Information geometry-based action decision-making for target tracking by fixed-wing unmanned aerial vehicle

Abstract

In this article, we study the ground moving target tracking problem for a fixed-wing unmanned aerial vehicle equipped with a radar. This problem is formulated in a partially observable Markov process framework, which contains the following two parts: in the first part, the unmanned aerial vehicle utilizes the measurements from its radar and employs a Kalman filter to estimate the target's real-time location; in the second part, the unmanned aerial vehicle optimizes its trajectory in a realtime manner so that the radar's measurements can include more useful information. To solve the trajectory optimization problem, we proposed an information geometry-based partially observable Markov decision process method. Specifically, the cumulative amount of information in the observation is represented by Fisher information of information geometry, and acts as the criterion of the partially observable Markov decision process problem. Furthermore, to guarantee the realtime performance, an important trade-off between the optimality and computation cost is made by an approximate receding horizon approach. Finally, simulation results corroborate the accuracy and time-efficiency of our proposed method and also show our advantage in computation time compared to existing methods.

Optimal control of nonlinear systems with unsymmetrical input constraints and its applications to the UAV circumnavigation problem

“…Through the discussions above, the optimal virtual input û * obviously satisfies the constraint (26). Substituting (34) into (27) results in…”

“…If the solution V * X of the HJB-equation (36) is found, the optimal virtual input û * for the system (20) can be obtained by (34). Furthermore, the optimal control input u * for the original system (12) can be acquired by (24).…”

“…In the previous part, it has been proved that the policy u * determined by ( 24) and ( 34) is optimal over all admissible controls. From the expression of û * in (34), the solution V * X of the HJB-equation (36) has to be solved in order to obtain û * . However, since (36) is usually highly nonlinear, it is quite difficult to obtain its analytical solution.…”

“…The observation model of the radar sensor is illustrated as follows: is the measurement noise, and Z(.) is the observation function, which is specifically defined as in [34]:…”

“…It is usually assumed that the measurement noise follows a zero-mean Gaussian distribution ( [34]), which is ζ(t)|s r ∼ N (η(s r ), C o (s r )), where ζ(t) is the obtained sensor measurement at time t. The measurement noise depends on the signal-to-noise ratio and the covariance matrix of the measurement is…”