Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

Abstract

According to the asynchronous transmission of data for the SINS/WSN integrated positioning system, this paper proposes a novel asynchronous data fusion method using Unscented Kalman Filter for SINS/WSN integrated positioning system based on indoor mobile target. The state equation of the integrated system is built with the motion characteristic of mobile target. The pseudo measurement equation is built based on the time sequence of SINS/WSN measured value through detecting the measurement of WSN in every fusion period. Considering that the improved state-space model, comprised of the state equation and pseudo measurement equation, is the nonlinear equations, the Unscented Kalman Filter is applied to estimate the state value of the statespace model. Hence the asynchronous data fusion method for SINS/WSN integrated positioning system can be achieved. Simulation results and experimental tests show that the positioning system with proposed asynchronous data fusion algorithm performs feasibility and stability under circumstances of the asynchronous time, and it is superior to the traditional asynchronous data fusion and synchronous data fusion methods.

UWB Indoor Localization Algorithm Using Firefly of Multistage Optimization on Particle Filter

“…With advances in sensor technologies [6] and wireless communications [7], various indoor localization methods and systems have been developed over the past years [8]. In indoor environments, positioning is mainly achieved through the use of radio and inertial navigation technologies [9]. Among them, ultra-wideband (UWB) is a new technology in the field of communication that uses pulses with a width of nanoseconds as wireless communication signals [10], which has the advantages of strong multipath resolutions, fast transmission rate, low power consumption, and high positioning accuracy [11].…”

An Optimal Enhanced Kalman Filter for a ZUPT-Aided Pedestrian Positioning Coupling Model

“…A gait detection mode [ 15 , 16 , 17 , 18 , 19 ] and adaptive filter [ 20 , 21 , 22 ] have been designed to study the regularity of pedestrian kinematics and walking gaits to offset positioning errors. Integrated positioning systems are introduced to offset errors [ 23 ], including IMU/UWB [ 24 ], IMU/WSN (Wireless Sensor Networks) [ 25 ], INS/WIFI [ 26 ], and INS/RFID [ 27 ]. When the pedestrian is in an unknown enclosed environment, some integrated positioning systems will be ineffective.…”