Parameter estimation method, device and electronic equipment for pedestrian dead reckoning model

Abstract

The present invention provides a parameter estimation method, device and electronic device for a pedestrian dead reckoning model. The method uses fast Fourier transform to process vertical acceleration data corresponding to multiple outdoor walking paths of a user to obtain frequency domain modulus values of the acceleration data, and then obtains personalized frequency options and corresponding step count options for PDR through fitting or machine learning methods. Then, the path length calculated using GPS positioning data is used as the true value, and the frequency options and corresponding step count options are combined to determine step length calculation parameters and step count calculation parameters through fitting. These parameters conform to the user's actual walking characteristics, and the user does not need to provide privacy data such as height. This not only improves the indoor navigation accuracy based on PDR, but also, when outdoor walking data continues to be obtained, the PDR parameters can be continuously updated based on more walking data, so that the personalized PDR parameters are gradually optimized and iterated.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a parameter estimation method, device and electronic equipment for a pedestrian dead reckoning model. Background Art

[0002] In recent years, with the rapid development of food delivery services such as food delivery, people have put forward higher requirements for navigation technology.

[0003] However, due to the obstruction and reflection of high-rise buildings, existing GPS positioning technology is not suitable for indoor scenarios. Existing navigation technology based on the pedestrian dead reckoning (PDR) model, although suitable for indoor navigation, not only does this technology require additional positioning reference to the Global Positioning System (GPS) during use, but it also cannot adaptively calculate and optimize personalized PDR parameters for individuals, resulting in large positioning errors and easily causing inconvenience to users. Summary of the Invention

[0004] In view of this, the present invention aims to propose a parameter estimation method, device, storage medium, and electronic device for a pedestrian dead reckoning model to address the problem that existing pedestrian dead reckoning models are unable to adaptively adjust parameters according to different users, resulting in large positioning errors and easily causing inconvenience to users.

[0005] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] A method for estimating parameters of a pedestrian dead reckoning model, applied to an electronic device, wherein the method comprises:

[0007] Obtain GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user;

[0008] Processing the acceleration data of each outdoor walking path using a fast Fourier transform to obtain a frequency domain modulus value of the acceleration data;

[0009] Determining a frequency candidate and a step number candidate corresponding to the frequency candidate according to the frequency domain modulus and the acceleration data;

[0010] Determining the length of each outdoor walking path according to the GPS positioning data;

[0011] Determining, by fitting, a step length calculation parameter and a step count calculation parameter based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step count options corresponding to each of the outdoor walking paths, and the frequencies corresponding to the step count options, such that, for each of the outdoor walking paths, a difference between a product of a calculated step length and a calculated number of steps and the path length is within a preset difference range;

[0012] Among them, the step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter.

[0013] Furthermore, in the method, for each of the outdoor walking paths, the step of determining a frequency candidate and a step number candidate corresponding to the frequency candidate based on the frequency domain modulus and the acceleration data includes:

[0014] Determining, based on the frequency domain modulus, a DC component and N amplitude candidate options, and a frequency candidate option corresponding to each of the amplitude candidate options; wherein the N amplitude candidate options are the first N larger amplitudes in the frequency domain modulus;

[0015] Determine N step number calculation parameter options according to the N amplitude options and the corresponding N frequency options, wherein the step number calculation parameter options include a peak and trough threshold, a cycle upper limit, and a cycle lower limit;

[0016] N step number options are determined according to the DC component, the acceleration data, and the N step number calculation parameter options.

[0017] Furthermore, in the method, the step of determining a step length calculation parameter and a step count calculation parameter based on multiple path lengths corresponding to the multiple outdoor walking paths, the step count options corresponding to each of the outdoor walking paths, and the frequency options corresponding to the step count options, includes:

[0018] Taking any one of the multiple outdoor walking paths as a target outdoor walking path, sequentially selecting one from N frequency alternatives corresponding to the target outdoor walking path as a frequency alternative, and determining a step number alternative corresponding to the target frequency alternative in each of the outdoor walking paths;

[0019] Determining a step length calculation parameter option for a linear prediction model for calculating step length based on step frequency according to the target frequency option, the path length corresponding to each of the outdoor walking paths, and the step number option corresponding to the target frequency option;

[0020] From the multiple step length calculation parameter options and the corresponding multiple step count calculation parameter options, determine the step length calculation parameter and the step count calculation parameter for the user, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

[0021] Furthermore, in the method described above, for any one of the N amplitude options,

[0022] The step of determining N step number calculation parameter options based on the N amplitude options and the corresponding N frequency options includes:

[0023] Determine a peak and trough threshold by multiplying the amplitude candidate by (1-first adjustment coefficient), wherein the first adjustment coefficient is greater than 0 and less than 1;

[0024] The upper limit of the period is determined by dividing (1+the second adjustment coefficient) by the frequency alternative, and the lower limit of the period is determined by dividing (1-the second adjustment coefficient) by the frequency alternative; wherein the second adjustment coefficient is greater than 0 and less than 1.

[0025] Furthermore, in the method, the step of determining N step number options based on the DC component, the acceleration data, and the N step number calculation parameter options includes:

[0026] subtracting the DC component from the acceleration data to obtain adjusted acceleration data;

[0027] N step number options are determined based on the adjusted acceleration data and the N step number calculation parameter options.

[0028] Furthermore, in the method, before the step of processing the acceleration data of each outdoor walking path using a fast Fourier transform to obtain the frequency domain modulus value of the vertical acceleration, the method further includes:

[0029] The acceleration data is converted from the device coordinate system to the north-east coordinate system, and the gravity acceleration in the vertical direction is subtracted.

