CN103411607B - Pedestrian's step-size estimation and dead reckoning method - Google Patents

Abstract

The invention discloses a pedestrian step length estimation and dead reckoning method. The method includes the following steps: a) using a portable terminal with an acceleration sensor to measure the personal step length parameter K when the pedestrian walks according to the normal step length; b) detecting the pedestrian walking The maximum value a _max and the minimum value a _min of the total acceleration amplitude in one step process; c) using the formula Calculate the step size step _size of the pedestrian's walking step. A new nonlinear step size model proposed by the invention more accurately represents the relationship between the step size of a pedestrian's walking step and the maximum value and minimum value of the total acceleration amplitude during the period. The proposed method of pedestrian step estimation and dead reckoning is easy to realize by computer, and can calculate the position of pedestrians in real time to realize indoor positioning, which has practical application significance.

Description

Pedestrian Step Length Estimation and Dead Reckoning Method

technical field

The invention relates to the field of pedestrian dead reckoning. Specifically, the present invention relates to a new pedestrian step estimation and dead reckoning method.

Background technique

Walking dead reckoning refers to the measurement and estimation of the number of steps, step length, and direction of pedestrians, so as to calculate the trajectory and position of pedestrians. It is one of the technologies for indoor positioning. The walking direction of pedestrians can be measured by existing heading measuring instruments, such as electronic compass and gyroscope. However, how to estimate the pedestrian's step length in real time during the walking process is a difficult problem, and the step length estimation will directly affect the accuracy of walking dead reckoning. For estimating step size, currently existing models can be divided into four categories: constant/pseudo-constant step size models, linear step size models, nonlinear step size models, and artificial intelligence step size models. Considering the convenience and real-time performance of the algorithm, as well as the accuracy of the step size estimation, the nonlinear step size model is often selected. At present, the commonly used nonlinear step size models mainly include the following types:

The comparative literature "H.Weinberg, "Using the ADXL202in Pedometer and Personal Navigation Applications," Analog Devices AN-602Application Note, 2002." discloses the Weinberg approach model:

${\mathrm{<span\; class="notranslate">\; step</span>}}_{\mathrm{<span\; class="notranslate">\; size</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}^{<span\; class="notranslate">\; *</span>}\sqrt[\mathrm{<span\; class="notranslate">\; 4</span>}]{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}$

Comparative literature "J.Scarlet, "Enhancing the Performance of Pedometers Using a Single Accelerometer," Analog Devices AN-900Application Note, 2005." discloses the Scarlet approach model:

${\mathrm{<span\; class="notranslate">\; step</span>}}_{\mathrm{<span\; class="notranslate">\; size</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}^{<span\; class="notranslate">\; *</span>}\frac{\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; |</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}\mathrm{<span\; class="notranslate">\; in</span>}}}{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}$

Comparative literature "J.W.Kim, H.J.Jang, D-H.Hwang, and C.Park, "A Step, Stride and Heading Determination for the Pedestrian Navigation System," Journal of Global Positioning Systems, pp.273-279, 2004." Kim approach model:

${\mathrm{<span\; class="notranslate">\; step</span>}}_{\mathrm{<span\; class="notranslate">\; size</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}^{<span\; class="notranslate">\; *</span>}\sqrt[\mathrm{<span\; class="notranslate">\; 3</span>}]{\frac{\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; N</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; |</span>}{\mathrm{<span\; class="notranslate">\; N</span>}}}$

In these formulas, a _max and a _min represent the maximum and minimum total acceleration amplitudes generated when the _pedestrian walks one step, respectively; The j-th total acceleration amplitude in ; K represents the parameters of each formula for different walkers, that is, the parameters given by the statistical comparison between the actual step length and the estimated step length when different walkers walk according to the normal step length. Since the above formulas give corresponding parameters for different walkers, when a walker walks with a normal step length, the error of the step size estimated by the above step size estimation algorithm is smaller than the actual step size.

