阅读说明：本技术 一种基于惯性数据时频域特征提取的行人步长建模方法 (Pedestrian step size modeling method based on inertial data time-frequency domain feature extraction ) 是由 王鹏宇 孙伟 李海军 蒋荣 裴玉锋 徐西京 苗宏胜 徐兴华 晏升辉 刘冲 于 2021-08-20 设计创作，主要内容包括：本发明提供了一种基于惯性数据时频域特征提取的行人步长建模方法,首先采集非常规步态下的惯性数据,对不同步态的惯性数据进行分段；然后计算单步周期内的步频、加速度方差,构建时域线性步长模型；随后将单步周期内的三轴加速度矢量和信号进行分数阶傅里叶变换,计算变换后的加速度信号的标准差因子和四分位差因子,构建频域线性步长模型；最后利用加权方法融合时域线性步长模型和频域线性步长模型,得到融合步长模型。该方法通过对惯性数据时频域特征的提取、融合,提高多运动状态下基于惯性传感器的行人航位推算精度,解决现有步长建模方法无法直接应用于跑步、侧走、倒走等非常规步态的技术问题。(The invention provides a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction, which comprises the steps of firstly, acquiring inertial data under an unconventional gait, and segmenting the asynchronous inertial data; then calculating step frequency and acceleration variance in the single step period, and constructing a time domain linear step model; then, fractional Fourier transform is carried out on the triaxial acceleration vector sum signal in the single-step period, a standard deviation factor and a quartile difference factor of the transformed acceleration signal are calculated, and a frequency domain linear step model is constructed; and finally, fusing the time domain linear step size model and the frequency domain linear step size model by using a weighting method to obtain a fused step size model. The method improves the accuracy of pedestrian dead reckoning based on the inertial sensor in a multi-motion state by extracting and fusing the time-frequency domain characteristics of the inertial data, and solves the technical problem that the existing step modeling method cannot be directly applied to unconventional gaits such as running, side walking and back walking.)

1. A pedestrian step size modeling method based on inertial data time-frequency domain feature extraction is characterized by comprising the following steps

Acquiring inertia data under walking and unconventional gait, and segmenting the inertia data in an asynchronous state;

calculating step frequency and acceleration variance in a single step period, and constructing a time domain linear step model;

carrying out fractional Fourier transform on the triaxial acceleration vector sum signal in the single-step period, calculating a standard deviation factor and a quartile difference factor of the transformed acceleration signal, and constructing a frequency domain linear step model;

and fusing the time domain linear step size model and the frequency domain linear step size model by using a weighting method to obtain a fused step size model.

2. The pedestrian step modeling method based on inertial data time-frequency domain feature extraction as claimed in claim 1, wherein the irregular gait includes running, side walking, and back walking.

3. The pedestrian step modeling method based on inertial data time-frequency domain feature extraction according to claim 1, wherein the step frequency f_stepAnd the acceleration variance v is calculated as follows

f_step＝1/(t_i-t_i-1)

Wherein, t_i-1And t_iRespectively the start and end times of the ith step, a_tThe vertical acceleration output for time t is obtained,is the average value of the vertical acceleration in the process of the ith step, and N is the acceleration sampling number in the ith step.

4. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction as claimed in claim 3, wherein the time domain linear step size model is

Wherein the content of the first and second substances,respectively represents time domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated.

5. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction as claimed in claim 4, characterized in that the calculation method of p-order Fourier transform is as follows

Wherein x (t) is an acceleration vector sum signal in a single step period, F^pDefined as a fractional Fourier transform operator, α ═ p π/2, K_p(u, t) is an integral kernel function,n is an integer.

6. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction according to claim 5, characterized in that the Fourier transform order p is in a range of 0.2-0.5.

7. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction as claimed in claim 5, wherein the standard deviation factor calculation method is as follows

Wherein N is the number of acceleration samples added in the step i, MoX_p(. a) a process of taking a modulus value for the acceleration signal after the p-order Fourier transform, M^FIs the average of the amplitude of the acceleration signal,

ordering the acceleration signals after the p-order Fourier transform into q from small to large_i1,2,3, k, the four-quadrant difference factor is calculated as follows

Wherein INT (-) is a rounding operation.

8. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction as claimed in claim 7, wherein the frequency domain linear step size model is obtained by a linear combination method, specifically

Wherein the content of the first and second substances,respectively representing frequency domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated.

9. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction according to claim 8, characterized in that the fusion step size model is

Wherein the content of the first and second substances,c belongs to { walk, run, side, back } and respectively represents the weight of the time domain linear step size model and the frequency domain linear step size model in the asynchronous state.

10. The pedestrian step size modeling method based on inertial data time-frequency domain feature extraction according to claim 9, characterized in that the time domain linear step size model weight selection method in the asynchronous state is that, when walking backwards and walking sideways, the time domain linear step size model weight range is 0.4-0.6, when walking, the time domain linear step size model weight range is 0.6-0.8, and when running, the time domain linear step size model weight range is 0.6-0.7.

Technical Field

The invention belongs to the technical field of pedestrian navigation, and particularly relates to a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction.

Background

The waist-bound pedestrian navigation system fixedly connects the micro-inertial sensor to the waist of a human body and realizes position updating by using a dead reckoning method. When the traditional pedestrian navigation method based on the inertial sensor is used for dead reckoning, the step length is obtained by utilizing an acceleration signal in a modeling mode, and the conventional modeling method mainly considers normal walking gait and cannot be directly applied to unconventional gaits such as running, side walking, back walking and the like, so that the pedestrian step length modeling method suitable for the walking and the unconventional gaits is needed.

Disclosure of Invention

The invention aims to provide a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction, which obtains a pedestrian step size model in an asynchronous state through extraction and fusion of the inertial data time-frequency domain features, improves the pedestrian dead reckoning precision based on an inertial sensor in a multi-motion state, and solves the technical problem that the existing step size modeling method cannot be directly applied to unconventional gaits such as running, side walking and back walking.

In order to achieve the purpose, the technical scheme adopted by the invention is as follows:

the invention provides a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction, which comprises the following steps

Acquiring inertia data under walking and unconventional gait, and segmenting the inertia data in an asynchronous state;

calculating step frequency and acceleration variance in a single step period, and constructing a time domain linear step model;

carrying out fractional Fourier transform on the triaxial acceleration vector sum signal in the single-step period, calculating a standard deviation factor and a quartile difference factor of the transformed acceleration signal, and constructing a frequency domain linear step model;

and fusing the time domain linear step size model and the frequency domain linear step size model by using a weighting method to obtain a fused step size model.

Further, the unconventional gait includes running, sideways walking, and back walking.

Further, the step frequency f_stepAnd the acceleration variance v is calculated as follows

f_step＝1/(t_i-t_i-1)

Wherein, t_i-1And t_iRespectively the start and end times of the ith step, a_tThe vertical acceleration output for time t is obtained,is the average value of the vertical acceleration in the process of the ith step, and N is the acceleration sampling number in the ith step.

Further, the time domain linear step size model is

Wherein the content of the first and second substances,respectively represents time domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated.

Further, the calculation method of the p-order Fourier transform is as follows

Wherein x (t) is an acceleration vector sum signal in a single step period, F^pDefined as a fractional Fourier transform operator, α ═ p π/2, K_p(u, t) is an integral kernel function,n is an integer.

Further, the Fourier transform order p is within the range of 0.2-0.5.

Further, the standard deviation factor calculation method is as follows

Wherein N is the number of acceleration samples added in the step i, MoX_p(. a) a process of taking a modulus value for the acceleration signal after the p-order Fourier transform, M^FIs the average of the amplitude of the acceleration signal,

ordering the acceleration signals after the p-order Fourier transform into q from small to large_i1,2,3, k, andthe method for calculating the four-point difference factor comprises the following steps

Wherein INT (-) is a rounding operation.

Further, the frequency domain linear step size model is obtained by utilizing a linear combination mode, specifically, the frequency domain linear step size model is obtained by utilizing a linear combination mode

Wherein the content of the first and second substances,respectively representing frequency domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated.

