Pedestrian track plotting enhanced positioning method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of positioning, in particular to a pedestrian track plotting enhanced positioning method and device, electronic equipment and a medium, and the method comprises the steps: obtaining acceleration data and angular velocity data, collected by a mobile terminal, of a pedestrian at a plurality of sampling moments; calculating a stride frequency detection result of the pedestrian steps according to the acceleration data to obtain the real steps of the pedestrian; recognizing the motion state of the pedestrian at each sampling moment based on the real step, and recognizing the motion state of the pedestrian at each step in the real step according to the real step frequency detection result; estimating the step length of the pedestrian according to the real stride frequency detection result and the motion state of each step; and estimating the speed of the pedestrian at the current sampling moment according to the step length of the latest complete step of the pedestrian or the motion state of each sampling moment, and calculating the position of the pedestrian at the current sampling moment according to the speed. Therefore, the problem of low pedestrian track plotting precision caused by inaccuracy of stride frequency detection and step length estimation in different motion states in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of positioning technology, and in particular to a method, device, electronic device and medium for pedestrian dead reckoning enhanced positioning. Background Art

[0002] With the development of microelectronic system technology and mobile Internet, smartphones have become an indispensable part of people's daily lives. They not only have powerful computing capabilities, but also integrate a variety of sensors, such as accelerometers, gyroscopes, magnetometers, etc., to achieve indoor positioning. PDR (Pedestrian Dead Reckoning), as an important indoor positioning technology, mainly relies on the gait characteristics of pedestrians to estimate their movement path, usually including three key steps: step detection, step length estimation and heading estimation.

[0003] However, the accuracy of PDR positioning is greatly affected by the accuracy of gait detection, stride length estimation, and heading estimation. Most existing cadence detection methods are very sensitive to the redundant movements of pedestrians when walking, resulting in low cadence detection accuracy. In addition, the estimation performance of traditional stride length models varies in different motion scenarios, especially in challenging motion scenarios such as turning and U-turning, the accuracy drops significantly. Summary of the invention

[0004] The present application provides a pedestrian dead reckoning enhanced positioning method, device, electronic device and medium to solve the problems in the related art of low pedestrian dead reckoning accuracy caused by inaccurate step frequency detection and step length estimation under different motion states.

[0005] The first aspect of the present application provides a method for enhanced positioning by dead reckoning of pedestrians, comprising the following steps: acquiring acceleration data and angular velocity data generated by an accelerometer and a gyroscope of a pedestrian at multiple sampling moments collected by a mobile terminal; calculating a real cadence detection result of the pedestrian's steps based on the acceleration data, and identifying the real steps of the pedestrian at multiple sampling moments based on the real cadence detection result; identifying the motion state of the pedestrian at each sampling moment based on the target angular velocity data and target acceleration data of the real steps of the pedestrian at each sampling moment, and identifying the motion state of each step of the pedestrian within the real step based on the real cadence detection result; estimating the pedestrian's step length based on the real cadence detection result and the motion state of each step; estimating the pedestrian's speed at the current sampling moment based on the length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and calculating the pedestrian's position at the current sampling moment based on the pedestrian's speed at the current sampling moment and the pedestrian's track.

[0006] Optionally, the real pace of the pedestrian at multiple sampling moments is identified based on the real cadence detection results, including: identifying the zero point and the peak point in the real cadence detection results, and determining the pace candidates based on the zero point and the peak point; calculating the frequency domain characteristics and time domain characteristics of the gait of the pace candidates, and eliminating pseudo steps in the pace candidates based on the frequency domain characteristics and the time domain characteristics to obtain the real pace.

[0007] Optionally, the motion state includes: a stationary state, a straight state, a turning state, and a U-turn state. The motion state is divided into two layers, namely, the motion state at each sampling moment and the motion state at each step.

[0008] The motion state S at each sampling moment t The calculation formula is:

[0009]

[0010] Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std Indicates the acceleration standard deviation threshold, Th st Indicates the threshold for transition between straight and turning states, Th tta Indicates the threshold for transition between turning and U-turn states;

[0011] The motion state of each step is comprehensively judged based on the motion state at all times within the real detection step. The motion state S of each step step The calculation formula is:

[0012]

[0013] Among them, Num(S t =i) (i = stationary, straight, turning, U-turn) the number of states within one step, Th num is the U-turn state number threshold.

