Pedestrian navigation method and system based on intelligent fusion of optical signal and inertia

Abstract

The invention discloses a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. The method comprises the following steps: calculating the distance between a receiving end and a transmitting end according to the positions of a visible light transmitting end and a receiving end and visible light original data collected by the receiving end; calculating the collected inertial data by adopting a pedestrian dead reckoning method to obtain position information of a receiving end; and carrying out tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on a particle filtering algorithm to obtain a predicted pedestrian position. According to the method, the pedestrian dead reckoning positioning result and the visible light ranging information are fused, the defects of a single positioning technology are overcome, the method has good performance on a fusion positioning system of visible light and other signals susceptible to environmental interference and inertial and other high-noise signals, the adverse effect of environmental noise on the fusion system is weakened, and the positioning accuracy is improved. And the migration capability and the adaptive capability of the fusion system are effectively improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the technical field of pedestrian positioning and navigation, and in particular, to a pedestrian navigation method and system based on intelligent fusion of optical signals and inertia. Background Art

[0002] With the expansion of the scope of positioning services into daily life, the demand for indoor location-based services (ILBS) has increased rapidly. The progress of the Internet of Things (IoT), ubiquitous sensing technologies, and smart city infrastructure has provided the necessary hardware and technical support for ILBS. However, due to signal obstruction in indoor environments, the Global Navigation Satellite System (GNSS) has difficulty providing reliable positioning services. To address this issue, researchers have explored various indoor positioning technologies, including absolute positioning technologies based on wireless signals such as WIFI, Bluetooth, audio, and magnetic fields, as well as relative positioning methods based on inertial measurement units (IMUs) and cameras. However, these technologies still pose significant challenges in terms of cost, accuracy, and applicability.

[0003] Visible Light Positioning (VLP) is considered one of the most promising indoor positioning technologies due to its wide infrastructure coverage, low power consumption, high accuracy, and anti-electromagnetic interference capabilities. In most indoor environments, commonly used Light Emitting Diodes (LEDs) can provide centimeter-level positioning accuracy. However, despite years of research on VLP technology, problems such as receiver tilt, signal occlusion, and environmental interference still pose significant challenges to ranging accuracy.

[0004] The Pedestrian Dead Reckoning (PDR) technology based on IMU is a relative positioning method that does not require external infrastructure and is suitable for complex environments with signal constraints. However, the PDR technology has a problem of cumulative error, and long-term operation will cause a significant decline in positioning accuracy.

[0005] In addition, a single positioning technology often has difficulty meeting the positioning requirements in complex indoor environments. Summary of the Invention

[0006] To solve the problems in the prior art, the present invention provides a pedestrian navigation method and system based on the intelligent fusion of optical signals and inertia. By tightly coupling and fusing visible light ranging and PDR positioning, accurate and stable positioning can be achieved in indoor pedestrian navigation scenarios, effectively improving the stability and robustness of the positioning system.

[0007] In a first aspect, an embodiment of the present invention provides a pedestrian navigation method based on the intelligent fusion of optical signals and inertia, including:

[0008] S1. Calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and receiving end and the original visible light data collected by the receiving end.

[0009] S2. Use the pedestrian dead reckoning method to calculate the collected inertial data to obtain the position information of the receiving end.

[0010] S3. Based on the particle filter algorithm, tightly combine and fuse the distance between the receiving end and the transmitting end and the position information of the receiving end to obtain the predicted pedestrian position.

[0011] Optionally, S1 includes an offline calibration step and an online ranging step, where:

[0012] The offline calibration step includes: calibrating the Lambert order M of the transmitting end of the Lambert channel model according to the original visible light data measured by the receiving end at different tilt angles.

[0013] According to the original visible light data measured by the receiving end at different positions, as well as the distance and incident angle between the receiving end and the transmitting end, calibrate the power parameter a and the receiving end half-power angle m of the Lambert channel model.

[0014] The online ranging step includes: the receiving end collects the original visible light data for a period of time, extracts the corresponding received signal strength through fast Fourier transform, and substitutes the received signal strength into the calibrated Lambert channel model to obtain the distances from different transmitting ends to the receiving end.

[0015] Optionally, the calculation formula for the distance from the transmitting end to the receiving end is:

[0016]

[0017] where d is the distance from the receiving end to the transmitting end, h is the height difference from the receiving end to the transmitting end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving end, a is the power parameter of the ranging system, m is the receiving end half-power angle, and M is the Lambert order of the transmitting end.

