With the rapidly increasing spectrum demand by multimedia applications, the limitation of the spectrum resource restricts the improvement of the performance for communication systems. The cognitive spectrum utilization scenario can solve this problem by sharing the licensed spectrum of primary users with secondary users under specific constrains. In this paper, we consider the uplink resource allocation problem in cognitive satellite network, where cognitive satellite users exploit the spectrum allocated to terrestrial networks as primary users. In order to control the interference to the primary users caused by cognitive users and achieve a fair allocation with considerable total capacity, we detailedly investigate the joint transmit power and bandwidth allocation problem with reasonable system model we proposed. We propose a combined resource management architecture to improve computational efficiency, and formulate the resource allocation problem as a cooperative bargaining game based on game theory. The near optimal joint resource allocation is derived on dual domain of original problem, and a cooperative resource allocating algorithm is proposed based on subgradient method. From simulation results, several important concluding remarks are obtained as follows: a) The proposed algorithm has a considerable convergence rate, and distributed computation can further improve computational efficiency; b) The multiuser and multi-beam diversity can improve total capacity, while interference constrains and the limitation of resource limits the performance boundary; c) Compared with existing methods, the proposed algorithm is Pareto optimal, which can achieve a better trade-off between fairness among users and total capacity of the whole network.

In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.

The penetration of unmanned aerial vehicles (UAVs) is an essential and important link in modern warfare. Enhancing UAV’s ability of autonomous penetration through machine learning has become a research hotspot. However, the current generation of autonomous penetration strategies for UAVs faces the problem of excessive sample demand. To reduce the sample demand, this paper proposes a combination policy learning (CPL) algorithm that combines distributed reinforcement learning and demonstrations. Innovatively, the action of the CPL algorithm is jointly determined by the initial policy obtained from demonstrations and the target policy in the asynchronous advantage actor-critic network, thus retaining the guiding role of demonstrations in the initial training. In a complex and unknown dynamic environment, 1000 training experiments and 500 test experiments were conducted for the CPL algorithm and related baseline algorithms. The results show that the CPL algorithm has the smallest sample demand, the highest convergence efficiency, and the highest success rate of penetration among all the algorithms, and has strong robustness in dynamic environments.

Manned/Unmanned Aerial Vehicle cooperative combat is an inevitable trend in the future, and the determination of its efficiency has always been the focus of the research. This paper analyzes the system composition of Manned/Unmanned Aerial Vehicle cooperative combat, then establish integrated index model of collaborative system. Based on this, this paper put forward the task-oriented cooperative combat effectiveness evaluation method, and carry out the simulation under the typical combat application for qualitative validation. The conclusion is the theoretical calculation results are consistent with cooperative combat efficiency simulation results, and the efficiency evaluation model can provide a reference for the selection of the optimal formation combination mode in actual combat.