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How Much Is 800 Meters
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By Zhihong Deng Zhihong Deng Scilit Preprints.org Google Scholar * , Yun Cao Yun Cao Scilit Preprints.org Google Scholar , Pengyu Wang Pengyu Wang Scilit Preprints.org Google Scholar and Bo Wang Bo Wang Scilit Preprints.org Google Scholar
Received: 2018 April 15 / Revised: 2018 June 1 / Accepted: 2018 June 3 / Published: 2018 June 7
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The internal position is currently an area of research. Pedestrian death counting (PDR) has been widely used indoors in recent years. However, positional error due to head drop accumulates with increasing pitch, so some method must be used to adjust the head angle. Heuristic drift elimination (HDE) is an efficient heading correction algorithm that uses the information of the building’s dominant directions to reduce the heading error, but does not affect the situation of the non-dominant direction. In this paper, we propose an HDE constraint head drop suppression method. First, the method creates membership functions to make judgments about pedestrian movement based on the results of the comprehensive assessment. The shape of the threshold then determines whether the pedestrian is walking on critical paths, and a head error measurement is provided for head correction. Finally, we verify through experiments whether the proposed method can correctly adjust the heading angles for different conditions, indicating its adaptability to the environment.
As technology advances, people’s lives grow, and the demand for location services increases. Therefore, the development of indoor positioning technology has attracted people’s attention. Goal tracking, quick recovery, marketing leads, and more are highly anticipated.
At present, indoor positioning mainly relies on WiFi, Bluetooth, UWB (Ultra Wide Band) and other outdoor wireless technologies including communications, machine vision, etc. You can achieve good results using the above placement techniques. For example, when we use UWB, the positioning accuracy reaches the centimeter level. However, due to the common and expensive design of reference lamps, demand is limited. Therefore, some researchers use inertial acceleration technology to determine the position of rooms. The data collected by MEMS (Micro-Electro-Mechanical Systems) integrated IMUs (Inertial Measurement Units) can be used to calculate the position of the pedestrian. This approach can prevent environmental disturbances and gain autonomy, but the additional errors introduced by MEMS devices cannot be ignored. Foxlin  first proposed an IEZ system, that is, Inertial Navigation System (INS), Extended Kalman Filter (EKF), and Zero Velocity Update (ZUPT) to estimate the motion path. Some researchers have done their work in this way [2, 3]. A frame can prevent horizontal position errors, but it cannot correct a header. Therefore, many domestic and foreign experts have conducted in-depth research. Yang Hong et al. [ 4 , 5 ] adopted a geomagnetic correction algorithm to correct the heading angle based on the heading difference calculated from the measurement and the measured position matrix. Afzal  determined the head error by recording the changes in the magnetic field measured by a magnetometer in the form of a pedestrian. Since the magnetometer has more problems in indoor environments, the accuracy of its calculated head angle cannot be guaranteed. Therefore, some believe that the magnetometer is not suitable for indoor use . To improve head count accuracy, Widyawan et al.  combined INS with visual image information, but light intensity affected this method. Also, some researchers have used the particle analysis method to estimate the angle of the head, previously obtaining the interior plan of the house , but it is usually difficult to do. Borenstein  2010 proposed a heuristic drift elimination (HDE) algorithm. The algorithm is based on the fact that most building walls and tunnels are composed of straight lines and right angles, and using only four to eight cardinal directions without any prior information about the environment can reduce the head drop.
However, as Jiménez  said in Ref., the HDE algorithm is good for pedestrians who walk along major roads, but sometimes not all building paths are along major roads. There are special structures such as circular arcs and the heading angle will be incorrectly corrected if the HDE algorithm is used in this case. Therefore, the algorithm needs to be improved in some ways.
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In this paper, we have carried out a research on a header repair method based on the IEZ system to solve the above problems. First, we describe the basic IEZ system for pedestrian movement information acquisition, propose an improved HDE (IHDE) method, and then verify the validity of the proposed method through experiments.
PDR is implemented based on the Kalman filter. The method is to obtain motion information by calculating inertial sensor data. When a pedestrian fall is detected, ZUPT updates the EKF measurements and applies the estimated errors to the motion information. The 15-dimensional EKF error vector is
. The vector includes position, velocity, and position errors, as well as assumptions about accelerometers and gyroscopes, each with its own 3-axis components. k represents the data sample time. Figure 1 shows the PDR installation process.
An accelerometer and a gyroscope are attached to the human body to collect data related to movement information. The Micro Inertial Navigation System (MINS) unit calculates the data. The Zero Velocity Detector (ZVD) block is used to detect zero-velocity moments to trigger the ZUPT and IHDE blocks, and these two blocks provide measurements to the EKF block for error detection.
Number & Operations
The MINS unit calculates the data collected by the IMU to obtain attitude, velocity and position information. The process is divided into 4 parts:
We divide the operation of the EKF into two cases: if the zero velocity term is not found, then the EKF is updated in time, which is the last analyzed error covariance matrix.
The detection accuracy of the nonlinear velocity moment is close to the statistical result. The movement of a pedestrian can be divided into two cases: station and movement, which are called
, each. Essentially, the foot swings forward during movement and sticks to the ground near the station box . The generalized ratio method (GLRT) determines whether the acceleration moment is zero :
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Earlier, but in practice this information is not available. So we specify the formula each time:
Is not equal to 0; compare, otherwise they are equal to positive values.
Is the sample noise. If we consider the zero mean of the white noise and the variances of the accelerometer and gyroscope noise.
Follows a Gaussian distribution. Thus, based on the above equations, we obtain the probability density function as follows:
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In principle, the GLRT corresponding to time n can be calculated by multiplying equation (18) by the appropriate choice
Represents the average value of the acceleration samples in the sliding window. According to the above equation, we can determine the movement of the pedestrian at time n. If
Although the use of the IEZ frame has a large effect on the horizontal position angle correction, it has little effect on the course correction. The reason, according to the analysis of the INS error equations, is the state of the error
Is invisible . Therefore, as the distance increases, the predicted path will deviate from the predicted one.
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In an indoor environment, the HDE algorithm can precisely adjust the heading angle based on the cardinal directions of the building. However, not all car routes in the local environment are located along main roads. If the HDE algorithm is still used in a situation where the direction is not dominant, as seen in Figure 3, the head angle correction will be too large, with a new head.
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