[0030] Another object of the present invention is to provide a parameter estimation device for a pedestrian dead reckoning model, which is applied to an electronic device, wherein the device comprises:

[0031] An acquisition module is used to obtain GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user;

[0032] a processing module, configured to process the acceleration data of each outdoor walking path using a fast Fourier transform to obtain a frequency domain modulus of the acceleration data;

[0033] A first determining module is configured to determine a frequency candidate and a step number candidate corresponding to the frequency candidate according to the frequency domain modulus and the acceleration data;

[0034] A second determining module is used to determine the path length of each of the outdoor walking paths according to the GPS positioning data;

[0035] a third determining module, configured to determine, by fitting, a step length calculation parameter and a step number calculation parameter based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step number alternative corresponding to each of the outdoor walking paths, and the frequency alternative corresponding to the step number alternative, such that, for each of the outdoor walking paths, a difference between a product of a calculated step length and a calculated number of steps and the path length is within a preset difference range;

[0036] Among them, the step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter.

[0037] Furthermore, in the device, the first determining module includes:

[0038] a first determining unit configured to determine, for each of the outdoor walking paths, a DC component and N amplitude candidate options, and a frequency candidate corresponding to each of the amplitude candidate options based on the frequency domain modulus value; wherein the N amplitude candidate options are the first N larger amplitudes in the frequency domain modulus value;

[0039] a second determining unit, configured to determine N step number calculation parameter options based on the N amplitude options and the corresponding N frequency options, wherein the step number calculation parameter options include a peak and trough threshold, a cycle upper limit, and a cycle lower limit;

[0040] The third determining unit is configured to determine N step number options according to the DC component, the acceleration data, and the N step number calculation parameter options.

[0041] Furthermore, in the device, the third determining module includes:

[0042] a fourth determining unit, configured to take any one of the plurality of outdoor walking paths as a target outdoor walking path, sequentially select one from the N frequency alternatives corresponding to the target outdoor walking path as a frequency alternative, and determine a step number alternative corresponding to the target frequency alternative in each of the outdoor walking paths;

[0043] a fifth determining unit, configured to determine, according to the target frequency candidate, the path lengths corresponding to the outdoor walking paths, and the step number candidate corresponding to the target frequency candidate, a step length calculation parameter candidate for a linear prediction model for calculating step length based on cadence;

[0044] The sixth determination unit is used to determine the step length calculation parameter and the step count calculation parameter for the user from the multiple step length calculation parameter options and the corresponding multiple step count calculation parameter options, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

[0045] Furthermore, in the device, the second determining unit includes:

[0046] a first determining subunit, configured to determine, for any one of the N amplitude candidate options, a peak / valley threshold by multiplying the amplitude candidate option by (1-a first adjustment coefficient), wherein the first adjustment coefficient is greater than 0 and less than 1;

[0047] The second determination subunit is used to determine the upper limit value of the period by dividing (1+the second adjustment coefficient) by the frequency option for any one of the N amplitude option options, and to determine the lower limit value of the period by dividing (1-the second adjustment coefficient) by the frequency option; wherein the second adjustment coefficient is greater than 0 and less than 1.

[0048] Furthermore, in the device, the third determining unit includes:

[0049] an adjustment subunit, configured to subtract the DC component from the acceleration data to obtain adjusted acceleration data;

[0050] The third determining subunit is configured to determine N step number options based on the adjusted acceleration data and the N step number calculation parameter options.

[0051] Furthermore, the device further comprises:

[0052] The pre-processing module is used to convert the acceleration data from the device coordinate system to the north-east coordinate system and subtract the gravity acceleration in the vertical direction before processing the acceleration data of each outdoor walking path using the fast Fourier transform to obtain the frequency domain modulus value of the vertical acceleration.

[0053] Compared with the prior art, the parameter estimation method and device of the pedestrian dead reckoning model described in the present invention have the following advantages:

[0054] Fast Fourier transform is used to process the vertical acceleration data corresponding to multiple outdoor walking paths of the user to obtain the frequency domain modulus of the acceleration data, and then the personalized frequency options and corresponding step number options of PDR are obtained through fitting or machine learning methods; then the path length calculated by GPS positioning data is used as the true value, combined with the frequency options and the corresponding step number options, the step length calculation parameters and the step number calculation parameters are determined by fitting, so that each parameter conforms to the actual walking characteristics of the user, and there is no need for the user to provide privacy data such as height. This not only improves the indoor navigation accuracy based on PDR, but also when outdoor walking data continues to be obtained, the PDR parameters can be continuously updated based on more walking data, so that the personalized PDR parameters are gradually optimized and iterated.

[0055] Yet another object of the present invention is to provide a storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the parameter estimation method of the pedestrian dead reckoning model as described above.

[0056] Another object of the present invention is to provide an electronic device, comprising:

[0057] a processor adapted to implement the instructions; and

[0058] A storage medium is adapted to store a plurality of instructions, wherein the instructions are adapted to be loaded by a processor and executed by the parameter estimation method of the pedestrian dead reckoning model as described above.

[0059] Yet another object of the present invention is to provide an electronic device, wherein the electronic device includes the parameter estimation device of the pedestrian dead reckoning model as described above.

[0060] The storage medium, electronic device, and vehicle have the same advantages as the parameter estimation method and device of the above-mentioned pedestrian dead reckoning model over the prior art, and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings, which constitute part of the present invention, are provided to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are provided to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 Schematic diagram of the PDR motion model in an embodiment of the present invention;

[0063] Figure 2 Schematic diagram of the process of step count estimation based on acceleration changes in an embodiment of the present invention;

[0064] Figure 3 A schematic flow chart of a parameter estimation method for a pedestrian dead reckoning model provided by an embodiment of the present invention;

[0065] Figure 4 Schematic diagram of vertical acceleration values changing with time in an embodiment of the present invention;

[0066] Figure 5 Schematic diagram of frequency domain modulus values in an embodiment of the present invention;

[0067] Figure 6 1. A flowchart of a method for estimating parameters of a pedestrian dead reckoning model according to an embodiment of the present invention;

[0068] Figure 7 This is a schematic diagram of the structure of a bus route details display device proposed in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0071] In recent years, smartphones equipped with various sensors have become increasingly popular, which can detect the periodic changes in the vertical accelerometer data caused by pedestrians walking, making inertial navigation and positioning technology based on the pedestrian dead reckoning model (Pedestrian Dead Reckoning, PDR) possible. Figure 1 shown.