[0014] Optionally, the step length of the pedestrian is estimated based on the actual cadence detection result and the motion state of each step, including: the actual cadence detection result is input into a step length estimation model, and the step length estimation model outputs the estimated step length of the pedestrian at the current moment; the angular change of each step of the pedestrian is estimated based on the motion state of each step of the pedestrian at the current sampling moment within the actual pace and the motion state at the start and end moments of the cadence; the step length of the pedestrian is estimated based on the motion state of each step, the angular change of each step and the estimated step length.

[0015] Optionally, estimating the speed of the pedestrian at the current sampling moment according to the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment includes: when the end moment of a new step is detected and the step length of the pedestrian's most recent complete step at the current moment is estimated, then estimating the speed of the pedestrian at the current sampling moment according to the step length of the pedestrian's most recent complete step, the calculation formula is:

[0016]

[0017] in, Indicates the step length of the most recent complete step calculated at the end of the step, T n The step time of the new step detected.

[0018] The state sliding window is used to detect the transition label of the motion state at each sampling moment. When the end of the step is not detected, the speed of the pedestrian at the current sampling moment is estimated based on the transition label and the motion state of the pedestrian at the current moment. The calculation formula is:

[0019]

[0020] Among them, S t represents the motion state at time t, ST represents the transition label between the straight and turning states, α, β are the set attenuation coefficients, γ is the set growth coefficient, and v t-1 is the speed at time t-1. According to recursion, when t-1 is exactly the end moment of the step, it can be estimated by the step length.

[0021] Optionally, the position of the pedestrian at the current sampling moment is calculated according to the speed at the current sampling moment, and the calculation formula is:

[0022]

[0023] Among them, x t ,y t represents the pedestrian position at sampling time t, x t+1 ,y t+1 represents the pedestrian position at sampling time t+1, v t represents the speed at sampling time t, Δt represents the time interval between time t+1 and time t, θ t Indicates the heading at time t.

[0024] The second aspect of the present application provides a pedestrian track calculation and enhanced positioning device, including: an acquisition module, which is used to obtain acceleration data and angular velocity data generated by an accelerometer and a gyroscope of a pedestrian collected by a mobile terminal at multiple sampling moments; a calculation module, which calculates the real step frequency detection result of the pedestrian's steps according to the acceleration data, and identifies the real steps of the pedestrian at multiple sampling moments according to the real step frequency detection result; an identification module, which is used to identify the motion state of the pedestrian at each sampling moment based on the target angular velocity data and target acceleration data of the real step of the pedestrian at each sampling moment, and identify the motion state of each step of the pedestrian within the real step according to the real step frequency detection result; an estimation module, which is used to estimate the step length of the pedestrian according to the real step frequency detection result and the motion state of each step; a calculation module, which is used to estimate the speed of the pedestrian at the current sampling moment according to the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and calculate the position of the pedestrian at the current sampling moment according to the speed of the pedestrian at the current sampling moment and the pedestrian's track.

[0025] Optionally, the calculation module is further used to identify zero points and peak points in the cadence detection results, and determine the step candidates based on the zero points and peak points; calculate the frequency domain characteristics and time domain characteristics of the gait of the step candidates, eliminate pseudo steps in the step candidates based on the frequency domain characteristics and time domain characteristics, and obtain the real steps.

[0026] Optionally, the recognition module is further used to recognize two levels of motion states, namely, the motion state at each sampling moment and the motion state at each step. t The calculation formula is:

[0027]

[0028] Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std Indicates the acceleration standard deviation threshold, Th st Indicates the threshold for transition between straight and turning states, Th tta Indicates the threshold for transition between turning and U-turn states;

[0029] The motion state of each step is comprehensively judged based on the motion state at all times within the real detection step. The motion state S of each step step The calculation formula is:

[0030]

[0031] Among them, Num(S t =i) (i = stationary, straight, turning, U-turn) the number of states within one step, Th num is the U-turn state number threshold.

[0032] Optionally, the estimation module is further used to input the real cadence detection results into a stride length estimation model, and the stride length estimation model outputs the estimated stride length of the pedestrian at the current moment; estimates the angular change of each step of the pedestrian based on the motion state of each step of the pedestrian at the current sampling moment within the real pace and the motion state at the start and end moments of the cadence; and estimates the step length of the pedestrian based on the motion state of each step, the angular change of each step and the estimated stride length.

