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.