[0072] Among them, θ i and d i are the heading azimuth and step length of each step respectively, S0 is the initial position (x i ,y i ), S i is the pedestrian position calculated after the i-th step, and its calculation formula is shown in Formula 1:

[0073]

[0074] Indoor navigation technology based on PDR consists of three parts: step counting, step length estimation, and heading angle measurement. Among them, step counting and step length estimation have personalized requirements, and the two parts together determine the total length of displacement. k represents the number of calculation steps, d i Represents the step length of the i-th step.

[0075] The commonly used algorithm for step count calculation is gait transition detection, which accumulates steps by detecting changes in gait. It involves three thresholds, including peak and trough thresholds. peak / buttom, maximum cycle threshold T max and the minimum cycle threshold T min .

[0076] For the step calculation process, please refer to Figure 2 .like Figure 2 As shown, when the acceleration is detected to be equal to 0, the gait is judged as Start and the step length detection begins; when the acceleration is greater than the threshold peak / buttom , the gait becomes Peak; then the acceleration is detected to be less than -threshold peak / buttom , the gait changes to Buttom; when the acceleration is detected to be 0 again, the gait changes to Start. If the time difference between the two Starts is t∈[T min , T max ], the step count is increased by 1. In the figure, the horizontal "--" dotted line is the average acceleration. It can be seen that the zero point of acceleration has shifted upward as a whole, which will affect not only the detection of the Start gait, but also the Peak and Buttom gait detection.

[0077] Because different pedestrians have different cadences and walking states, their vertical acceleration amplitudes are also different. Therefore, a fixed Threshold is used in step count calculation. peak / buttom Counting the number of steps will result in inaccurate step counting.

[0078] In addition, for step length estimation, models such as linear models, motion state models, empirical models, and neural network prediction models can be used for estimation. A linear prediction model based on step frequency and height is used, as shown in formula (2):

[0079] d=h*(a·f step +b)+c (2)

[0080] Among them, h is the height of the pedestrian, f step Pedestrian cadence.

[0081] When estimating the step length, not only the height of the pedestrian is required, but also the three formula parameters a, b, and c need to be provided. Moreover, combined with formula (1), it can be seen that the accuracy of step length estimation will greatly affect the indoor positioning accuracy based on PDR.

[0082] Based on the above problems, an embodiment of the present invention provides a method for estimating parameters of a pedestrian dead reckoning model, which is applied to electronic devices, wherein Figure 3 , which shows a flow chart of a parameter estimation method for a pedestrian dead reckoning model provided by an embodiment of the present invention, such as Figure 3 As shown, the method includes steps S100 to S500.

[0083] In an embodiment of the present invention, the above-mentioned electronic device may be an electronic device such as a mobile phone, a tablet computer, a smart bracelet, etc. equipped with sensors such as an accelerometer, a gyroscope, and a magnetometer.

[0084] Step S100: Obtain GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user.

[0085] In step S100, vertical acceleration data is obtained by periodically collecting sensor data such as accelerometer data, gyroscope data, and magnetometer data from the electronic device while the user is walking outdoors. GPS positioning data is also periodically collected, i.e., the current location of the electronic device is obtained using positioning technology. The path taken by the user within two or more consecutive GPS positioning data sampling intervals is considered the user's outdoor walking path. Optionally, the sensor data sampling interval is 0.01 seconds, and the GPS positioning data sampling interval is 1 second.

[0086] In practical applications, the vertical acceleration is recorded according to the sampling time. For details, please refer to Figure 4 As shown, the horizontal axis represents the sampling time, the sampling time interval is 0.01s, and the vertical axis represents the acceleration value in the vertical mode.

[0087] Step S200: Process the acceleration data of each outdoor walking path using fast Fourier transform to obtain a frequency domain modulus of the acceleration data.

[0088] In the above step S200, since the electronic device sensor has poor accuracy and a lot of noise, the vertical acceleration data measured by the accelerometer collected in step S100 is the superposition result of the user's walking and random noise, which can be expressed as:

[0089]

[0090] Among them, A D Indicates the DC component, A i Represents the amplitude of the i-th sub-signal, f represents the frequency corresponding to the sub-signal, and t represents time. The above acceleration data is processed using Fast Fourier Transform (FFT), which converts the acceleration data from time domain signals to frequency domain information, thereby obtaining the normalized unilateral vertical acceleration FFT modulus, which is also the above frequency domain modulus. The frequency domain modulus represents the corresponding relationship between frequency and the amplitude of the frequency signal. For details, please refer to Figure 5 , where the horizontal axis represents the frequency and the vertical axis represents the amplitude corresponding to the sine function of the frequency.

[0091] Step S300: Determine a frequency option and a step number option corresponding to the frequency option according to the frequency domain modulus and the acceleration data.

[0092] In the above step S300, the frequency domain modulus determines the corresponding relationship between the frequency signal and the amplitude in the acceleration data corresponding to the user's outdoor walking path, and the vertical acceleration periodic change A caused by walking step sin2πf step t should be the vertical acceleration The main component of the frequency domain modulus can be used to determine the possible walking frequency as the frequency alternative. The acceleration data is collected according to time. Therefore, combined with the walking duration corresponding to the acceleration data, the number of steps corresponding to each frequency alternative can be calculated as the step number alternative when the user walks through the outdoor walking path.