[0033] Optionally, the estimation module is further used to estimate the speed of the pedestrian at the current sampling time according to the length of the pedestrian's most recent complete step when the end time of a new step is detected and the length of the most recent complete step is estimated. The calculation formula is:

[0034]

[0035] in, Indicates the step length of the most recent complete step calculated at the end of the step, T n The step time of the new step detected.

[0036] The state sliding window is used to detect the transition label of the motion state at each sampling moment. When the end of the step is not detected, the speed of the pedestrian at the current sampling moment is estimated based on the transition label and the motion state of the pedestrian at each sampling moment. The calculation formula is:

[0037]

[0038] Among them, S t represents the motion state at time t, ST represents the transition label between the straight and turning states, α, β are the set attenuation coefficients, γ is the set growth coefficient, and v t-1 is the speed at time t-1. According to recursion, when t-1 is exactly the end moment of the step, it can be estimated by the step length.

[0039] Optionally, the position of the pedestrian at the current sampling moment is calculated according to the speed at the current sampling moment, and the calculation formula is:

[0040]

[0041] Among them, x t ,y t represents the pedestrian position at sampling time t, x t+1 ,y t+1 represents the pedestrian position at sampling time t+1, v t represents the speed at sampling time t, Δt represents the time interval between time t+1 and time t, θ t represents the heading at time t.

[0042] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pedestrian track estimation enhanced positioning method as described in the above embodiment.

[0043] The fourth aspect of the present application provides a computer-readable storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed, it is used to implement the pedestrian track estimation enhanced positioning method as described in the above embodiment.

[0044] Therefore, this application has at least the following beneficial effects:

[0045] The embodiment of the present application can determine the real pace of the pedestrian based on the acceleration data of the pedestrian at multiple sampling moments, and identify the motion state of the pedestrian at the current sampling moment and the motion state of each step through the real pace and acceleration data of the pedestrian at multiple sampling moments, and perform step length estimation on this basis, and constrain the pedestrian speed in combination with the motion state and step length estimation, thereby realizing pedestrian track calculation enhanced positioning and improving positioning accuracy. Thus, the problems of low pedestrian track calculation accuracy caused by inaccurate step frequency detection and step length estimation under different motion states in the related technology are solved.

[0046] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0048] Figure 1 A flowchart of a pedestrian dead reckoning enhanced positioning method provided according to an embodiment of the present application;

[0049] Figure 2 An example diagram of a pedestrian dead reckoning enhanced positioning method provided according to an embodiment of the present application;

[0050] Figure 3 A block diagram of a pedestrian dead reckoning enhanced positioning device provided according to an embodiment of the present application;

[0051] Figure 4 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0053] The following describes the pedestrian dead reckoning enhanced positioning method, device, electronic device and medium of the embodiments of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a pedestrian dead reckoning enhanced positioning method, in which the real pace of the pedestrian is determined based on the acceleration data of the pedestrian at multiple sampling moments, and the motion state of the pedestrian at the current sampling moment and the motion state of each step are identified through the real pace and acceleration data of the pedestrian at multiple sampling moments, and the step length is estimated on this basis, and the pedestrian speed is constrained in combination with the motion state and step length estimation, thereby realizing pedestrian dead reckoning enhanced positioning and improving positioning accuracy. As a result, the problems of low pedestrian dead reckoning accuracy caused by inaccurate step frequency detection and step length estimation under different motion states in the related technology are solved.

[0054] Specifically, Figure 1 A flowchart of a pedestrian dead reckoning enhanced positioning method provided in an embodiment of the present application.

[0055] like Figure 1 As shown, the pedestrian dead reckoning enhanced positioning method includes the following steps:

[0056] In step S101, acceleration data and angular velocity data generated by an accelerometer and a gyroscope of a pedestrian at multiple sampling moments are acquired by a mobile terminal.

[0057] Among them, the mobile terminal can be smart products such as mobile phones and watches.

[0058] The embodiment of the present application can use the built-in three-axis accelerometer of the mobile terminal to collect the acceleration data of pedestrians at multiple sampling times. In the actual implementation process, the embodiment of the present application can combine the built-in three-axis acceleration data of the mobile terminal to obtain the acceleration amplitude result, as shown in Formula 1, and estimate the local gravity acceleration value by analyzing the acceleration data over a period of time in a static state, as shown in Formula 2; remove the gravity component in the original acceleration data in Formula 1 to obtain the acceleration component that only reflects the movement of pedestrians. Then, the sliding window smoothing technology is applied to suppress noise, reduce measurement errors, and obtain smooth acceleration data, as shown in Formula 3.

