Task allocation of multiple unmanned aerial vehicles (multi-UAVs) is a typical NP-hard problem. In this paper, according to practical battlefield needs, mathematical model is constructed based on complex constrains of task allocation, and objective function is constructed based on multi-UAVs’ global voyage and task time. An improved strategy of particle position based on basic Particle Swarm Optimization (PSO) algorithm is applied to the problem, and reasonable allocation schemes are obtained. The allocation schemes meet the complex constrains including task sequence, time window, UAVs’ capacities and flight path, and can be chosen and adjusted flexibly by the decision maker according to the practical battlefield needs. A large number of simulation experiments show that improved PSO algorithm is effective and provides a reference for multi-UAVs’ task allocation problem with complex constrains and multi-objectives.
Aiming at the inaccuracy of Non-Local Means (NLM) algorithm for measuring the similarity of neighborhood blocks, an improved Non-Local Means denoising algorithm based on Difference Hash (dHash) algorithm and Hamming distance is proposed. The traditional algorithm measures the similarity between neighborhood blocks by Euclidean distance, so the ability to preserve edges and details is weak, which leads to the blurred and distorted images after filtering. To this end, the Difference Hash algorithm containing the gradient information is introduced, the difference hash images are generated from neighborhood blocks, and the Hamming distance of the difference hash images is calculated to measure the similarity of the neighborhood blocks. Finally, the Euclidean distance is improved. Experiment results show that the proposed method can preserve edges and details while denoising the low-noise images. Compared with other improved algorithms, the running speed of the proposed algorithm is also greatly improved, which has a certain application value.
In the context of the strategic goal of building energy Internet, State Grid Corporation of China has established a crossdisciplinary joint construction and common Internet of Things management platform, which carries out standardized access and unified online control for equipment in transmission, power transformation, distribution and other fields. In order to improve the access debugging efficiency of the IoT management platform accessed by terminal devices, this paper designed a full-scene simulation detection system for the intelligent IoT system oriented to the energy Internet. By using the existing construction ideas of intelligent IoT system and following the complete architecture of "cloud, tube, edge, terminal ", the construction method of the simulation and coordination platform of intelligent IoT system of "information-physical coupling" is realized. Advanced technologies such as micro-service and distribution are adopted to build a lightweight simulation test system. A small amount of server resources can be used to simulate and test whether the communication protocol of the side device supports the communication protocol requirements of the intelligent IoT management platform. The functions of reporting equipment data collection and issuing control commands are tested, so as to complete the simulation, verification and testing functions of the whole electric power industry chain intelligent IoT system covering both internal and external businesses, and provide technical support for the construction and promotion of the IoT management platform of State Grid Corporation of China. Combined with the whole scene simulation and detection system of the intelligent Internet of Things system, the operating state sensing monitoring scene of the power distribution area is built to verify the functions of data reporting of the fusion terminal and command issuing of the simulation and detection system.
This paper proposes a multi-beamspace division multiple access (MB-SDMA) by utilizing a directional radiation communication beam of the hemispherical LED array, which can solve the problem of network forming for underwater nodes. Meanwhile, a multiple-input multiple-output non-orthogonal multiple access(MIMO-NOMA) technologies for MB-SDMA is constructed. In this system, a multiple beam selection strategy aiming at the maximum receiving beam energy for the different spatial position is proposed. Then, an optimization model is established to make the user nodes satisfy the fairness criterion of maximizing the minimum rate under the constraint of total transmit power, and the non-convex model is solved by using the bisection method and the KKT condition. Finally, the power allocation algorithm to ensure user fairness is proposed in the multi-beam MIMO-NOMA system.The simulation results show that the system reduces inter-beam interference by reducing the number of radio frequency(RF) links, and it can be seen that the throughput of the multi-beam MIMO-NOMA system is higher than that of the traditional OMA system.
The kinematics of maneuvering multiple targets are generally unknown and time-varying. Using only a single frame of data, the conventional δ-Generalized Labeled Multi-Bernoulli (GLMB) filter has large tracking errors. Although the Multiple Hypothesis Tracking (MHT) algorithm utilizes multi-frame information, the amount of calculation will increase sharply with the number of targets and the complexity of environment. To address this issue, this paper incorporates the Multiple Model (MM) and MHT algorithm into δ-GLMB filter. MM is used to generate multiple hypothesis of δ-GLMB target components, and these hypotheses are enriched by association with measurements data on the level of multiple target component. In this way, multi- hypothesis information of each component is transmitted among successive multiple frames. Due to the use of multi-frame information, the performance of δ-GLMB filter for maneuvering targets in complex scenarios is effectively improved. At the same time, the computational complexity can be reduced by forming multiple hypothesis on the level of target components. Simulation results show that the proposed MM-MHT -δ-GLMB algorithm can effectively track targets in multi-target complex motion scenario and has better multi-target tracking accuracy than the conventional single model δ-GLMB filter.