However, it is found through experiments that when the same pedestrian walks with different step lengths, the step size estimated by the existing nonlinear step size estimation algorithm has a larger error than the actual step size.

Invention patent content

One of the purposes of the present invention is to provide a new pedestrian step size estimation method in order to overcome the deficiencies in the prior art. This method proposes a new nonlinear step size model, so that it can not only accurately estimate different pedestrian The normal step length of the same walker also has better adaptability to different step lengths of the same walker, and reduces the error of different step length estimation of the same walker without increasing the amount of calculation.

To achieve this goal, the present invention adopts following technical scheme:

A pedestrian step length estimation method, the method comprises the steps of:

a) Using a portable terminal with an acceleration sensor to measure the personal step length parameter K when the pedestrian walks according to the normal step length;

b) Detect the maximum value a _max and the minimum value a _min of the total acceleration amplitude during the pedestrian's walking step;

c) Using the formula ${\mathrm{step}}_{\mathrm{size}}=K^{*}((a_{\max }-a_{\min })+\sqrt[4]{a_{\max }-a_{\min }}),$ Calculate the step size step _size of the pedestrian's walking step.

Preferably, the method for measuring the personal step size parameter K in step a) may include the following steps:

a-1) Set the parameter Ki as an arbitrary constant;

a-2) The pedestrian carries the portable terminal and walks n steps according to the normal step length, and the measured actual walking is X meters. During the process, the portable terminal remains stationary relative to the upper body of the pedestrian carrying it;

a-3) Detect the maximum value a _max and the minimum value a _min of the total acceleration amplitude during each step of the pedestrian walking n steps, using the formula ${\mathrm{step}}_{\mathrm{size}}={\mathrm{Ki}}^{*}((a_{\max }-a_{\min })+\sqrt[4]{a_{\max }-a_{\min }})$ Estimating the step length of each step of the pedestrian's n steps, adding up the step length of each step, and estimating the distance Y meters of the pedestrian's n-step walk;

a-4) Using the formula Calculate the individual step size parameter K.

Preferably, in the step b), the method for detecting the maximum value a _max and the minimum value a _min of the total acceleration amplitude during the pedestrian walking one step may include the following steps:

b-1) The pedestrian carries a portable terminal that remains stationary relative to his upper body and walks one step, and obtains the total acceleration amplitude accl through the acceleration sensor embedded in the portable terminal. During the pedestrian's one-step walking process, the frequency of the acceleration sensor detecting acceleration is not less than 15Hz ;

b-2) Detect peaks and valleys of the total acceleration amplitude during a step;

b-3) When the difference between the peak value and the valley value of the total acceleration amplitude exceeds the threshold, and their time interval is within a certain range, the peak value is the maximum value a _max of the total acceleration amplitude of the pedestrian walking one step , the valley value is the minimum value a _min of the total acceleration amplitude when the pedestrian walks one step.

Preferably, step b-1-1) may also be included after the step b-1):

b-1-1) Use a sliding window filter to perform signal filtering on the obtained total acceleration amplitude accl to make the total acceleration amplitude signal curve smooth.

Preferably, the acceleration sensor of the portable terminal can be a three-axis acceleration sensor. In the step b-1), the three-axis acceleration sensor detects the amplitudes of the three axes of acceleration (accl_x, accl_y, accl_z), and then uses the formula $\mathrm{accl}=\sqrt{{(\mathrm{accl}\_x)}^2+{(\mathrm{accl}\_\mathrm{the\; y})}^2+{(\mathrm{accl}\_z)}^2}$ Calculate the total acceleration amplitude accl.

Preferably, the portable terminal may include an acceleration sensor and a processor, the acceleration sensor transmits the detected acceleration data to the processor, and the processor performs calculations to implement the method, or the processor uploads the data to the remote background, and performs the calculation in the background. Computing implements this method.

