scholarly journals Dynamic Adaptive Low Power Adjustment Scheme for Single-Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation in the Complex Urban Environment

2021 ◽  
Vol 13 (16) ◽  
pp. 3236
Author(s):  
Peihui Yan ◽  
Jinguang Jiang ◽  
Yanan Tang ◽  
Fangning Zhang ◽  
Dongpeng Xie ◽  
...  
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
High Precision ◽  
Single Frequency ◽  
Low Power Consumption ◽  
Integrated Navigation ◽  
Precision Positioning ◽  
Average Power Consumption ◽  
Mems Imu

Positioning accuracy and power consumption are essential performance indicators of integrated navigation and positioning chips. This paper proposes a single-frequency GNSS/MEMS-IMU/odometer real-time high-precision integrated navigation algorithm with dynamic power adaptive adjustment capability in complex environments. It is implemented in a multi-sensor fusion navigation SiP (system in package) chip. The simplified INS algorithm and the simplified Kalman filter algorithm are adopted to reduce the computation load, and the strategy of adaptively adjusting the data rate and selecting the observation information for measurement update in different scenes and motion modes is combined to realize high-precision positioning and low power consumption in complex scenes. The performance of the algorithm is verified by real-time vehicle experiments in a variety of complex urban environments. The results show that the RMS statistical value of the overall positioning error in the entire road section is 0.312 m, and the overall average power consumption is 141 mW, which meets the requirements of real-time integrated navigation for high-precision positioning and low power consumption. It supports single-frequency GNSS/MEMS-IMU/odometer integrated navigation SiP chip in real-time, high-precision, low-power, and small-volume applications.

scholarly journals An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module

2021 ◽  
Vol 13 (21) ◽  
pp. 4317
Author(s):  
Peihui Yan ◽  
Jinguang Jiang ◽  
Fangning Zhang ◽  
Dongpeng Xie ◽  
Jiaji Wu ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Covariance Matrix ◽  
Urban Environments ◽  
Single Frequency ◽  
Low Power Consumption ◽  
Integrated Navigation ◽  
Adaptive Kalman Filter ◽  
Mems Imu ◽  
Observation Vector

Aiming at the GNSS receiver vulnerability in challenging urban environments and low power consumption of integrated navigation systems, an improved robust adaptive Kalman filter (IRAKF) algorithm with real-time performance and low computation complexity for single-frequency GNSS/MEMS-IMU/odometer integrated navigation module is proposed. The algorithm obtains the scale factor by the prediction residual, and uses it to adjust the artificially set covariance matrix of the observation vector under different GNSS solution states, so that the covariance matrix of the observation vector changes continuously with the complex scene. Then, the adaptive factor is calculated by the Mahalanobis distance to inflate the state prediction covariance matrix. In addition, the one-step prediction Kalman filter is introduced to reduce the computational complexity of the algorithm. The performance of the algorithm is verified by vehicle experiments in the challenging urban environments. Experiments show that the algorithm can effectively weaken the effects of abnormal model deviations and outliers in the measurements and improve the positioning accuracy of real-time integrated navigation. It can meet the requirements of low power consumption real-time vehicle navigation applications in the complex urban environment.

Radar Based Real-Time Fall Detection System with Low Power Consumption

2021 ◽  
Author(s):  
Jincheng Lu ◽  
Zixuan Ou ◽  
Ziyu Liu ◽  
Cheng Han ◽  
Wenbin Ye
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Detection System ◽  
Fall Detection ◽  
Low Power Consumption ◽  
Time Fall

Design and Implementation of High-Precision Mine-Used Water-Pressure Sensor

2014 ◽  
Vol 513-517 ◽  
pp. 3513-3517
Author(s):  
Rui Xue Wang ◽  
Na Zhang ◽  
Le Nian Xu
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
High Precision ◽  
Pressure Sensor ◽  
Water Pressure ◽  
Low Power Consumption ◽  
Stability Measurement ◽  
Working Principle ◽  
The Stability ◽  
Upper Computer

A novel mine-used water-pressure sensor is presented in this paper, the configuration, working principle and design of main circuits are introduced in detail. Using pressure sensor, high precision 24 bits A/D converter AD7714 and low power consumption MCU P89LPC932 to complete the water-pressure measurement, and transferred the measuring results to upper computer through M-BUS, realized the stability measurement of high precision and low power consumption.

An implement method for real-time infrared imaging system with low power consumption based on DSP+CPLD

2012 ◽  
Vol 38 (1) ◽  
pp. 14-18
Author(s):  
黄战华 HUANG Zhanhua ◽  
杨鹤猛 YANG Hemeng ◽  
孙立彬 SUN Libin ◽  
蔡怀宇 CAI Huaiyu
Keyword(s):  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Infrared Imaging ◽  
Imaging System ◽  
Low Power Consumption ◽  
Infrared Imaging System

scholarly journals Efficiency Improvement of Kalman Filter for GNSS/INS through One-Step Prediction ofPMatrix

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Qingli Li ◽  
Yalong Ban ◽  
Xiaoji Niu ◽  
Quan Zhang ◽  
Linlin Gong ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Low Power ◽  
Real Time ◽  
Low Power Consumption ◽  
One Step ◽  
Kalman Filter Algorithm ◽  
Simplified Algorithm ◽  
Navigation Accuracy ◽  
Computation Load

To meet the real-time and low power consumption demands in MEMS navigation and guidance field, an improved Kalman filter algorithm for GNSS/INS was proposed in this paper named as one-step prediction ofPmatrix. Quantitative analysis of field test datasets was made to compare the navigation accuracy with the standard algorithm, which indicated that the degradation caused by the simplified algorithm is small enough compared to the navigation errors of the GNSS/INS system itself. Meanwhile, the computation load and time consumption of the algorithm decreased over 50% by the improved algorithm. The work has special significance for navigation applications that request low power consumption and strict real-time response, such as cellphone, wearable devices, and deeply coupled GNSS/INS systems.

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