[0018] Optionally, the state equation for tight combination and fusion in S3 is:

[0019]

[0020] Among them, X k+1 represents the position and heading set of the pedestrian at time k + 1, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian's step length.

[0021] Optionally, the model of the corresponding state error in S3 is:

[0022] δX = [δx δy δz δθ]

[0023] Among them, δX is the error state, δx is the positioning error state on the x-axis, δy is the positioning error state on the y-axis, δz is the positioning error state on the z-axis, and δθ is the heading angle error state.

[0024] Optionally, the measurement model corresponding to S3 is:

[0025] Z k = ||P pdr - ρ|| - d

[0026] Among them, Z k is the measurement error vector; ρ is the coordinate of the transmitting end; P pdr is the position vector updated by the pedestrian dead reckoning; d is the distance between the receiving end and the transmitting end calculated by the calibrated Lambert channel model.

[0027] In a second aspect, an embodiment of the present invention provides a pedestrian navigation system for optical signal and inertial intelligent fusion, including:

[0028] A visible light ranging module, configured to calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and the receiving end and the visible light raw data collected by the receiving end;

[0029] A pedestrian dead reckoning module, configured to perform dead reckoning on the collected inertial data to obtain the position information of the receiving end;

[0030] A fusion module, configured to perform tight combined fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on the particle filter algorithm to obtain the predicted pedestrian position.

[0031] Optionally, the visible light module specifically includes:

[0032] An offline calibration module, configured to calibrate the Lambert order M of the transmitting end of the Lambert channel model according to the visible light raw data measured by the receiving end at different tilt angles;

[0033] According to the original visible light data measured by the receiving end at different positions, as well as the distance and incident angle between the receiving end and the transmitting end, calibrate the power parameter a and the half-power angle m of the receiving end of the Lambert channel model;

[0034] An on-line ranging module is used for the receiving end to collect the original visible light data within a period of time, extract the corresponding received signal strength through fast Fourier transform, and substitute the received signal strength into the calibrated Lambert channel model to obtain the distances from different transmitting ends to the receiving end.

[0035] Optionally, the calculation formula for the distance from the transmitting end to the receiving end is:

[0036]

[0037] where d is the distance from the receiving end to the transmitting end, h is the height difference from the receiving end to the transmitting end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving end, a is the power parameter of the ranging system, m is the half-power angle of the receiving end, and M is the Lambert order of the transmitting end.

[0038] Optionally, the state equation in the fusion module is:

[0039]

[0040] where X k+1 represents the set of the position and heading of the pedestrian at the (k + 1)-th moment, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian's step length.

[0041] Advantages of the present invention:

[0042] 1. When performing visible light ranging, the present invention proposes a visible light ranging model and calibration method considering the inclination angle of the receiving end, solves the problem of large ranging error of the traditional visible light ranging model when the receiving end is tilted, and effectively improves the visible light ranging accuracy in indoor complex scenarios;

[0043] 2. The present invention proposes a visible light / inertial tightly coupled fusion framework based on particle filter, uses the positioning result of PDR as the state quantity of the system, and the visible light ranging information as the observation quantity of the system, and updates the state of the system through particle filter, solves the problems that visible light is easily affected by indoor complex environments and there are cumulative errors in pedestrian dead reckoning, and effectively improves the pedestrian positioning and navigation accuracy in indoor complex scenarios. Description of the Drawings

[0044] Figure 1 is a flowchart of a pedestrian navigation method based on intelligent fusion of optical signals and inertia provided by an embodiment of the present invention;

[0045] Figure 2 It is a framework diagram of a pedestrian navigation system based on the intelligent fusion of optical signals and inertia provided by an embodiment of the present invention;

[0046] Figure 3a It is a comparison diagram of the positioning effects of the present invention and other positioning methods for trajectory 1;

[0047] Figure 3b It is a comparison diagram of the positioning effects of the present invention and other positioning methods for trajectory 2. Detailed implementation manners

[0048] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the present invention, rather than limiting the present invention. In addition, it should be noted that, for the sake of convenience of description, only parts related to the present invention rather than all structures are shown in the drawings.

[0049] Embodiment

[0050] Figure 1 It is a flowchart of a pedestrian navigation method based on the intelligent fusion of optical signals and inertia provided by an embodiment of the present invention, which specifically includes the following steps:

[0051] S1. Calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and the receiving end and the original visible light data collected by the receiving end.

[0052] Among them, the visible light transmitting end is an LED base station. In this embodiment, first, the visible light ranging module converts the received signal strength (RSS) of the optical signal into the distance from the corresponding base station to the receiving end, which is used as the observable quantity of the system to provide constraints for the update of the system state.

