Application Research of an Array Distributed IMU Optimization Processing Method in Personal Positioning in Large Span Blind Environment

Abstract

Aiming at the long-term cumulative error inherent in pedestrian indoor inertial positioning filed, that error is mainly due to the low signal-to-noise ratio of the sensor output signal quality, the temperature drift of the gyro and the accuracy of error estimation. This paper proposes a new optimization method for array distributed MEMS-IMU: this method performs filtering and noise reduction optimization processing on inertial sensor data; The effect of temperature on the gyroscope is reduced by matrix-optimized layout, and distributed temperature compensation is performed for eight IMUs. We used MEMS-IMU worn on the foot finishing the data acquisition. Then improved a novel Pearson coefficient particle filtering method to finally complete the information fusion and positioning process in a blind environment (no beacon auxiliary information) high-precision personal large span (long time span, large distance span). The indoor positioning test results in the No. 6 Office Building of National Defense Science and Technology Park in Beijing Institute of Technology verify that the method has a horizontal error of only 6.23m (TTD ≈ 0.52%) during the horizontal span positioning which the total distance is about 1200m; In terms of vertical largespan positioning accuracy: the height error is only 4.56m (TTD ≈ 7.6%) during the positioning process of 68 minutes and 35 seconds (including intermediate stop). Compared with other multi-IMU personal positioning optimization methods, it has the advantages of high sensor data quality, small gyro temperature influence, good system error estimation accuracy and long-term long-distance positioning results. It provides a good and reliable theoretical reference for this field or extension applications. INDEX TERMS Array distribution, MEMS-IMU, filtering noise reduction, optimized layout, multi-channel temperature compensation, improved Pearson coefficient particle filter, large span blind environment.

A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

“…If all the measured values were filtered and updated uniformly, not only do they lose the data of the high sampling frequency but also the coupling problem occurs between different sensor data resulting in filtering divergence. The weakly-coupled hierarchical filtering method [22] can update the sensor data of different sampling frequencies, which solves the coupling problem between the sensor measured data and makes the filtering more flexible and safe.…”

“…Song Y [21] proposed a quaternion attitude Kalman filter algorithm and used the adaptive filter to modify the measurement noise covariance matrix. This solved the problem of large errors of MEMS IMU [22,23] and reduced the impact of gyroscope random errors in attitude estimation. Sabatelli S [24] proposed a quaternion double-level Kalman filter of the attitude estimation with a 9-axis MEMS IMU.…”

Redundant Configuration Method of MEMS Sensors for Bottom Hole Assembly Attitude Measurement

“…Nonetheless, in the intricate and challenging subterranean working environment, the reliability and measurement accuracy of individual MEMS sensors often fall short of operational requirements. Implementing a redundant system composed of multiple MEMS sensors effectively addresses these shortcomings by bolstering both reliability and measurement precision [ 3 , 4 , 5 , 6 , 7 , 8 ].…”

A tri-IMUs pedestrian positioning system based on single-lower-limb kinematic constraints

“…To further improve the estimated precision of a multi-sensor system, the layout of the sensors can be extended to the entire lower limb rather than two feet. For example, Liu placed four sensors layout on a printed circuit board (PCB), and designed a foot-mounted PINS with it [19]. This method is similar to the averaging principle.…”