[0059]

[0060] Where t represents the sampling time, a x,t ,a y,t,a z,t Represents the original measurement value of the three-axis acceleration, a t represents the acceleration amplitude at time t, g represents the gravity acceleration value, N represents the number of samples in the static time, l represents the sliding window length, Indicates the smoothed acceleration amplitude.

[0061] In step S102, the real step frequency detection result of the pedestrian's steps is calculated according to the acceleration data, and the real steps of the pedestrian at multiple sampling moments are identified according to the real step frequency detection result.

[0062] In one embodiment of the present application, the real pace of a pedestrian at multiple sampling times is identified based on the real cadence detection results, including: identifying the zero point and the peak point in the cadence detection results, and determining the pace candidates based on the zero point and the peak point; calculating the frequency domain characteristics and time domain characteristics of the gait of the pace candidates, and eliminating the pseudo steps in the gait based on the frequency domain characteristics and the time domain characteristics to obtain the real pace.

[0063] It can be understood that based on the acceleration data obtained in the above embodiment, zero-crossing detection and peak detection algorithms are used to preliminarily detect the zero points and peak points in the walking cycle, as shown in Formula 4-5, so as to obtain step candidates, and set thresholds to eliminate erroneous peak points and zero points, ensuring that only valid step candidates are retained.

[0064] A peak ={A t |(A t ≥A t-1 )∩(A t ≥A t+1 )} (4)

[0065] A zero ={A t |(A t ×A t-1 ≥0)∩(A t ×A t+1 ≤0)} (5)

[0066] Furthermore, in order to accurately identify the real steps of pedestrians from the step candidates, the embodiments of the present application can combine the periodicity and similarity of the pedestrian's movement steps, and use the STFT (Short-Time Fourier Transform) algorithm to calculate the frequency amplitude of the corresponding steps, as shown in formula 6-7; and use the DTW (Dynamic TimeWarping) algorithm to calculate the similarity of the corresponding steps, as shown in formula 8-10. Through the frequency domain characteristics and time domain characteristics of the gait, pseudo steps are eliminated from the step candidate results to ensure that the selected steps represent the real walking behavior and obtain the real steps.

[0067]

[0068] ω(t)=0.54-0.46cos(2πt / N),0≤t≤N (7)

[0069] Among them, x[t] represents the data at sampling time t, T represents the center time of the sliding window, and STFT(ω,T) represents the frequency domain characteristics of the acceleration data calculated using the short-time Fourier transform algorithm.

[0070]

[0071] D=DTW(N,M) (10)

[0072] Wherein, N represents the smoothed acceleration data within the Nth step, M represents the smoothed acceleration data within the Mth step, and D represents the DTW distance representing the similarity of pedestrian steps calculated using the dynamic time warping algorithm.

[0073] In step S103, based on the target angular velocity data and target acceleration data of the pedestrian's real pace at each sampling moment, the movement state of the pedestrian at each sampling moment is identified, and the movement state of each step of the pedestrian within the real pace is identified according to the real cadence detection result.

[0074] Among them, the motion state includes: stationary state, straight state, turning state and U-turn state. The embodiment of the present application can identify the motion state at each sampling moment based on the smoothed acceleration data and the angular velocity data at each sampling moment, as shown in Formula 11. In order to avoid misjudgment of the state due to unnecessary movements during walking, a state sliding window is used to detect the actual motion state transition. In addition, combined with the actual step frequency detection results detected by the above embodiment, the two-layer motion state of the pedestrian is determined, namely the motion state at each sampling moment and the motion state of each step. Among them, the motion state S at each sampling moment t The calculation formula is:

[0075]

[0076] Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std Indicates the acceleration standard deviation threshold, Th st Indicates the threshold for transition between straight and turning states, Th tta Indicates the threshold for transition between turning and U-turn states;

[0077] The motion state of each step is comprehensively judged based on the motion state at all times within the real detection step. The motion state S of each step step The calculation formula is:

[0078]

[0079] Among them, Num(S t =i) (i = stationary, straight, turning, U-turn) the number of states within one step, Th num is the U-turn state number threshold.

[0080] In step S104, the pedestrian's step length is estimated based on the actual step frequency detection result and the motion state of each step.

