Mobility estimation using an extended Kalman filter for unmanned ground vehicle networks

Abstract

Abstract-An ad hoc unmanned ground vehicle (UGV) network operates as an intermittently connected mobile delay tolerant network (DTN). In this paper, we develop a mobility estimation algorithm that can be coupled with a cooperative communication routing algorithm to provide a basis for real time path planning in UGV-DTNs. A Gauss-Markov state space model is used for the node dynamics. The nonlinear measurement signals are constant-power RSSI (Received Signal Strength Indicator) signals transmitted from fixed-position base stations. An extended Kalman filter (EKF) is derived for estimating the position, velocity and acceleration of a UGV node in a twodimensional spatial grid environment. We use Matlab to simulate a single mobile node traveling along a trajectory that includes abrupt maneuvers. Estimation performance is measured using zero-mean whiteness tests on the innovations sequences, root mean square error (RSME) of the state estimates, weighted sum squared residuals (WSSRs), and the posterior Cramer-Rao lower bound (PCRLB). Under these performance indices, we demonstrate that the mobility estimation algorithm performs effectively.

A Fuzzy Simulation Model for Military Vehicle Mobility Assessment

“…Mobility model for ground vehicles based on soil-moisture can be found in [13]. An extended Kalman filter based mobility estimation for unmanned ground vehicles is presented in [14]. The tire-soil interaction simulation based on absolute nodal coordinate formulation (ANCF) is developed by Recuero et al in [15].…”

Prediction of link residual lifetime using Kalman filter in vehicular ad hoc networks

“…In Gauss-Markov mobility model, a vehicle periodically updates its velocity based on the past speed which is determined by the memory level parameter or time correlation factor (α) [9]. Thus Gauss-Markov model is suitable for vehicular traffic in freeway highway scenario [10][11] [12][13] [14].…”