No abstract

No abstract

Purpose The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs. Design/methodology/approach In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model. Findings Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value. Originality/value This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

This paper uses Analytic Hierarchy Process(AHP) to analyze the probability of individual appearing acts of violence by the influence of different roles in the crowd of Mass Violence Event, obtaining the weight of influence of organization leaders, agitators, general participants and bystanders to individuals. This paper also introduces spatial heterogeneity to reflect different roles' influence decaying with distance. By means of experiment, we obtain the approximate probabilistic data of individuals appearing violence in different locations and personnel situations, depict the roughly curve of probability affected by crowd density in different personnel situations. Then we can determine the areas of high violence probability in certain environment, which provide data support for the police to dispose analogous events.

This paper introduces a new method for analyzing Mass Violence Events, and then we make use of Intelligent Artificial and Agent-Based System to deal with them. In the meanwhile, we describe and defy three kinds of agents (cops, activists and civilians), what comes next is that we apply these theories to model and simulate such events. The simulation results show that the behaviors of activists can easily affect whether civilians will join into Mass Violence Events, if they attend events, it will lead cops to face more difficulties when dealing with them. The new method can provide certain theoretical meanings for public security organs to address Mass Violence Events.