Abstract
Pedestrian dead reckoning (PDR) can be used for continuous position estimation when satellite or other radio signals are not available, and the accuracy of the stride length measurement is important. Current stride length estimation algorithms, including linear and nonlinear models, consider a few variable factors, and some rely on high precision and high cost equipment. This paper puts forward a stride length estimation algorithm based on a back propagation artificial neural network (BP-ANN), using a consumer-grade inertial measurement unit (IMU); it then discusses various factors in the algorithm. The experimental results indicate that the error of the proposed algorithm in estimating the stride length is approximately 2%, which is smaller than that of the frequency and nonlinear models. Compared with the latter two models, the proposed algorithm does not need to determine individual parameters in advance if the trained neural net is effective. It can, thus, be concluded that this algorithm shows superior performance in estimating pedestrian stride length.
Highlights
Global navigation satellite systems (GNSS) play an important role in daily life; in some places satellite signals may be severely degraded or may not be received at all, leading to issues with continuous navigation [1]
The aim of this paper is to propose a universal model based on back propagation artificial neural network (BP-ANN) to estimate pedestrian stride length; this model does not need to predetermine pedestrian parameters each time, which is different from the frequency model in [9, 10] and the nonlinear model proposed in [14, 15]
The hardware utilized in the trials was a consumer-grade inertial measurement unit (IMU) consisting of MPU6050, and the dimensions of the printed circuit board (PCB) are 15.2 mm × 15.2 mm × 2 mm
Summary
Global navigation satellite systems (GNSS) play an important role in daily life; in some places satellite signals may be severely degraded or may not be received at all, leading to issues with continuous navigation [1]. Inertial navigation requires accurate initial alignment and heading information in real time, and owing to the drift of gyro, it must be combined with other information for positioning. This will increase the complexity of the use of information fusion algorithms and hardware, thereby raising the cost of pedestrian positioning. PDR can achieve continuous position estimation when satellite signals cannot be used. PDR has become an effective positioning technology, and acceleration signal statistical parameters can be used to estimate stride length. The stride length estimation algorithm is complex, because there may be a variety of motion patterns during walking or running.
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