Further, the fusion step size model is

Wherein the content of the first and second substances,c belongs to { walk, run, side, back } and respectively represents the weight of the time domain linear step size model and the frequency domain linear step size model in the asynchronous state.

Further, the time domain linear step size model weight selection method in the asynchronous state is that the time domain linear step size model weight range is 0.4-0.6 when the user walks backwards and sideways, the time domain linear step size model weight range is 0.6-0.8 when the user walks, and the time domain linear step size model weight range is 0.6-0.7 when the user runs.

Compared with the prior art, the invention has the beneficial effects that:

the invention provides a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction. The method can greatly improve the step length estimation precision under the complex gait under the condition of effectively fusing time domain and frequency domain step length models, realize the high-precision positioning and navigation of the pedestrian under the multi-motion state, and greatly improve the dead reckoning precision of the pedestrian under the complex gait.

Drawings

The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.

Fig. 1 is a schematic block diagram of a pedestrian step modeling method based on inertial data time-frequency domain feature extraction according to a specific embodiment of the present invention.

Detailed Description

The following provides a detailed description of specific embodiments of the present invention. In the following description, for purposes of explanation and not limitation, specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details.

It should be noted that, in order to avoid obscuring the present invention with unnecessary details, only the device structures and/or processing steps closely related to the scheme of the present invention are shown in the drawings, and other details not so related to the present invention are omitted.

The waist-bound pedestrian navigation system fixedly connects the micro-inertial sensor to the waist of a human body, and realizes position updating by using a dead reckoning method, wherein the single step length can be obtained through a linear step length model based on time domain characteristics. In order to excavate step length characteristics of unconventional gaits such as running, side walking and backward walking, the invention extracts step length related factors of acceleration signals in a single-step period by utilizing fractional Fourier transform, combines the step length related factors with time domain characteristics to obtain a fusion step length model, and can improve the step length estimation precision under complex gaits. The invention is particularly suitable for solving the application requirement of high-precision positioning and navigation of people in a multi-motion state.

The basic principle of the invention is as follows: fixedly connecting a micro inertial sensor to the waist of a pedestrian, collecting original inertial data under walking, running, side walking, backward walking and other gaits, and constructing a time domain linear step size model by using time domain motion characteristic parameters such as step frequency, acceleration variance and the like; fractional Fourier transform is carried out on the triaxial acceleration vector and the signal in the single-step period, step-length related factors such as a standard deviation factor and a quartile difference factor are extracted from the transformed signal, and a frequency domain linear step-length model is constructed; and comprehensively considering the time domain and frequency domain characteristics, and fusing the time domain linear step size model and the frequency domain linear step size model by using a weighting method to obtain a fused step size model.

The invention provides a pedestrian step size modeling method based on inertial data time-frequency domain feature extraction, which specifically comprises the following steps:

acquiring original inertia data under unconventional gaits such as walking, running, side walking, back walking and the like, and segmenting the asynchronous inertia data;

calculating step frequency and acceleration variance in a single step period, and constructing a time domain linear step model;

carrying out fractional Fourier transform on the triaxial acceleration vector sum signal in the single-step period, calculating a standard deviation factor and a quartile difference factor of the transformed acceleration signal, and constructing a frequency domain linear step model;

and fusing the time domain linear step size model and the frequency domain linear step size model by using a weighting method to obtain a fused step size model.

The pedestrian step size model established by the method greatly improves the step size estimation precision under complex gait under the condition of effectively fusing time domain and frequency domain step size models, realizes high-precision positioning and navigation of pedestrians under multiple motion states, and greatly improves the pedestrian dead reckoning precision under complex gait.

The technical solution of the present invention is explained in detail with reference to a specific embodiment. As shown in fig. 1, the specific method is as follows:

(1) inertial data acquisition

The micro inertial sensor is fixedly connected to the waist of the pedestrian, original inertial data under walking, running, side walking, back walking and other gaits are collected, and asynchronous inertial data are segmented to determine the starting and stopping time of each step.