The second object of the present invention is to provide a new dead reckoning method for pedestrians in order to overcome the deficiencies of the prior art, which is convenient for real-time estimation of pedestrian step length and dead reckoning by using computers or other electronic devices.

To achieve this goal, the present invention adopts following technical scheme:

A pedestrian dead reckoning method utilizing the pedestrian step length estimation method described in claim 1, the method comprising the steps of:

a) Set the initial position of the pedestrian;

b) Using the pedestrian step estimation method described in claim 1 to estimate the step length of a pedestrian's walking step; at the same time, using the heading measuring instrument to measure the heading angle of the pedestrian's walking step;

c) Estimate the new position of the pedestrian based on the estimated step length and heading angle of the pedestrian's walking step.

Preferably, the set initial position of pedestrians can be obtained through manual input or by using WIFI or GPS positioning.

Preferably, the heading angle at which the pedestrian takes one step can be measured by an electronic compass or a gyroscope.

The present invention proposes a new nonlinear step size model:

${\mathrm{<span\; class="notranslate">\; step</span>}}_{\mathrm{<span\; class="notranslate">\; size</span>}}<span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; K</span>}}^{<span\; class="notranslate">\; *</span>}<span\; class="notranslate">\; (</span><span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\sqrt[\mathrm{<span\; class="notranslate">\; 4</span>}]{{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; a</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; ,</span>$

This model more accurately represents the relationship between the step length of a pedestrian's walking step and the maximum and minimum values of the total acceleration amplitude during the period. Experiments show that the pedestrian step size estimation method using this nonlinear step size model has better adaptability to different step lengths of the same pedestrian without increasing the amount of calculation, making the step size estimation more accurate. The dead reckoning method for pedestrians proposed by the invention is convenient for computer implementation, and the position of pedestrians can be calculated in real time to realize indoor positioning, which has practical application significance.

The present invention will become clearer through the following description in conjunction with the accompanying drawings, which are used to explain the embodiments of the present invention.

Description of drawings

Fig. 1 is the flowchart of embodiment 1 of the present invention;

Fig. 2 is the flowchart of step 100 of embodiment 1 of the present invention;

Fig. 3 is the acceleration amplitude waveform figure of embodiment 1 of the present invention;

4 to 6 are effect diagrams of Embodiment 1 of the present invention;

Fig. 7 is a schematic diagram of Embodiment 2 of the present invention;

Fig. 8 is a flowchart of Embodiment 2 of the present invention.

Detailed ways

Embodiment 1. This embodiment relates to a method for estimating pedestrian step length, and its flow chart refers to FIG. 1 . First, a pedestrian carries a portable terminal and tries to walk with a normal step length. The portable terminal includes a three-axis acceleration sensor and a processor, and the portable terminal remains stationary relative to the upper body of the pedestrian carrying it. Specifically, the portable terminal can be held in a pedestrian's non-swaying fixed hand, or can be fixed on the pedestrian's waist, and is not limited thereto. During this process, the personal step length parameter K is identified and stored (step 100). The method of identifying the individual step size parameter K will be described in detail in the subsequent section.

After obtaining the pedestrian's personal step length parameter K, the pedestrian's step length can be estimated. When a pedestrian walks with the portable terminal, the frequency of the acceleration detected by the three-axis acceleration sensor is not less than 15Hz, and the three-axis acceleration sensor detects the amplitude of the three axes of acceleration (accl_x, accl_y, accl_z) and then uses the formula $\mathrm{accl}=\sqrt{{(\mathrm{accl}\_x)}^2+{(\mathrm{accl}\_\mathrm{the\; y})}^2+{(\mathrm{accl}\_z)}^2}$ Calculate the total acceleration amplitude accl (step 101).

Then, a sliding window filter can also be used to perform signal filtering on the obtained total acceleration amplitude accl to make the waveform smooth.

