimuth angle, and elevation angle) and the high order kinematic measurements (bistatic velocity, acceleration, and jerk). As the undetermined target state variables constitute a complex system of equations that can only be solved iteratively, an auxiliary coordinate system based on the bistatic geometry is defined to help reduce the equations to one unary quartic equation. Further, the closed-form expressions of the orbital state are derived. The performance of the proposed method is evaluated using linearization approximations. Numerical simulations are carried out for several typical LEO observation scenarios to demonstrate the performance of the proposed method.

Midcourse defense is essential for countering ballistic missile threats, and radar is a critical component of midcourse defense systems. However, the advanced maneuvering and deceptive electronic countermeasure (DECM) technologies employed during the midcourse flight of a ballistic missile significantly impair radar detection and tracking capabilities. In this work, a midcourse target maneuver detection and deception false target discrimination method is proposed that uses motion characteristics. By augmenting the state vector with deception and maneuvering parameters, motion and measurement models that are compatible with both false targets and maneuvering targets are developed. Based on these models, the deception parameters, maneuvering parameters, and target state are jointly estimated using radar measurements. The problem of target maneuver detection and false target discrimination is formulated as a target classification problem for which the generalized likelihood ratio test (GLRT) method is employed. The derived test statistics imply that different target classes can be distinguished on the basis of their unique feature spaces, which are specified by their projection matrices. A two-step GLRT scheme is then proposed for target classification, along with a strategy for controlling the false alarm probability. The numerical simulation results verify the effectiveness of the strategy and highlight the powerful detection and discrimination capabilities of the proposed scheme.

This article focuses on the midcourse track association scenario of ballistic target groups observed by ground-based pulse-Doppler radar. It proposes a ballistic target group track association neural network (BTGTANN) to perform track detection and association for individual targets within a ballistic target group. First, time-range profile (TRP) samples generated by performing pulse compression (PC) on raw echo signals are used to represent the spatial distribution of multiple targets over time. Second, a feature selection and aggregation (FSA) module and a context-aware enhancement (CAE) module are developed based on a convolutional neural network (CNN) architecture. These modules enhance the feature fusion and context awareness capabilities of the network. Finally, the target detection branch of the BTGTANN is used to detect multiple target tracks in TRP samples, yielding track detection boxes. An instance segmentation branch is then employed to accurately extract the contours of the tracks within the detection boxes, thereby determining the track positions at each pulse time. Unlike traditional methods, this approach formulates the multitarget track association problem as an object detection and instance segmentation task, providing an innovative solution within a deep learning framework. Experimental results on simulated datasets demonstrate that the detection probability (P_d), the false alarm probability (P_f), and the root-mean-square-error (RMSE) of BTGTANN reached 93.81%, 0.11%, and 8.43m, respectively. Relative to the baseline, P_d was increased by 5.70%, while P_f and RMSE were decreased by 0.06% and 3.97m, respectively. Moreover, the robustness of the BTGTANN is validated across different target scenarios, with the results indicating its substantial performance and generalizability undermultiple targets, low-signal-to-noise ratio (SNR) and low-signal-to-clutter ratio (SCR) environments.