[0081] The step length estimation is further adjusted on the step length model according to the motion state of each step. The step length estimation is not available at every moment. Only when the step is detected, that is, when the pedestrian takes a step, a real step is generated, and then a step length is generated.

[0082] In one embodiment of the present application, the step length of a pedestrian is estimated based on the actual cadence detection result and the motion state of each step, including: inputting the actual cadence detection result into a stride length estimation model, and the stride length estimation model outputs the estimated stride length of the pedestrian at the current moment; estimating the angular change of each step of the pedestrian based on the motion state of each step of the pedestrian at the current sampling moment within the actual pace and the motion state at the start and end moments of the cadence; estimating the step length of the pedestrian based on the motion state of each step, the angular change of each step and the estimated stride length.

[0083] It can be understood that the embodiments of the present application can use the step length estimation model to estimate the step length in combination with the step frequency detection results determined in the above embodiments and the motion state corresponding to the actual step, and constrain the estimated step length according to the motion state, as shown in formulas 13-14.

[0084]

[0085] Among them, f k represents the step frequency of the kth step, acc max,k ,acc min,k They represent the maximum and minimum acceleration of the kth step, K represents the empirical constant, S step is the motion state of the kth step, Δφ k represents the angle change in the kth step, Th φ Indicates the set angle change threshold.

[0086] During the actual implementation process, the embodiments of the present application also take into account that angle changes may affect the actual step length (for example, the step length may become shorter when turning), and it is necessary to incorporate angle changes into the step length estimation and combine the angle changes with the preliminary estimated step length obtained by the step length estimation model to further optimize the estimation results and obtain the final step length to improve the accuracy of pedestrian positioning.

[0087] In step S104, the speed of the pedestrian at the current sampling moment is estimated based on the length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and the position of the pedestrian at the current sampling moment is inferred based on the pedestrian's speed at the current sampling moment.

[0088] The embodiment of the present application can constrain the pedestrian's speed by combining the pedestrian's current motion state and the step length estimated by the step length estimation model in the above embodiment, and use a speed-based position estimation model to perform position estimation, as shown in Formula 15, thereby achieving pedestrian track calculation enhanced positioning and improving positioning accuracy.

[0089]

[0090] Among them, x t ,y t represents the plane coordinates at sampling time t, x t+1 ,y t+1 represents the plane coordinates at sampling time t+1, v t represents the speed at sampling time t, Δt represents the sampling time interval, θ t Indicates the heading at time t.

[0091] In one embodiment of the present application, the speed of the pedestrian at the current sampling moment is estimated based on the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, including: when the end moment of a new step is detected and the step length of the most recent complete step is estimated, the speed of the pedestrian at the current sampling moment is estimated based on the step length of the pedestrian's most recent complete step; otherwise, a state sliding window is used to detect a transition label of the motion state at each sampling moment, and the speed of the pedestrian at the current sampling moment is estimated based on the transition label and the motion state of the pedestrian at each sampling moment.

[0092] It is understandable that the process of position estimation in the embodiment of the present application can use two methods to calculate the speed according to different situations, thereby affecting the update of the position. These two methods correspond to the speed estimation when a new step is detected and during a non-step event, specifically:

[0093] When the end of a new step is detected and the length of the most recent complete step is estimated, the speed estimate is calculated directly from the ratio of the step length to the time interval, using the formula:

[0094]

[0095] in, Indicates the step length of the most recent complete step calculated at the end of the step, T n The step time of the new step detected.

[0096] When the step end moment is not detected, the state sliding window is used to detect the transition label of the motion state at each sampling moment, and the speed is adjusted according to the state and state transition label at each sampling moment. In this case, although there is no new step length data, the position can still be recursively updated by adjusting the speed to maintain the continuity and accuracy of the position estimation. The calculation formula is:

[0097]

[0098] Among them, S t represents the motion state at time t, ST represents the transition label between the straight and turning states, α, β are the set attenuation coefficients, γ is the set growth coefficient, and v t-1 is the speed at time t-1. According to recursion, when t-1 is exactly the end moment of the step, it can be estimated by the step length.