[0093] Step S400: determining the length of each outdoor walking path according to the GPS positioning data.

[0094] In the above step S400, because the GPS positioning data records the coordinate position of the user at the time of collecting and obtaining the positioning data, after the outdoor walking path is determined, the path length of each outdoor walking path can be calculated based on the above GPS positioning data.

[0095] Step S500: Determine, based on the multiple path lengths corresponding to the multiple outdoor walking paths, the step count options corresponding to each of the outdoor walking paths, and the frequencies corresponding to the step count options, a step length calculation parameter and a step count calculation parameter, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated step count and the path length is within a preset difference range;

[0096] Among them, the step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter.

[0097] In the above step S500, the above step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency; the above calculated number of steps is the number of steps determined for each outdoor walking path based on the above step number calculation parameter; the above calculated step length is the step length determined for each outdoor walking path based on the above step length calculation parameter and the step frequency corresponding to the step length calculation parameter.

[0098] In step S500, each outdoor walking path corresponds to a path length determined by GPS positioning data, and each outdoor walking path can determine the corresponding step count option and the frequency option corresponding to the step count option using vertical acceleration data. The path length can be determined by multiplying the number of steps by the step length, and the step length can be determined by using the frequency option as a possible cadence input into a linear prediction model for calculating step length based on cadence. Therefore, using the path length calculated by the outdoor GPS positioning data as the true value, combined with GPS positioning data and acceleration data corresponding to multiple outdoor walking paths, step length calculation parameters in the linear prediction model for calculating step length based on cadence can be solved, and the optimal step count option can be selected from the step count options. Furthermore, step count calculation parameters for the pedestrian dead reckoning model can be fitted and determined using machine learning, so that, for each outdoor walking path, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

[0099] Compared with the prior art, the parameter estimation method of the pedestrian dead reckoning model described in the present invention has the following advantages:

[0100] In the parameter estimation method of the above-mentioned pedestrian dead reckoning model, because the fast Fourier transform is used to process the vertical acceleration data corresponding to the user's multiple outdoor walking paths to obtain the frequency domain modulus of the acceleration data, the personalized frequency options and corresponding step number options of the PDR can be obtained through fitting or machine learning methods. Then, the path length calculated by the GPS positioning data is used as the true value. Combined with the frequency options and the corresponding step number options, the step length calculation parameters and the step number calculation parameters are determined by fitting, so that each parameter conforms to the user's actual walking characteristics, and the user does not need to provide privacy data such as height. This not only improves the indoor navigation accuracy based on PDR, but also when outdoor walking data continues to be obtained, the PDR parameters can be continuously updated based on more walking data, so that the personalized PDR parameters are gradually optimized and iterated.

[0101] In practical applications, after determining the above-mentioned step length calculation parameters and step count calculation parameters, when the user enters the room and walks, because the initial position is known and the user's step length can be determined by the step length calculation parameters, after collecting the vertical acceleration data, the user's gait transformation can be determined by the above-mentioned step count calculation parameters to calculate the accurate step count. Combined with the above-mentioned formula (1), the user's real-time position can be accurately inferred.

[0102] Optionally, in one embodiment, before the above step S200, step S201 is further included:

[0103] The acceleration data is converted from the device coordinate system to the north-east coordinate system, and the gravity acceleration in the vertical direction is subtracted.

[0104] In this embodiment, because the GPS positioning data is based on the North-East Earth coordinate system, the acceleration data obtained by the sensor is converted from the device coordinate system to the North-East Earth coordinate system, making the data reference system unified, which is convenient for subsequent calculations. Since the gravitational acceleration to which the user is subjected remains unchanged during the step length, the vertical gravitational acceleration is subtracted, that is, the vertical acceleration value of the user's walking obtained by monitoring is subtracted from the gravitational acceleration value, so that the theoretical average value of the acceleration data used in the parameter estimation of the pedestrian dead reckoning model is 0, that is, theoretically about 0 m / s. 2 Symmetry makes it easier to identify peaks and troughs, and thus to accurately distinguish gait changes.

[0105] Optionally, in one embodiment, for each outdoor walking path, step S300 includes steps S301 to S303:

[0106] Step S301: Determine a DC component and N amplitude options, and a frequency option corresponding to each amplitude option, based on the frequency domain modulus; wherein the N amplitude options are the first N larger amplitudes in the frequency domain modulus.

[0107] In the above step S301, in formula (3), when f=0, the amplitude is the DC component A D , which corresponds to the amplitude when the frequency is 0 in the frequency domain modulus, thereby determining the DC component; and the vertical acceleration periodic change A caused by walking step sin 2πf step t should be the vertical acceleration Therefore, we can take the first N larger amplitudes and corresponding frequencies of f>0 as the amplitude alternative A of the periodic transformation of vertical acceleration caused by walking. i and frequency alternative f i , thus determining the alternative (A i , f i ), i=1,2,3…N。

[0108] Step S302: Determine N step calculation parameter options based on the N amplitude options and the corresponding N frequency options. The step calculation parameter options include peak and trough thresholds, cycle upper limit values, and cycle lower limit values.

[0109] In the above step S302, for each amplitude option A i and the corresponding frequency alternative f i , can be achieved through A i The first adjustment coefficient sets the peak and valley thresholds, and thei The second adjustment coefficient sets the upper limit and lower limit of the cycle, thereby determining the corresponding step calculation parameter options (Threshold peak|buttom , T max , T min ) i , i=1, 2, 3…N; wherein, the above-mentioned first adjustment coefficient and the second adjustment coefficient need to be adjusted and determined according to the situation of the electronic equipment, and the setting of the first adjustment coefficient can allow the fluctuation of the acceleration amplitude during the user's walking process, avoiding the omission of gait monitoring and causing step calculation deviation.