(2) Establishing a time domain linear step size model

Extracting time domain motion characteristics such as step frequency, acceleration variance and the like:

f_step＝1/(t_i-t_i-1)

wherein f is_stepAnd upsilon represents the step frequency and the acceleration variance, t, respectively_i-1And t_iRespectively the start and end times of the ith step, a_tThe vertical acceleration output for time t is obtained,is the average value of the vertical acceleration in the process of the ith step, and N is the acceleration sampling number in the ith step.

Constructing a time domain linear step size model based on time domain motion characteristic parameters such as step frequency, acceleration variance and the like:

wherein the content of the first and second substances,respectively represents time domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated. The pre-calibrated model parameters can be determined by a table look-up method, the model parameters are calculated by collecting multi-target inertia data under walking, running, side walking, back walking and other gaits and a statistical method, and corresponding parameters are manufacturedAnd standardizing the table for table lookup.

(3) Frequency domain transformation of raw inertial data

In order to extract the frequency domain characteristics of the out-of-sync inertial data, fractional Fourier transform is performed on the original inertial data. The fractional Fourier transform integrates partial effective information in a time domain while keeping the property of Fourier transform, eliminates redundant information, ensures that sequences which are similar in time domain performance have certain discrimination after transformation, and can obtain a matched step size model aiming at an asynchronous state. Defining the acceleration vector sum signal as x (t) in a single step period, and carrying out p-order Fourier transform on the acceleration vector sum signal as:

wherein, K_p(u, t) is the integral kernel function:

wherein the content of the first and second substances,n is an integer, X_p(u) may be further represented as:

wherein, F^pDefined as the fractional fourier transform operator, α ═ p pi/2.

The higher the order of the fractional fourier transform, the less time domain features the output retains, and the more concentrated the energy. The invention carries out transformation aiming at the time domain signals in the single step period, the number of sampling points is less, therefore, the transformation order p is selected to be within the range of 0.2-0.5, and certain time domain characteristics are kept while introducing frequency domain characteristics. In this embodiment, the transformation order p is 0.2.

(4) Extracting frequency domain step size correlation factor and establishing frequency domain linear step size model

On the basis of time-frequency transformation, step length related factors capable of enhancing the asynchronous state discrimination are selected, wherein the step length related factors include a standard deviation factor and a quartile difference factor.

The standard deviation factor can be expressed as:

wherein N is the number of acceleration samples added in the step i, MoX_p(. a) a process of taking a modulus value for the acceleration signal after the p-order Fourier transform, M^FIs the average value of the acceleration signal amplitude, and is expressed as:

ordering the acceleration signals after the p-order Fourier transform into q from small to large_i1,2,3, k, the quartering difference factor may be expressed as:

wherein INT (-) is a rounding operation.

The frequency domain linear step size model obtained by using the linear combination mode is as follows:

wherein the content of the first and second substances,respectively representing frequency domain step models of walking, running, side walking and back walking,the model parameters are pre-calibrated.

(5) Establishing a fusion step size model

Combining the time domain characteristics and the frequency domain characteristics, fusing a time domain linear step size model and a frequency domain linear step size model by using a weighting method, constructing a fusion step size model, and realizing the step size estimation of the complex gait, wherein the formula is as follows:

wherein the content of the first and second substances,respectively representing the weights of the time domain step size model and the frequency domain step size model in the asynchronous state, and the selection of the weights is related to the quality of the signal. For example, when walking backward and walking sideways, the original signal contains more high-frequency noise due to poor body stability, so that the reliability of the signal in the frequency domain is reduced, and the corresponding signalsThe value is low, and the method has the advantages of low value,the value ranges are all 0.4-0.6; the time domain signals of walking and running have strong periodicity, corresponding toThe value is higher,the value ranges are all 0.6-0.8,the value ranges are all 0.6-0.7. In this embodiment, the weight table of the out-of-sync fusion step size model is shown in table 1.

TABLE 1 asynchronous dynamic fusion step-size model weight table

Features that are described and/or illustrated above with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.

It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof.

The many features and advantages of these embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of these embodiments which fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

The invention has not been described in detail and is in part known to those of skill in the art.