Video transmission, as one of the indispensable core services in the Internet of Vehicles, is confronted with numerous challenges such as the dynamics in the Internet of Vehicles environment, the limited resources, and the demand of vehicles for high-quality user Experience (Quality of Experience, QoE). In real-time video streaming scenarios, multicast optimization strategies, including convex optimization, game theory, stochastic optimization, etc., usually only group based on channel quality without considering task types and priorities, which may lead to the mixture of urgent tasks and ordinary tasks, affecting QoE. Therefore, we propose a task-aware three-stage collaborative optimization framework (TARA-IoV). The objective is to prioritize critical mission video services under limited bandwidth while maximizing the overall QoE. The framework first performs dynamic and adaptive vehicle grouping based on multi-dimensional features including task type, priority, geographical location, and channel state. Second, it conducts QoE-driven video quality layer selection leveraging Scalable Video Coding (SVC). Finally, it employs a deep reinforcement learning agent to dynamically allocate bandwidth with explicit task priority awareness under resource constraints. Evaluations on a real-world vehicle trajectory dataset demonstrate that TARA-IoV achieves improved QoE performance and more stable video delivery compared with existing schemes.

With the wide application of emerging technologies such as ultra-high definition video in Vehicular Edge Computing (VEC), the massive heterogeneous video data generated by vehicles have put forward higher requirements for real-time performance, energy efficiency and accuracy of processing. However, higher video analysis accuracy often leads to an increase in delay and energy consumption. How to balance the relationship between the three is an urgent problem to be solved. Meanwhile, the balanced or fixed bandwidth allocation mechanism adopted by most studies often ignores the characteristic differences of video tasks, resulting in inefficient resource allocation. At the same time, the security risks in the Internet of vehicles cannot be ignored. In order to deal with these challenges, this paper proposed a distributed task offloading framework combining Analytic Hierarchy Process (AHP) and Deep Deterministic Policy Gradient (DDPG). An adaptive bandwidth allocation mechanism based on the characteristics of video tasks is designed, and an improved blockchain consensus mechanism is introduced to ensure the optimal offloading decision in a trusted environment. Experimental results show that compared with the existing offloading schemes, the proposed algorithm reduces the task offloading delay by about 7.54%, reduces the energy consumption by about 6.37%, and improves the accuracy of video analysis by about 5.02% while ensuring security.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

This paper considers transmit beamforming and space-time adaptive processing (STAP) filter design in airborne radar for clutter suppression. We suppose that there are some strong clutters, which have a different distribution than the clutter in the cell under test (CUT) based on the database containing a geographical information system (GIS). Hence, we devise a novel algorithm for the joint design of transmit beamforming and STAP filter which aims to maximize the signal-to-clutter-plus-noise ratio (SCNR). To tackle the nonconvex problem, we convert the joint optimization problem into an equivalent beamforming optimization problem and find the optimal so-lution based on the minorization-maximization (MM) technique iteratively. Numerical simulations are provided to val-idate the proposed method and demonstrate its high performance.

This paper deals with the adaptive persymmetric detection problem in compound Gaussian sea clutter with inverse Gaussian texture. We derive a novel compound Gaussian detector based on the two-step generalized likelihood ratio test (GLRT). In detail, we assume the texture component and the clutter covariance matrix are known in the first step; Then we employ the maximum a posterior (MAP) and persymmetric property method to acquire the estimates of texture component and the clutter covariance matrix, respectively. The new detector is proved to be constant false alarm rate (CFAR) detector. The experiments that compare the proposed detector with the non-persymmetric detection are conducted by the simulated data and the measured data. The results reveal that the proposed detector outperforms the non-persymmetric detector in detection performance.

In battery/ultracapacitor (UC) hybrid energy storage systems (HESS), sizing and energy management strategies are crucial, which determine the system cost and performance. However, research on these two problems in a coupled manner for plug-in electric vehicles is still immature. This article aims at resolving this issue in the perspective of minimizing the average operating cost. Both manufacturing cost and system end-of-life timing are incorporated. A quantitative battery degradation model is employed to evaluate the battery dynamic capacity loss and cycle life. Dynamic programming algorithm is then deployed to achieve optimal power distribution between battery and UC. Furthermore, the power management and HESS optimal sizing strategies are unified into a single cost-minimization problem. Combining those efforts, the optimal size of the HESS with minimized average operating cost is solved by simulated annealing method. Optimization results illustrate that a minimum cost of 15.52 USD is achieved with 72 UC cells and 7100 battery cells. A large set of simulation data has proved the optimality of the optimization results. Compared with the battery-only solution, the proposed solution demonstrates 11.9% cost reduction and 21.7% battery cycle life extension under the Urban Dynamometer Driving Schedule. Moreover, the temperature rise of the battery is reduced by 31.1%. Finally, based on the optimal results, the energy management strategy is extended to fit real-time applications by utilizing Markov chain and stochastic dynamic programming.

Dwell scheduling is an important module of phased array radars. In order to implement effective dwell scheduling, a scheduling method based on new synthetic priority is proposed, in which the scheduling model is founded from the viewpoint of scheduling gain. The scheduling gain integrates the synthetic priority of the task and the validity of scheduling this task, where the synthetic priority integrates the importance, urgency of the task and threat level of the target. Simulation results demonstrate that compared with the conventional dwell scheduling method, the proposed method improves the scheduling performance of phased array radars.