Fig. 3 is a waveform diagram of the total acceleration amplitude of the embodiment of the present invention. As shown in the figure, the abscissa is the sampling point number of the acceleration amplitude, and the ordinate is the total acceleration amplitude. The line shape is the original total acceleration signal waveform of the pedestrian walking four steps, The line shape is the total acceleration signal waveform filtered by the sliding window.

It can be seen from Figure 3 that the total acceleration amplitude will have a maximum value and a minimum value every time the pedestrian takes a step, and the acceleration signal presents a periodic characteristic as the pedestrian walks. Therefore, the peak detection method is used to detect the peak value and valley value of the total acceleration amplitude. If the difference between the peak value and the valley value exceeds the threshold and their time interval is within a certain range, it can be judged that the pedestrian is walking. step. The peak value of this wave is the maximum value a _max of the total acceleration amplitude during the pedestrian's walking step, and the valley value is the minimum value a _min of the total acceleration amplitude during the pedestrian's walking step (step 102 ). The threshold value of the difference between the peak value and the valley value can be determined according to the maximum and minimum values of the total acceleration amplitude of each step in the pedestrian trial walking process. Specifically, the threshold value can be equal to the pedestrian trial walking process of each step. The average value of the difference between the maximum and minimum values of the total acceleration magnitude is multiplied by 0.8. The range of the time interval between the peak value and the valley value can be determined according to the pedestrian's step frequency. Generally, the pedestrian's step frequency is about 1-3 Hz, and the time interval can range from 0.3 second to 1 second. In practical applications, the method of detecting the maximum value a _max and the minimum value a _min of the acceleration amplitude during a pedestrian's walking step is not limited to the above method, and other effective methods may also be used.

Finally, using the formula ${\mathrm{step}}_{\mathrm{size}}=K^{*}((a_{\max }-a_{\min })+\sqrt[4]{a_{\max }-a_{\min }})$ Calculate the step size step _size of the pedestrian's walking step (step 103). This formula is a kind of new nonlinear step size model that the present invention proposes,

Referring to FIG. 2 , it is a flowchart of step 100 in Embodiment 1 of the present invention. The method for identifying the personal step length parameter K in step 100 is as follows: first, set the parameter Ki to be an arbitrary constant (step 200), and carry a portable terminal to try to walk n steps according to the normal step length, and measure the actual walking X meters. , the portable terminal remains stationary relative to the upper body of the pedestrian carrying it (step 201). Detect the maximum value a _max and the minimum value a _min of the total acceleration amplitude during each step of the pedestrian walking n steps, using the formula ${\mathrm{step}}_{\mathrm{size}}={\mathrm{Ki}}^{*}((a_{\max }-a_{\min })+\sqrt[4]{a_{\max }-a_{\min }})$ Estimate the step length of each step of the pedestrian's n steps, and accumulate the steps of each step to estimate the distance Y meters of the pedestrian's n steps (step 202 ). use the formula Calculate the personal step length parameter K of the pedestrian (step 203).

In this embodiment, the three-axis acceleration sensor of the portable terminal transmits the detected acceleration data to the processor of the portable terminal, and the processor performs calculations to estimate the pedestrian's step length. In practical applications, the data can also be uploaded to the remote background, and calculations are performed in the background to realize the method, and there is no limitation on the hardware devices used to realize the method.

Referring to FIG. 4 to FIG. 6 , they are effect diagrams of Embodiment 1 of the present invention. The step size estimation method proposed in this paper and the three step size estimation methods of Weinberg, Kim and Scarlet in the background technology are implemented on the XIAOMI M1 smart phone platform. The same walker walks 10m, 30m, and 50m with different step lengths while holding a mobile phone. The experimental results are shown in Figures 4, 5, and 6, where and The four line types represent the results of the step size estimation method proposed in this paper and the three step size estimation methods of Weinberg, Kim, and Scarlet in the background technology respectively. The vertical axis in the figure represents the walking distance estimated by the step size estimation method. The total acceleration amplitude used by the Kim step estimation method excludes the gravitational acceleration.