[0099] Combine the following Figure 2 The pedestrian dead reckoning enhanced positioning method of the embodiment of the present application is described in detail, including the following steps: first, combining the measurement value of the built-in IMU data of the mobile phone to obtain the acceleration smoothing data after removing the gravity; secondly, based on the zero-crossing detection and peak detection algorithms, and setting the threshold to eliminate the erroneous peak points and zero points, so as to obtain the step candidates, and further using the short-time Fourier transform algorithm and the dynamic time warping algorithm to respectively calculate the frequency domain characteristics and time domain characteristics of the pedestrian motion, eliminate the pseudo steps from the step candidates, and obtain the real steps; then, combining the angular velocity and acceleration data, realize the motion state judgment, and realize the step length estimation based on the motion state constraint; finally, combining the motion scenario knowledge and the pedestrian step length, constraining the speed in the PDR position estimation model, and realizing the PDR robust positioning in different motion scenarios.

[0100] According to the pedestrian dead reckoning enhanced positioning method proposed in the embodiment of the present application, the real pace of the pedestrian is determined based on the acceleration data of the pedestrian at multiple sampling moments, and the motion state of the pedestrian at the current sampling moment is identified through the real pace and acceleration data of the pedestrian at multiple sampling moments, and the step length is estimated on this basis, and the pedestrian speed is constrained in combination with the motion state and step length estimation, thereby realizing pedestrian dead reckoning enhanced positioning and improving positioning accuracy. As a result, the problem of low step frequency detection accuracy in the related art, which leads to poor subsequent positioning accuracy, is solved.

[0101] Next, the pedestrian dead reckoning enhanced positioning device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0102] Figure 3 It is a block diagram of a pedestrian dead reckoning enhanced positioning device according to an embodiment of the present application.

[0103] like Figure 3 As shown, the pedestrian dead reckoning enhanced positioning device 10 includes: an acquisition module 100 , a calculation module 200 , an identification module 300 , an estimation module 400 and a calculation module 500 .

[0104] Among them, the acquisition module 100 is used to obtain the acceleration data and angular velocity data generated by the accelerometer and gyroscope of the pedestrian collected by the mobile terminal at multiple sampling moments; the calculation module 200 calculates the real step frequency detection result of the pedestrian's steps according to the acceleration data, and identifies the real steps of the pedestrian at multiple sampling moments according to the real step frequency detection result; the identification module 300 is used to identify the motion state of the pedestrian at each sampling moment based on the target angular velocity data and target acceleration data of the real step of the pedestrian at each sampling moment, and identify the motion state of each step of the pedestrian within the real step according to the real step frequency detection result; the estimation module 400 is used to estimate the step length of the pedestrian according to the real step frequency detection result and the motion state of each step; the inference module 500 is used to estimate the speed of the pedestrian at the current sampling moment according to the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and infer the position of the pedestrian at the current sampling moment according to the speed of the pedestrian at the current sampling moment and the pedestrian's track.

[0105] In one embodiment of the present application, the calculation module 200 is further used to identify the zero point and the peak point in the real step frequency detection result, and determine the step candidates based on the zero point and the peak point; calculate the frequency domain characteristics and time domain characteristics of the gait of the step candidates, eliminate the pseudo steps in the step candidates based on the frequency domain characteristics and the time domain characteristics, and obtain the real step.

[0106] In one embodiment of the present application, the recognition module 300 is further used to recognize two levels of motion states of pedestrians, namely, the motion state at each sampling moment and the motion state at each step. The motion state categories include: stationary state, straight state, turning state and U-turn state. The motion state S at each sampling moment t The calculation formula is:

[0107]

[0108] Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std Indicates the acceleration standard deviation threshold, Th stIndicates the threshold for transition between straight and turning states, Th tta Indicates the threshold for transition between turning and U-turn states;

[0109] The motion state of each step is comprehensively judged based on the motion state at all times within the real detection step. The motion state S of each step step The calculation formula is:

[0110]

[0111] Among them, Num(S t =i) (i = stationary, straight, turning, U-turn) the number of states within one step, Th num is the U-turn state number threshold.

[0112] In one embodiment of the present application, the estimation module 400 is further used to input the actual cadence detection results into a stride length estimation model, and the stride length estimation model outputs the estimated stride length of the pedestrian at the current moment; estimates the angular change of each step of the pedestrian based on the motion state of each step of the pedestrian at the current sampling moment within the actual pace and the motion state at the start and end moments of the cadence; and estimates the stride length of the pedestrian based on the motion state of each step, the angular change of each step and the estimated stride length.