[0110] Because each outdoor walking path corresponds to N amplitude options A i and the corresponding frequency alternative f i , so each outdoor walking path corresponds to N step calculation parameter options.

[0111] Step S303: Determine N step number options based on the DC component, the acceleration data, and the N step number calculation parameter options.

[0112] In the step S303, the acceleration data is adjusted using the DC component so that the vertical acceleration is about 0 m / s 2 Symmetry is used to avoid errors in stride length calculation caused by errors in detection of the start, peak, and trough of the gait. Then, for each step calculation parameter option, in the adjusted vertical acceleration data, when the acceleration is detected to be equal to 0, the gait is Start, indicating that the stride length detection begins; when the acceleration is greater than the threshold peak / buttom When the acceleration is detected to be less than -threshold peak / buttom , the gait changes to Buttom; when the acceleration is detected to be 0 again, the gait changes to Start. If the time difference between the two Starts is t∈[T min , T max ], the step count is increased by 1 until the analysis of the entire outdoor walking path is completed, so that a step count option can be determined; because each outdoor walking path corresponds to N step count calculation parameter options, each step count calculation parameter option can determine a step count option, so for N outdoor walking paths, N step count options can be determined.

[0113] In this embodiment, considering that the vertical acceleration periodic change caused by the user walking should be the vertical acceleration The main components of the acceleration data are obtained by calculating the frequency domain modulus of f>0, and the first N larger amplitudes and corresponding frequencies are selected as the acceleration amplitude and step frequency corresponding to the user's walking. Then, N step calculation parameter options are determined, that is, the possible N step number options can be calculated, and the preliminary determination of the walking step number can be quickly achieved.

[0114] Optionally, in a specific implementation, the above step S301 includes steps S3011 to S3012.

[0115] Step S3011: multiply the amplitude candidate by (1-first adjustment coefficient) to determine the peak and trough thresholds, wherein the first adjustment coefficient is greater than 0 and less than 1.

[0116] In the above step S3011, for each amplitude option A i , through A i Set the peak and trough threshold i =(1-p1)·A i , where p1 is the first adjustment coefficient mentioned above. The setting of the first adjustment coefficient can allow fluctuations in the acceleration amplitude during the user's walking process, avoid omissions in gait monitoring, and cause deviations in step calculation.

[0117] In practical applications, the first adjustment coefficient also needs to ensure that the Threshold i >A i+1 , that is, the above-mentioned first adjustment coefficient and amplitude option A i The determined peak and trough thresholds should be larger than the amplitudes of other amplitude options to avoid excessive adjustment, which may lead to peak signals in different cycles being judged as peak signals in the same cycle during gait analysis, resulting in errors in the detection of each start, peak and trough gait, and thus errors in step length calculation.

[0118] Step S3012: Divide (1+second adjustment coefficient) by the frequency alternative to determine the upper limit of the period, and divide (1-second adjustment coefficient) by the frequency alternative to determine the lower limit of the period; wherein the second adjustment coefficient is greater than 0 and less than 1.

[0119] In the above step S3012, by f i Set the cycle upper limit value T max =(1+p2) / f i And the cycle lower limit T min =(1-p2) / f i , f i This is the second adjustment coefficient mentioned above.

[0120] Through the above implementation, N step count calculation threshold options (Threshold peak|buttom , T max , T min ) i For each step calculation threshold option, the step calculation module can calculate its corresponding step option N step，i =SD(Threshold peak|buttom , T max , T min ) i , i=1,2,3…N。

[0121] Optionally, in a specific implementation, the above step S303 includes steps S3031 to S3032.

[0122] Step S3031: Subtract the DC component from the acceleration data to obtain adjusted acceleration data.

[0123] In the above step S3031, the vertical acceleration data corresponding to each outdoor walking path is subtracted from the DC component determined in step S301, so that the adjusted acceleration data can be obtained. The adjusted acceleration data is about 0 m / s 2 Symmetry avoids errors in stride length calculation due to errors in detecting the start, peak, and trough of gait.

[0124] Step S3032: Determine N step number options based on the adjusted acceleration data and the N step number calculation parameter options.

[0125] In the above step S3032, gait detection is performed using the adjusted acceleration data in combination with the step count calculation parameter options to determine the number of steps corresponding to the path as a step count option; for N outdoor walking paths, N step count options can be determined.

[0126] Optionally, in one embodiment, the above step S500 includes steps S501 to S502.

[0127] Step S501: Take any one of the multiple outdoor walking paths as a target outdoor walking path, select one frequency option from N frequency options corresponding to the target outdoor walking path in sequence as a frequency option, and determine the step number option corresponding to the target frequency option in each outdoor walking path.

[0128] In the above step S501, because the frequency options are the possible step frequencies of the user when walking outdoors, the frequency options corresponding to the acceleration data of each of the above outdoor walking paths include the target frequency option, and the frequency options correspond to the step number options one-to-one, so the step number option corresponding to the target frequency option can also be found.

[0129] Step S502: Determine a step length calculation parameter option for a linear prediction model for calculating step length based on step frequency according to the target frequency option, the path length corresponding to each outdoor walking path, and the step number option corresponding to the target frequency option.

[0130] In the above step S502, the linear prediction model for calculating the step length based on the step frequency is set to step length = w·f step +v, one of the N step number options determined by each outdoor walking path of the user is input into the above linear prediction model as the target step number option, and the frequency option corresponding to the target step number option is used as the step frequency f step , and taking the length of the outdoor walking path as the true value, a relationship as shown in formula (5) can be formed for each outdoor path and each step number option:

[0131]

[0132] Where D(j, j-1) is the distance formula between the starting point of the outdoor walking path and the two adjacent GPS coordinates of the terminal, SD (Threshold peak|buttom , T max , T min ) is the above-mentioned step number alternative, so the step frequency f in the acceleration data of multiple outdoor walking paths is combined step The corresponding step number options, that is, the corresponding (w, v) mentioned above can be calculated, thereby determining the step length calculation parameter options of the linear prediction model based on the step frequency calculation step length.

