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.

Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

This paper adopts the intelligent scheduling method to conduct an in-depth study and analysis on the optimization of financial asset liquidity management model, elaborates and analyzes the liquidity risk management theory of commercial banks, and reviews the progress of liquidity risk management research in domestic and foreign academia as the theoretical basis of this paper. After that, we analyze the liquidity risk management of Anhui Tianchang Rural Commercial Bank from both qualitative and quantitative levels and further review and analyze the problems and causes. Finally, the full research is summarized and reviewed, theoretical and practical insights are discussed and analyzed, and future liquidity risk management research priorities and directions are elaborated. Based on the analysis results, the problems of the bank in liquidity risk management are described one by one, and further deep-seated cause discovery is carried out to summarize the problems of liquidity risk management which exist in the bank’s operation process due to the lack of liquidity risk management, unbalanced asset, and liability allocation, as well as weak emergency management capability, insufficient day-to-day liquidity monitoring, and lack of professional talents. For the problems and causes of the study, effective suggestions on how to strengthen the bank’s liquidity risk management in multiple aspects are proposed. It is hoped that, by improving the bank’s liquidity risk management and reducing the chance of liquidity risk occurrence, the bank’s sustainable development can be enhanced, and it is also hoped that it can provide some reference for the liquidity risk management of similar rural small- and medium-sized financial institutions.

Energy Storage Economic Optimization Scheduling Method for Multi-Scene Demand of Peak and Frequency Modulation

energy storage (ES) only contributes to a single-scene (peak or frequency modulation (FM)) control of the power grid, resulting in low utilization rate and high economic cost. Herein, a coordinated control method of peak modulation and FM based on the state of ES under different time scales is proposed. Firstly, for monotone peak and FM control scenarios, the ES configuration and scheduling model is constructed with the goal of maximizing net profit. Secondly, to further improve the ES utilization rate and optimize the operating cost of ES, a cooperative control method of peak modulation and FM is proposed. This method can realize the switch between peak modulation and FM control of ES and improve the ES utilization rate and system economy. Finally, the simulation results show that, compared with that of mono-peak and single-FM control, the ES efficiency of the peak-FM multiscenario optimization scheduling method is improved by 16.25% and 37.29%, respectively. The annual net income is increased by €28,021.50, the investment recovery period is shortened by 0.27 years, and the ES configuration economy is effectively improved.