[0113] In one embodiment of the present application, the calculation module 500 is further used to estimate the speed of the pedestrian at the current sampling moment based on the length of the pedestrian's most recent complete step when the end moment of a new step is detected and the length of the most recent complete step is estimated; otherwise, a state sliding window is used to detect transition labels of the pedestrian's motion state at multiple sampling moments; and the speed of the pedestrian at the current sampling moment is estimated based on the transition labels and the pedestrian's motion state at each sampling moment.

[0114] In one embodiment of the present application, when the end time of a new step is detected and the length of the most recent complete step is estimated, the speed of the pedestrian at the current sampling time is estimated based on the length of the most recent complete step, and the calculation formula is:

[0115]

[0116] in, Indicates the step length of the most recent complete step calculated at the end of the step, T n The step time of the new step detected.

[0117] When the end of the step is not detected, the speed of the pedestrian at the current sampling moment is estimated based on the transition label and the pedestrian's motion state at each sampling moment. The calculation formula is:

[0118]

[0119] Among them, St represents the motion state at time t, ST represents the transition label between the straight and turning states, α, β are the set attenuation coefficients, γ is the set growth coefficient, and v t-1 is the speed at time t-1. According to recursion, when t-1 is exactly the end moment of the step, it can be estimated by the step length.

[0120] The position of the pedestrian at the current sampling time is calculated based on the speed at the current sampling time. The calculation formula is:

[0121]

[0122] Among them, x t ,y t represents the pedestrian position at sampling time t, x t+1 ,y t+1 represents the pedestrian position at sampling time t+1, v t represents the speed at sampling time t, Δt represents the time interval between time t+1 and time t, θ t Indicates the heading at time t.

[0123] It should be noted that the above explanation of the embodiment of the pedestrian dead reckoning enhanced positioning method is also applicable to the pedestrian dead reckoning enhanced positioning device of this embodiment, and will not be repeated here.

[0124] According to the pedestrian dead reckoning enhanced positioning device proposed in the embodiment of the present application, the real pace of the pedestrian is determined based on the acceleration data of the pedestrian at multiple sampling moments, and the motion state of the pedestrian at the current sampling moment and the motion state of each step are identified through the real pace and acceleration data of the pedestrian at multiple sampling moments, and the step length is estimated on this basis, and the pedestrian speed is constrained in combination with the motion state and step length estimation, thereby realizing pedestrian dead reckoning enhanced positioning and improving positioning accuracy. As a result, the problem of low pedestrian dead reckoning accuracy caused by inaccurate step frequency detection and step length estimation under different motion states in the related technology is solved.

[0125] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0126] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0127] When the processor 402 executes the program, the pedestrian dead reckoning enhanced positioning method provided in the above embodiment is implemented.

[0128] Furthermore, the electronic device further comprises:

[0129] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0130] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0131] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0132] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0133] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0134] The processor 402 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0135] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned pedestrian dead reckoning enhanced positioning method.

[0136] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0137] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0138] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0139] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0140] A person skilled in the art may understand that all or part of the steps in the above-mentioned embodiment method may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0141] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A pedestrian dead reckoning enhanced positioning method, characterized in that: The following steps are involved: Acquire acceleration data and angular velocity data generated by an accelerometer and a gyroscope of a pedestrian collected by a mobile terminal at multiple sampling moments; Calculating a real step frequency detection result of the pedestrian's steps according to the acceleration data, and identifying the real steps of the pedestrian at multiple sampling moments according to the real step frequency detection result; Based on the target angular velocity data and the target acceleration data of the pedestrian's real pace at each sampling moment, identifying the motion state of the pedestrian at each sampling moment, and identifying the motion state of each step of the pedestrian within the real pace according to the real step frequency detection result; estimating the pedestrian's step length according to the real step frequency detection result and the motion state of each step; The speed of the pedestrian at the current sampling moment is estimated according to the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and the position of the pedestrian at the current sampling moment is inferred according to the pedestrian's speed and the pedestrian's track at the current sampling moment.

2. The method for enhancing pedestrian dead reckoning positioning according to claim 1, characterized in that: The identifying the real pace of the pedestrian at multiple sampling moments according to the real pace detection result includes: Identifying a zero point and a peak point in the actual cadence detection result, and determining a step candidate according to the zero point and the peak point; The frequency domain features and the time domain features of the gait of the step candidate are calculated, and the pseudo steps in the step candidate are eliminated according to the frequency domain features and the time domain features to obtain the real step.