Found through experiments:

When a pedestrian walks with a normal step length (14 steps for 10m, 41 steps for 30m, 68 steps for 50m), as shown in Figure 3, the estimation results of the four algorithms are basically the same, and the error is not large;

When the same pedestrian strides (10 steps are used for walking 10m, 30 steps are used for walking 30m, and 51 steps are used for walking 50m), as shown in Figure 4, the pedestrian actually walks 50m, and the estimation result of the step length estimation method of the present invention is 51.94 m, while the estimation results of the Weinberg, Kim and Scarlet step length estimation methods are about 41.62m, 45.41m and 40.32m respectively, with large errors;

When the same walker walks with a small step length (20 steps for walking 10m, 60 steps for walking 30m, and 100 steps for walking 50m), as shown in Figure 5, the walker actually walks 50m, the estimated result of the step length estimation method of the present invention It is 52.44m, while the estimation results of Weinberg, Kim and Scarlet step length estimation methods are about 60.57m, 61.05m and 76.12m respectively, with large errors.

Therefore, it is proved by experiments that the present invention better solves the problem that the pedestrian's step length estimated by the existing nonlinear step length estimation method has a large error when the same pedestrian walks with different step lengths. practical significance.

Embodiment 2, the embodiment of the present invention relates to a method for realizing indoor positioning by using the dead reckoning method for pedestrians, and refer to FIG. 8 for the flow chart. For indoor positioning of pedestrians, the initial position must first be obtained. The initial position of the pedestrian can be obtained through manual input or other positioning methods, such as WIFI positioning (step 300 ). Then, the pedestrian walks one step, according to a pedestrian step estimation method described in Embodiment 1, the step length of the pedestrian's walking step is estimated (step 301), and at the same time, the pedestrian's walking step is obtained by using an electronic compass or a gyroscope or other instrument sensors. heading angle (step 302), and finally estimate the new position of the pedestrian according to the estimated step size and the measured heading angle (step 303), and then update the initial position of the pedestrian with the estimated new position (back to Step 300), the pedestrian walks another step, and the new position of the pedestrian is re-estimated according to steps 300, 301, 302, 303, and so on, so as to realize the indoor positioning of the pedestrian.

Referring to FIG. 7 , it is a schematic diagram of a method for estimating a new position of a pedestrian by using the estimated step size and the measured heading angle in this embodiment. Assume that a pedestrian is at P _n-1 (X _n-1 , Y _{n-1 )} at a certain moment, and the angle between the walking direction and the E axis _is α. _n ), then the relationship between P _n (X _n ,Y _n ) and P _n-1 (X _n-1 ,Y _n-1 ) is as follows: $\left\{\begin{array}{c}{x}_{\mathrm{no}}=x_{\mathrm{no}-1}+d\&times;\cos \mathrm{\&alpha;}\\ Y_{\mathrm{no}}=Y_{\mathrm{no}-1}+d\&times;\sin \mathrm{\&alpha;}\end{array}\right..$

The present invention has been described above in conjunction with the best embodiments, but the present invention is not limited to the above-disclosed embodiments, but should cover various modifications and equivalent combinations made according to the essence of the present invention.

Claims (9)