[0053] Specifically, the above S1 includes an offline calibration step and an online ranging step. Among them, offline calibration requires collecting the optical signals emitted by the LED base stations and the corresponding position data, and fitting the Lambert channel model parameters that the optical signal transmission conforms to. In the online ranging stage, it is necessary to substitute the received signal strength of the optical signal corresponding to the LED base station into the model with known parameters according to the Lambert model parameters fitted in the offline calibration stage, and calculate the distance from the current receiving end to the LED base station.

[0054] In the offline calibration stage, the Lambert parameters of the LED base station are calibrated as M, m, and a, and the formula of the Lambert model is:

[0055]

[0056] In the formula, Ar is the effective area of the receiver, P ti$P_i$ is the transmission power of the $i$-th transmitter, $d$ is the distance from the transmitter to the receiver, $\theta$ is the angle between the incident light ray and the normal vector of the receiver, $\varphi$ is the angle between the outgoing light ray and the normal vector of the transmitter, $m$ is the half-power angle of the receiver, and $M$ is the Lambert order of the transmitter. Generally, it is assumed that the normal vector of the transmitter is perpendicular to the ground. At this time, Equation (1) can be simplified and rewritten as:

[0057]

[0058] In the formula, $P$ ri is the received signal strength, $d$ is the distance from the receiver to the transmitter, $h$ is the height difference between the receiver and the transmitter, $\theta$ is the angle between the incident light ray and the normal vector of the receiver, $a$ is the power parameter of the ranging system, $m$ is the half-power angle of the receiver, and $M$ is the Lambert order of the transmitter.

[0059] Specifically, the visible light offline calibration module is divided into two stages. In the first stage, the Lambert parameter $M$ is calibrated, and its process is as follows:

[0060] First, directly below the LED base station, keep the position and height of the receiver PD unchanged, and measure the visible light raw data at multiple tilt angles; then, perform a fast Fourier transform on the visible light raw data measured at multiple tilt angles, and extract the RSS from the corresponding frequencies; finally, according to Equation (2), fit the Lambert parameter $M$ through the visible light RSS measured at multiple tilt angles and multiple receiver positions.

[0061] In the second stage of the visible light offline calibration module, the Lambert parameters $a$ and $m$ need to be calibrated, and its process is as follows:

[0062] First, place the receiver PD horizontally at different positions, and measure the visible light raw data at multiple positions; then, obtain the distances and incident angles between the multiple receivers and the LED base station according to the position of the LED base station and the positions of the multiple receiver PDs; next, perform a fast Fourier transform on the visible light raw data at multiple positions, and extract the RSS from the corresponding frequencies; finally, fit the parameters $a$ and $m$ according to the visible light RSS at multiple moments, the distances between the multiple receivers and the LED base station, and the incident angles.

[0063] After the offline calibration is completed, the calibrated Lambert model can be used for online ranging. The process of visible light online ranging is as follows:

[0064] First, the receiving - end PD collects the original visible - light data for a period of time; then, perform a fast Fourier transform on the original visible - light data, and extract the RSS from the corresponding frequencies of different receiving - ends; finally, substitute the extracted RSS into formula (3) to obtain the ranging information from different transmitting - ends to the receiving - end, and solve the distance between the receiving - end PD and the corresponding LED base station through the least - squares method. The calculation formula for the distance between the receiving - end PD and the corresponding LED base station is:

[0065]

[0066] In the formula, d is the distance from the receiving - end to the transmitting - end, h is the height difference from the receiving - end to the transmitting - end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving - end, a is the power parameter of the ranging system, m is the half - power angle of the receiving - end, and M is the Lambert order of the transmitting - end.

[0067] S2. Use the pedestrian dead - reckoning method to calculate the collected inertial data to obtain the position information of the receiving - end.

[0068] Since the visible - light ranging algorithm is affected by environmental factors such as occlusion and tilt, it is difficult to maintain high - precision positioning in complex dynamic scenarios. Inertial sensors, which are not affected by the external environment, are widely used in the field of navigation and positioning. In the field of pedestrian navigation, the PDR algorithm is usually used for positioning.

[0069] The PDR module in this embodiment can be divided into four steps: step detection, heading estimation, step - length estimation, and position recursion.

[0070] Step 1: Step detection

[0071] Step detection is to detect the periodic step signal when a pedestrian walks through an accelerometer. Usually, the accelerometer records the periodic vibration signal generated by a pedestrian during walking, and detects the steps by analyzing the peaks of these signals.

