Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

Purpose Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system. Design/methodology/approach First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly. Findings Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance. Originality/value In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory

In order to realize a saturation attack of multiple unmanned aerial vehicles (UAVs) on the same target, the problem is transformed into one of multiple UAVs hitting the same target simultaneously, and a terminal distributed cooperative guidance law for multiple UAVs based on consistency theory is proposed. First, a new time-to-go estimation method is proposed, which is more accurate than the existing methods when the leading angle is large. Second, a non-singular sliding mode guidance law (NSMG) of impact time control with equivalent control term and switching control term is designed, which still appears to have excellent performance even if the initial leading angle is zero. Then, based on the predicted crack point strategy, the NSMG law is extended to attack maneuvering targets. Finally, adopting hierarchical cooperative guidance architecture, a terminal distributed cooperative guidance law based on consistency theory is designed. Numerical simulation results verify that the terminal distributed cooperative guidance law is not only applicable to different forms of communication topology, but also effective in the case of communication topology switching.

An Iterative Phase Autofocus Approach for ISAR Imaging of Maneuvering Targets

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.