[0133] Step S503: Determine the step length calculation parameter and the step count calculation parameter for the user from the multiple step length calculation parameter options and the corresponding multiple step count calculation parameter options, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

[0134] In the above step S503, the PDR personalized parameter options ((w, v), (Threshold peak|buttom , T max， T min )) iAfter that, by calculating that the difference between the product of the calculation step length and the number of calculation steps and the path length is within the preset difference range, it is possible to select appropriate PDR personalized parameters from the personalized parameter options, and achieve fitting accuracy adjustment of the PDR personalized parameters, thereby improving the indoor navigation accuracy based on PDR.

[0135] In addition, as more GPS positioning data and vertical acceleration data corresponding to the outdoor walking path are obtained, the PDR parameters can be continuously updated based on more measurement data through formula (5), so that the PDR personalized parameters are gradually optimized and iterated.

[0136] See also Figure 6 , shows a parameter estimation execution flow chart of the pedestrian dead reckoning model in an embodiment of the present application.

[0137] like Figure 6 As shown, in step S601, it is first determined whether the current environment is outdoor. If it is outdoor, the process proceeds to step S602, otherwise it proceeds to step S608;

[0138] In step S602, it is determined whether the electronic device has fitted and determined the personalized PDR parameters for the current user. If so, the process proceeds to step S603; otherwise, the process proceeds to steps S604 and S605.

[0139] In step S603, GPS positioning data and vertical acceleration data generated when the user walks outdoors in the current environment are obtained, and personalized PDR parameters are optimized and adjusted;

[0140] In step S604, the GPS data is processed to remove the acceleration of gravity;

[0141] In step S605, the acceleration data of the outdoor walking path is processed using a fast Fourier transform to obtain a frequency domain modulus of the acceleration data, thereby determining a step count calculation parameter;

[0142] In step S606, based on the processed GPS positioning data and the corresponding step calculation parameters, step length detection parameters are estimated to determine the step length calculation parameters;

[0143] In step S607, a personalized PDR parameter for the user is determined based on the step length calculation parameter and the step number calculation parameter.

[0144] In step S608, the determined personalized PDR parameters are combined with the accurate GPS positioning coordinates of the user when entering from outdoors and the vertical acceleration data generated when walking indoors, so that the coordinates of the user when walking indoors can be calculated in real time, thereby realizing real-time positioning of the user's indoor position.

[0145] Another object of the present invention is to provide a bus route details display device, which is applied to electronic equipment, wherein, see Figure 7 , Figure 7 A schematic diagram of the structure of a parameter estimation device for a pedestrian dead reckoning model proposed in an embodiment of the present invention is shown. The device includes:

[0146] An acquisition module 71 is used to obtain GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user;

[0147] a processing module 72 for processing the acceleration data of each outdoor walking path using a fast Fourier transform to obtain a frequency domain modulus of the acceleration data;

[0148] A first determining module 73 is configured to determine a frequency candidate and a step number candidate corresponding to the frequency candidate according to the frequency domain modulus and the acceleration data;

[0149] A second determining module 74 is configured to determine the length of each outdoor walking path according to the GPS positioning data;

[0150] a third determining module 75 for fitting and determining step length calculation parameters and step count calculation parameters based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step count options corresponding to each of the outdoor walking paths, and the frequency options corresponding to the step count options, so that for each of the outdoor walking paths, a difference between the product of the calculated step length and the calculated step count and the path length is within a preset difference range;

[0151] Among them, the step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter.

[0152] In the device described in the embodiment of the present invention, the vertical acceleration data corresponding to multiple outdoor walking paths of the user are processed using fast Fourier transform to obtain the frequency domain modulus of the acceleration data, and then the personalized frequency options and corresponding step number options of the PDR are obtained through fitting or machine learning methods; then the path length calculated by GPS positioning data is used as the true value, combined with the frequency options and the corresponding step number options, the step length calculation parameters and the step number calculation parameters are determined by fitting, so that each parameter conforms to the actual walking characteristics of the user, and the user does not need to provide privacy data such as height. This not only improves the indoor navigation accuracy based on PDR, but also when outdoor walking data continues to be obtained, the PDR parameters can be continuously updated based on more walking data, so that the personalized PDR parameters are gradually optimized and iterated.

[0153] Optionally, in the device, the first determining module 73 includes:

[0154] a first determining unit configured to determine, for each of the outdoor walking paths, a DC component and N amplitude candidate options, and a frequency candidate corresponding to each of the amplitude candidate options based on the frequency domain modulus value; wherein the N amplitude candidate options are the first N larger amplitudes in the frequency domain modulus value;

[0155] a second determining unit, configured to determine N step number calculation parameter options based on the N amplitude options and the corresponding N frequency options, wherein the step number calculation parameter options include a peak and trough threshold, a cycle upper limit, and a cycle lower limit;

[0156] The third determining unit is configured to determine N step number options according to the DC component, the acceleration data, and the N step number calculation parameter options.

