Abstract
In this paper, an angle-of-arrival (AOA)-based algorithm is proposed for tracking the position of an anonymous target in three-dimensional (3D) space. Distributed sensors are deployed, which can measure both the azimuth and elevation angles of the AOAs. Assuming the target movement is non-linear, the extended Kalman filter (EKF) is applied, where the observation process is realized by a practical AOA-based position detector, to form a unified factor graph (FG) framework. Moreover, the variance of observation errors, which is needed by EKF, is estimated in real time by using both the AOA measurements and the predicted target state. Such a dynamic estimating approach exhibits higher performance robustness compared to the conventional method, especially when the sensing environment is unstable. Additionally, the predicted target state is also used as the a priori information of the system, in order to reduce the impacts of burst sensing errors. According to the simulations, the proposed system is shown to achieve less root mean squared errors (RMSE) in different evaluation scenarios, with fast convergence behavior.
Highlights
Future wireless networks should realize communication, and support new services, such as connected transportation systems, smart logistics, unmanned factories, etc. [1,2,3,4]
Values of the observer and the extended Kalman filter (EKF) are 6.15 and 4.6 in Figure 4b, which are higher than Figure 4a, because of a larger σφ,θ = 20◦ is assumed in the simulation
This paper provided an AOA-based tracking technique in 3D with an factor graph (FG) algorithm
Summary
Future wireless networks should realize communication, and support new services, such as connected transportation systems, smart logistics, unmanned factories, etc. [1,2,3,4]. For location detectors that work in a relatively stable condition, the variance of detection errors will be stable In this case, a fixed variance value can be fed to EKF for estimating the performance of the observer. For the AOA-based location detection problem, the CRB can be calculated by using the variances of AOA measurements and the real position of the target The latter parameter is unavailable in practice. It is possible to get a predicted target position with EKF, with which the ACRB can be calculated as the estimation of the observation variance Such a process can work in dynamic environments and has been shown to achieve a robust tracking performance in this paper.
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