Abstract
For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.
Highlights
With the advent of the era of Internet of everything, indoor and outdoor positioning information with high accuracy, high reliability, large capacity and low delay has became indispensable, which is vital for intelligent robots, unmanned driving and other unmanned platforms
A batch inverse covariance intersection (BICI) multi-sensor fusion positioning algorithm is proposed based on the batch form of ICI
Compared with sequential inverse covariance intersection (SICI), batch covariance intersection (BCI) and sequential covariance intersection (SCI) algorithm, the positioning accuracy of the BICI algorithm increases by 26.7%, at least in the simulation
Summary
With the advent of the era of Internet of everything, indoor and outdoor positioning information with high accuracy, high reliability, large capacity and low delay has became indispensable, which is vital for intelligent robots, unmanned driving and other unmanned platforms. Multi-sensor fusion methods are mainly divided into centralized and distributed methods, where the difference is whether the original measurements are capable of direct fusion The former can obtain a global optimal state estimation by expanded measurement equations and covariance matrix, but there are some drawbacks such as its computational complexity, fault tolerance and flexibility. The interacting multiple model (IMM) [24] has the same positioning accuracy and computational complexity as second-order and first-order pseudo Bayesian, respectively. It makes real-time high-precision reliable positioning feasible. The BICI-IMM multi-sensor positioning algorithm is presented for unknown covariance among local filters, uncertain time-varying noise variance and unknown motion models in challenging environments. The positioning accuracy of the BICI and BICI-IMM multi-sensor fusion positioning algorithms is demonstrated by simulations and experiments in open and semi-open areas
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