1. A pedestrian step length estimation method, the method may further comprise the steps: a) Utilize a portable terminal with an acceleration sensor to measure the personal step length parameter K when the pedestrian walks according to the normal step length; b) Detect the maximum value a _max and the minimum value a _min of the total acceleration amplitude during the pedestrian's walking step; c) using the formula ${\mathrm{step}}_{\mathrm{the\; s}ize}=K*\left(\right(a_{max}-a_{mi\mathrm{no}})+\sqrt[4]{a_{max}-a_{mi\mathrm{no}}}),$ Calculate the step size step _size of the pedestrian's walking step. 2. a kind of pedestrian step length estimation method according to claim 1, is characterized in that, the method for measuring personal step length parameter K in described step a) comprises the steps: a-1) setting parameter Ki to be an arbitrary constant; a-2) The pedestrian carries the portable terminal to try to walk n steps according to the normal step length, and the measured actual walking is X meters. During this process, the portable terminal remains stationary relative to the upper body of the pedestrian carrying it; a-3) Detect the maximum value a _max and the minimum value a _min of the total acceleration amplitude during each step of the pedestrian walking n steps, using the formula ${\mathrm{step}}_{\mathrm{the\; s}ize}=Ki*\left(\right(a_{max}-a_{mi\mathrm{no}})+\sqrt[4]{a_{max}-a_{mi\mathrm{no}}})$ Estimating the step length of each step of the pedestrian's n steps, adding up the step length of each step, and estimating the distance Y meters of the pedestrian's n-step walk; a-4) Using the formula Calculate the individual step size parameter K. 3. a kind of pedestrian step length estimation method according to claim 1, is characterized in that, in described step b) in the method for detecting the maximum value a _max and the minimum value a _min of the total acceleration amplitude value in the step process of pedestrian walking Including the following steps: b-1) The pedestrian carries a portable terminal that remains stationary relative to his upper body and walks for one step, and obtains the total acceleration amplitude accl through the acceleration sensor embedded in the portable terminal. During the pedestrian's one-step walking process, the frequency of the acceleration sensor detecting acceleration is not less than 15Hz ; b-2) Detect the peak value and valley value of the total acceleration amplitude during one step; b-3) When the difference between the peak value and the valley value of the total acceleration amplitude exceeds the threshold, and their time interval is within a certain range, then this peak value is the maximum value a _max of the total acceleration amplitude of the pedestrian walking one step , the valley value is the minimum value a _min of the total acceleration amplitude when the pedestrian walks one step. 4. a kind of pedestrian step length estimation method according to claim 3, is characterized in that, also comprises step b-1-1) after described step b-1): b-1-1) Use a sliding window filter to perform signal filtering on the obtained total acceleration amplitude accl to make the total acceleration amplitude signal curve smooth. 5. a kind of pedestrian step length estimation method according to claim 3 is characterized in that, the acceleration sensor of portable terminal is a three-axis acceleration sensor, and in described step b-1), three axis acceleration sensors detect three axes of acceleration The magnitude of (accl_x,accl_y,accl_z), then use the formula $accl=\sqrt{{(accl\_x)}^2+{(accl\_\mathrm{the\; y})}^2+{(acc\_z)}^2}$ Calculate the total acceleration amplitude accl. 6. A kind of pedestrian step estimation method according to claim 1, is characterized in that, portable terminal comprises acceleration sensor and processor, and acceleration sensor transmits the acceleration data that detects to processor, calculates and realizes this by processor. method, or the processor uploads the data to the remote background, and performs calculations in the background to realize the method. 7. A pedestrian dead reckoning method utilizing the pedestrian step length estimation method according to claim 1, the method comprising the steps of: a) Set the pedestrian's initial position P _n-1 (X _n-1 , Y _n-1 ); b) Utilize the method for estimating pedestrian step length described in claim 1 to estimate the step length d of the pedestrian walking one step; simultaneously utilize the heading measuring instrument to measure the heading angle α of the pedestrian walking the step; c) using the formula $\left\{\begin{array}{c}{x}_{\mathrm{no}}=x_{\mathrm{no}-1}+d\&times;\cos \mathrm{\&alpha;}\\ Y_{\mathrm{no}}=Y_{\mathrm{no}-1}+d\&times;\sin \mathrm{\&alpha;}\end{array}\right.$ Estimate the new location of the pedestrian. 8. A dead reckoning method for pedestrians according to claim 7, characterized in that the initial position of the set pedestrians is obtained through manual input or using WIFI or GPS positioning. 9. A dead reckoning method for pedestrians according to claim 7, characterized in that the heading angle at which the pedestrian walks one step is measured by an electronic compass or a gyroscope.