[0157] Optionally, in the device, the third determining module 75 includes:

[0158] a fourth determining unit, configured to take any one of the plurality of outdoor walking paths as a target outdoor walking path, sequentially select one from the N frequency alternatives corresponding to the target outdoor walking path as a frequency alternative, and determine a step number alternative corresponding to the target frequency alternative in each of the outdoor walking paths;

[0159] a fifth determining unit, configured to determine, according to the target frequency candidate, the path lengths corresponding to the outdoor walking paths, and the step number candidate corresponding to the target frequency candidate, a step length calculation parameter candidate for a linear prediction model for calculating step length based on cadence;

[0160] The sixth determination unit is used to determine the step length calculation parameter and the step count calculation parameter for the user from the multiple step length calculation parameter options and the corresponding multiple step count calculation parameter options, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

[0161] Optionally, in the device, the second determining unit includes:

[0162] a first determining subunit, configured to determine, for any one of the N amplitude candidate options, a peak / valley threshold by multiplying the amplitude candidate option by (1-a first adjustment coefficient), wherein the first adjustment coefficient is greater than 0 and less than 1;

[0163] The second determination subunit is used to determine the upper limit value of the period by dividing (1+the second adjustment coefficient) by the frequency option for any one of the N amplitude option options, and to determine the lower limit value of the period by dividing (1-the second adjustment coefficient) by the frequency option; wherein the second adjustment coefficient is greater than 0 and less than 1.

[0164] Optionally, in the device, the third determining unit includes:

[0165] an adjustment subunit, configured to subtract the DC component from the acceleration data to obtain adjusted acceleration data;

[0166] The third determining subunit is configured to determine N step number options based on the adjusted acceleration data and the N step number calculation parameter options.

[0167] Optionally, the device further comprises:

[0168] The pre-processing module is used to convert the acceleration data from the device coordinate system to the north-east coordinate system and subtract the gravity acceleration in the vertical direction before processing the acceleration data of each outdoor walking path using the fast Fourier transform to obtain the frequency domain modulus value of the vertical acceleration.

[0169] Yet another object of the present invention is to provide a storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor and executing the parameter estimation method of the pedestrian dead reckoning model as described above.

[0170] Another object of the present invention is to provide an electronic device, comprising:

[0171] a processor adapted to implement the instructions; and

[0172] A storage medium is adapted to store a plurality of instructions, wherein the instructions are adapted to be loaded by a processor and executed by the parameter estimation method of the pedestrian dead reckoning model as described above.

[0173] Yet another object of the present invention is to provide a vehicle, wherein the vehicle includes the parameter estimation device of the pedestrian dead reckoning model as described above.

[0174] The storage medium, electronic device, and vehicle have the same advantages as the parameter estimation method and device of the above-mentioned pedestrian dead reckoning model over the prior art, and will not be described in detail here.

[0175] In summary, the parameter estimation method, device, storage medium and electronic device of the pedestrian dead reckoning model provided in the present application use fast Fourier transform to process the vertical acceleration data corresponding to multiple outdoor walking paths of the user, obtain the frequency domain modulus of the acceleration data, and then obtain the PDR personalized frequency options and corresponding step number options through fitting or machine learning methods; then, the path length calculated by GPS positioning data is used as the true value, combined with the frequency options and the corresponding step number options, the step length calculation parameters and the step number calculation parameters are determined by fitting, so that each parameter conforms to the actual walking characteristics of the user, and the user does not need to provide privacy data such as height. This not only improves the indoor navigation accuracy based on PDR, but also when outdoor walking data continues to be obtained, the PDR parameters can be continuously updated based on more walking data, so that the PDR personalized parameters are gradually optimized and iterated.

[0176] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0177] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatus, or computer program products. Thus, embodiments of the present invention may take the form of a fully hardware embodiment, a fully software embodiment, or an embodiment combining software and hardware. Furthermore, embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] In a typical configuration, the computer device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory. Memory may include non-permanent storage in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium. Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented using any method or technology to store information. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include non-transitory computer-readable media such as modulated data signals and carrier waves.

[0179] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the process in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0180] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0181] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0182] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic creative concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0183] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0184] The above is a detailed introduction to a bus route details display method, device, storage medium and electronic device provided by the present invention. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for estimating parameters of a pedestrian dead reckoning model, applied to electronic equipment, characterized in that: The method comprises: Obtain GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user; Processing the acceleration data of each outdoor walking path using a fast Fourier transform to obtain a frequency domain modulus value of the acceleration data; Determining a frequency candidate and a step number candidate corresponding to the frequency candidate according to the frequency domain modulus and the acceleration data; Determining the length of each outdoor walking path according to the GPS positioning data; Determining, by fitting, a step length calculation parameter and a step number calculation parameter based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step number alternative corresponding to each of the outdoor walking paths, and the frequency alternative corresponding to the step number alternative, such that, for each of the outdoor walking paths, a difference between a product of a calculated step length and a calculated number of steps and the path length is within a preset difference range; The step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter; for each of the outdoor walking paths, the step of determining the frequency alternatives and the step number alternatives corresponding to the frequency alternatives based on the frequency domain modulus and the acceleration data includes: Determining, based on the frequency domain modulus, a DC component and N amplitude candidate options, and a frequency candidate option corresponding to each of the amplitude candidate options; wherein the N amplitude candidate options are the first N larger amplitudes in the frequency domain modulus; Determine N step number calculation parameter options according to the N amplitude options and the corresponding N frequency options, wherein the step number calculation parameter options include a peak and trough threshold, a cycle upper limit, and a cycle lower limit; Determining N step number options according to the DC component, the acceleration data, and the N step number calculation parameter options; For any one of the N amplitude alternatives, the step of determining N step number calculation parameter alternatives based on the N amplitude alternatives and the corresponding N frequency alternatives includes: Determine a peak and trough threshold by multiplying the amplitude candidate by (1-first adjustment coefficient), wherein the first adjustment coefficient is greater than 0 and less than 1; The upper limit of the period is determined by dividing (1+the second adjustment coefficient) by the frequency alternative, and the lower limit of the period is determined by dividing (1-the second adjustment coefficient) by the frequency alternative; wherein the second adjustment coefficient is greater than 0 and less than 1.

