Pedestrian navigation device and method based on inertial sensor

Abstract

The invention discloses a pedestrian navigation device based on an inertial sensor. The pedestrian navigation device comprises a microprocessor, a three-axis acceleration sensor, a three-axis gyroscope, a three-axis magnetometer and a wireless communication module, wherein the three-axis acceleration sensor, the three-axis gyroscope, the three-axis magnetometer and the wireless communication module are respectively connected with the microprocessor; the microprocessor is used for receiving data collected by the three-axis acceleration sensor, the three-axis gyroscope and the three-axis magnetometer and uploading the data to a PC (Personal Computer) through the wireless communication module. The invention further discloses a pedestrian navigation method based on the pedestrian navigation device. The pedestrian navigation method comprises a step stage detection algorithm, a foot and body orientation estimation algorithm and an extended Kalman filter algorithm. The complexity of calculation is greatly reduced while the accuracy is guaranteed, and a condition that the requirement of timeliness can be met is guaranteed without consuming a large amount of hardware power consumption in a practical environment.

Description

Technical field [0001] The invention belongs to the technical field of navigation, and particularly relates to a pedestrian navigation device and method based on inertial sensors. Background technique [0002] With the rapid growth of data services and multimedia service data, the demand for high-precision pedestrian navigation systems is increasing. People with such navigation systems can easily enjoy many location-based services (LBS). However, due to the impact of the urban jungle environment, GPS signals will be blocked or reflected by skyscrapers to a large extent, resulting in the ultimate inability to directly locate and navigate through GPS. Therefore, it is based on a low-cost Micro Electro-Mechanic System (Micro Electro-Mechanic System). , MEMS) solutions gradually entered people's sight. The MEMS-based pedestrian inertial navigation system uses a set of inertial sensors contained within itself, such as acceleration sensors, gyroscope sensors, and magnetic sensors. It ...

AI Technical Summary

Problems solved by technology

However, the inertial navigation system itself has many shortcomings. The most serious error is that the error will accumulate over time. For example, when using a gyroscope to measure angular velocity, due to some characteristics of the gyroscope itself, there are phenomena such as zero point drift, so it is necessary to use various Algorithms to continuously correct and process to eliminate cumulative errors

And due to the limitation of accuracy, general commercial gyroscope sensors cannot measure a wide range of angular velocity values, and when pedestrians are walking or even going up and down stairs, the instantaneous angular velocity value will be much larger than its range

[0003] At present, some scholars have researched the MEMS-based inertial navigation system. In order to overcome the shortcomings of the system itself and improve the accuracy of the system, some researchers used a pedometer to count the number of steps, and then calculated the number of steps based on this statistics. The average step length can calculate and estimate the walking distance and trajectory, but the pedometer cannot distinguish different step states, such as running, stepping, etc. When the pedestrian walks fast, the method will miss the estimate. When the walking speed is slow, this method will overestimate, and the effect is not very ideal

Someone used a more complex method similar to the previous pedometer. They placed a two-axis acceleration sensor and a two-axis gyroscope on the pedestrian's feet, and used the neural network method to estimate the step length with the acceleration sensor, and Add improved Kalman filter technology to calculate the trajectory of pedestrians, but this method has high computational complexity and cannot perform real-time navigation

Some researchers use ultrasound and radio frequency to assist navigation, and because they require that the propagation path between the receiving end and the sending end cannot be blocked, it will be greatly restricted in the case of bad terrain

[0004] Therefore, in the current research results at home and abroad, the accuracy of most algorithms is still not very high, and some do not take into account the impact of computational complexity on real-time performance

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