3. The method for enhancing pedestrian dead reckoning positioning according to claim 1, characterized in that: The motion states include: stationary state, straight-moving state, turning state and U-turn state; The motion state S at each sampling moment t The calculation formula is: Among them, std acc represents the standard deviation of acceleration, ω t represents the angular velocity at time t, Th std Indicates the acceleration standard deviation threshold, Th st Indicates the threshold for transition between straight and turning states, Th tta Indicates the threshold for transition between turning and U-turn states; The motion state of each step is comprehensively judged based on the motion state at all times within the real step. step The calculation formula is: Among them, Num(S t =i) (i = stationary, straight, turning, U-turn) the number of states within one step, Th num is the U-turn state number threshold.

4. The pedestrian dead reckoning enhanced positioning method according to claim 1, characterized in that: The estimating the pedestrian's step length according to the real step frequency detection result and the motion state of each step includes: The actual step frequency detection result is input into a step length estimation model, and the step length estimation model outputs an estimated step length of the pedestrian at a current moment; estimating the angle change of each step of the pedestrian according to the motion state of each step of the pedestrian at the current sampling time within the real pace and the motion state at the starting time and the ending time of the step frequency; The step length of the pedestrian is estimated based on the motion state of each step, the angle change of each step and the estimated step length.

5. The method for enhancing pedestrian dead reckoning positioning according to claim 1, characterized in that: The speed of the pedestrian at the current sampling moment is estimated based on the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, including: When the end time of a new step is detected, the speed of the pedestrian at the current sampling time is estimated according to the step length of the pedestrian's most recent complete step at the current time. The calculation formula is: in, Indicates the step length of the most recent complete step calculated at the end of the step, T n is the step time of the new step detected; The state sliding window is used to detect the transition label of the motion state at each sampling moment. When the step end moment is not detected, the speed of the pedestrian at the current sampling moment is estimated according to the transition label and the motion state of the pedestrian at each moment. The calculation formula is: Among them, S t represents the motion state at time t, ST represents the transition label between the straight and turning states, α, β are the set attenuation coefficients, γ is the set growth coefficient, and v t-1 is the speed at time t-1. When t-1 is the end time of the step, it can be estimated from the step length.

6. The method for enhancing pedestrian dead reckoning positioning according to claim 5, characterized in that: The position of the pedestrian at the current sampling time is calculated based on the speed at the current sampling time, and the calculation formula is: Among them, x t ,y t represents the pedestrian position at sampling time t, x t+1 ,y t+1 represents the pedestrian position at sampling time t+1, v t represents the speed at sampling time t, Δt represents the time interval between time t+1 and time t, θ t Indicates the heading at time t.

7. A pedestrian dead reckoning enhanced positioning device, characterized in that: include: An acquisition module is used to acquire acceleration data and angular velocity data generated by an accelerometer and a gyroscope of a pedestrian collected by a mobile terminal at multiple sampling moments; a calculation module, configured to calculate a real step frequency detection result of the pedestrian's steps according to the acceleration data of the pedestrian at multiple sampling moments, and identify the real step frequency of the pedestrian according to the real step frequency detection result; an identification module, for identifying the motion state of the pedestrian at each sampling moment based on the target angular velocity data and the target acceleration data of the pedestrian's real pace at each sampling moment, and identifying the motion state of each step of the pedestrian within the real pace according to the real step frequency detection result; An estimation module, used for estimating the pedestrian's step length based on the real step frequency detection result and the motion state of each step; The estimation module is used to estimate the speed of the pedestrian at the current sampling moment according to the step length of the pedestrian's most recent complete step at the current moment or the motion state at each sampling moment, and to estimate the position of the pedestrian at the current sampling moment according to the speed of the pedestrian at the current sampling moment.

8. The pedestrian dead reckoning enhanced positioning device according to claim 7, characterized in that: The estimation module is further configured to: The actual step frequency detection result is input into a step length estimation model, and the step length estimation model outputs an estimated step length of the pedestrian at a current moment; estimating a step angle change of the pedestrian according to a step motion state of the pedestrian at a current sampling moment within a real step and a motion state at a starting moment and an ending moment of a step frequency; The step length of the pedestrian is estimated based on the step motion state, the step angle change and the estimated step length.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pedestrian dead reckoning enhanced positioning method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the pedestrian dead reckoning enhanced positioning method according to any one of claims 1 to 6 is implemented.

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