2. The parameter estimation method according to claim 1, wherein The step of determining a step length calculation parameter and a step count calculation parameter based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step count options corresponding to each of the outdoor walking paths, and the frequency options corresponding to the step count options, comprises: Taking any one of the multiple outdoor walking paths as a target outdoor walking path, sequentially selecting one from the N frequency alternatives corresponding to the target outdoor walking path as a frequency alternative, and determining a step number alternative corresponding to the target frequency alternative in each of the outdoor walking paths; According to the target frequency alternatives, the path lengths corresponding to each of the outdoor walking paths, and the step number alternatives corresponding to the target frequency alternatives, the step length calculation parameter alternatives of the linear prediction model based on step frequency calculation are determined; from the multiple step length calculation parameter alternatives and the corresponding multiple step number calculation parameter alternatives, the step length calculation parameters and step number calculation parameters for the user are determined, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated step number and the path length is within a preset difference range.

3. The parameter estimation method according to claim 1, wherein The step of determining N step number options based on the DC component, the acceleration data, and the N step number calculation parameter options includes: subtracting the DC component from the acceleration data to obtain adjusted acceleration data; N step number options are determined based on the adjusted acceleration data and the N step number calculation parameter options.

4. The parameter estimation method according to claim 1, wherein Before the step of processing the acceleration data of each outdoor walking path by using fast Fourier transform to obtain the frequency domain modulus value of the vertical acceleration, the method further includes: The acceleration data is converted from the device coordinate system to the north-east coordinate system, and the gravity acceleration in the vertical direction is subtracted.

5. A parameter estimation device for a pedestrian dead reckoning model, applied to electronic equipment, characterized in that: The device includes: an acquisition module for acquiring GPS positioning data and vertical acceleration data corresponding to multiple outdoor walking paths of the user; a processing module, configured to process the acceleration data of each outdoor walking path using a fast Fourier transform to obtain a frequency domain modulus of the acceleration data; A first determining module is configured to determine a frequency candidate and a step number candidate corresponding to the frequency candidate according to the frequency domain modulus and the acceleration data; A second determining module is used to determine the path length of each of the outdoor walking paths according to the GPS positioning data; a third determining module, configured to determine, by fitting, a step length calculation parameter and a step number calculation parameter based on a plurality of path lengths corresponding to the plurality of outdoor walking paths, the step number alternative corresponding to each of the outdoor walking paths, and the frequency alternative corresponding to the step number alternative, such that, for each of the outdoor walking paths, a difference between a product of a calculated step length and a calculated number of steps and the path length is within a preset difference range; The step length calculation parameter is a parameter of a linear prediction model for calculating the step length based on the step frequency, the calculated number of steps is the number of steps determined based on the step number calculation parameter, and the calculated step length is the step length determined based on the step length calculation parameter and the step frequency corresponding to the step length calculation parameter; the first determination module includes: a first determining unit configured to determine, for each of the outdoor walking paths, a DC component and N amplitude candidate options, and a frequency candidate corresponding to each of the amplitude candidate options based on the frequency domain modulus value; wherein the N amplitude candidate options are the first N larger amplitudes in the frequency domain modulus value; a second determining unit, configured to determine N step number calculation parameter options based on the N amplitude options and the corresponding N frequency options, wherein the step number calculation parameter options include a peak and trough threshold, a cycle upper limit, and a cycle lower limit; a third determining unit, configured to determine N step number options based on the DC component, the acceleration data, and the N step number calculation parameter options; The second determining unit includes: a first determining subunit, configured to determine, for any one of the N amplitude candidate options, a peak / valley threshold by multiplying the amplitude candidate option by (1-a first adjustment coefficient), wherein the first adjustment coefficient is greater than 0 and less than 1; The second determination subunit is used to determine the upper limit value of the period by dividing (1+the second adjustment coefficient) by the frequency option for any one of the N amplitude option options, and to determine the lower limit value of the period by dividing (1-the second adjustment coefficient) by the frequency option; wherein the second adjustment coefficient is greater than 0 and less than 1.

6. The device according to claim 5, characterized in that The third determining module includes: a fourth determining unit, configured to take any one of the plurality of outdoor walking paths as a target outdoor walking path, sequentially select one from the N frequency alternatives corresponding to the target outdoor walking path as a frequency alternative, and determine a step number alternative corresponding to the target frequency alternative in each of the outdoor walking paths; a fifth determining unit, configured to determine, according to the target frequency candidate, the path lengths corresponding to the outdoor walking paths, and the step number candidate corresponding to the target frequency candidate, a step length calculation parameter candidate for a linear prediction model for calculating step length based on cadence; The sixth determination unit is used to determine the step length calculation parameter and the step count calculation parameter for the user from the multiple step length calculation parameter options and the corresponding multiple step count calculation parameter options, so that for each of the outdoor walking paths, the difference between the product of the calculated step length and the calculated number of steps and the path length is within a preset difference range.

7. The device according to claim 5, characterized in that The third determining unit includes: an adjustment subunit, configured to subtract the DC component from the acceleration data to obtain adjusted acceleration data; The third determining subunit is configured to determine N step number options based on the adjusted acceleration data and the N step number calculation parameter options.

8. The device according to claim 5, characterized in that The device further comprises: The pre-processing module is used to convert the acceleration data from the device coordinate system to the north-east coordinate system and subtract the gravity acceleration in the vertical direction before processing the acceleration data of each outdoor walking path using the fast Fourier transform to obtain the frequency domain modulus value of the vertical acceleration.

9. An electronic device, characterized in that: A parameter estimation device comprising a pedestrian dead reckoning model as claimed in any one of claims 5 to 8.

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