[0072] a(t)>a th (4)

[0073] where a(t) is the acceleration at time t, and a th is the set acceleration threshold for step detection. When the peak value of the acceleration signal exceeds the threshold, it is considered that one step has been taken at this time.

[0074] Step 2: Heading estimation

[0075] Heading estimation is to estimate the walking direction of a pedestrian through sensors such as gyroscopes. The gyroscope can provide angular velocity information, and the change in the heading of the pedestrian can be obtained by integrating the angular velocity.

[0076]

[0077] In the formula, θ(t) represents the heading angle at time t, θ(t0) represents the heading angle at time t0, t0 is the starting time for calculating the angle change, and ω(τ) represents the angular velocity at time τ. Generally, the heading angle corresponding to the detected step time t during the step detection stage is considered as the advancing direction of the current step.

[0078] Step 3: Step length estimation

[0079] The step length is estimated by the pedestrian's step characteristics (such as step frequency, acceleration amplitude, etc.) to estimate the length of each step. The step length estimation is usually based on an empirical model. The step length estimation model used in the present invention is the Weinberg model, and its formula for calculating the step length is:

[0080]

[0081] where L k is the estimated step length of the k-th step, a max is the maximum value of the pedestrian's vertical acceleration in one step, a min is the minimum value of the pedestrian's vertical acceleration in one step, and K is a model parameter.

[0082] Step 4: Position recursion

[0083] After the step detection, heading estimation, and step length estimation are completed, the position is updated through position recursion:

[0084]

[0085] where (x n , y n ) is the position of the n-th step, (x n+1 , y n+1 ) is the position of the (n + 1)-th step, L n is the step length of the n-th step, and θ n is the heading angle of the n-th step. Through position recursion, the position of the (n + 1)-th step can be obtained.

[0086] S3. Based on the particle filter algorithm, tightly combine and fuse the distance between the receiving end and the transmitting end and the position information of the receiving end to obtain the predicted pedestrian position.

[0087] Since both the single visible light or single inertial sensor positioning methods have limitations, it is necessary to combine the two positioning methods to achieve complementary advantages. Therefore, the present invention proposes a tightly combined fusion method of PDR based on particle filter and visible light ranging. The positioning result of PDR is used as the state quantity of the system, and the visible light ranging information is used as the observation quantity of the system. The state of the system is updated through particle filter, effectively improving the robustness of the fusion positioning system.

[0088] Among them, the state equation of the tight combination method in this embodiment can be expressed as:

[0089]

[0090] In the formula, X k+1 represents the position and heading set at time k+1, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian stride estimated using the Weinberg model.

[0091] According to the above formula, the system error state model can be expressed as:

[0092] δX = [δx δy δz δθ] (9)

[0093] In the formula, δX is the error state, δx is the x-axis positioning error state, δy is the y-axis positioning error state, δz is the z-axis positioning error state, and δθ is the heading angle error state.

[0094] Therefore, the linearized state space model of the discrete-time process of the equation can be expressed as:

[0095] δX k+1 = F k δX k (10)

[0096] In the formula, k and k+1 represent times k and k+1, δX is the error state, and F k represents the state transition matrix:

[0097]

[0098] Furthermore, the measurement model can be expressed as:

[0099] Z k = ||P pdr - ρ|| - d (12)

[0100] In the formula, Z k is the measurement error vector; ρ represents the coordinates of the LED; P pdr represents the position vector updated by pedestrian dead reckoning; d is the visible light ranging observation value calculated from formula (3). After linearizing the equation, the measurement equation can be re-expressed as:

[0101] Z k = H k δ k + v k (13)

[0102] In the formula, v k is the covariance matrix Rk Range measurement error at time H k Denote the Jacobian matrix, and its calculation formula is:

[0103]

[0104] In the formula, J F The calculation formula of is:

[0105]

[0106] In the formula, N led Denotes the number of LED lights; m denotes the m-th LED light.

[0107] The technical solution of this embodiment overcomes the limitations of single visible light ranging and single PDR technology by tightly coupling and integrating visible light ranging and PDR positioning, significantly improving the stability and robustness of the system positioning. This method can still maintain high precision and stability under complex conditions such as signal occlusion and IMU degradation, and has wide applicability and generalizability.

[0108] Further referring to Figure 2 , the embodiment of the present invention also provides a pedestrian navigation system based on the intelligent fusion of optical signals and inertia, including:

[0109] A visible light ranging module, configured to calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and the receiving end and the original visible light data collected by the receiving end;

[0110] A pedestrian dead reckoning module, configured to deduce the collected inertial data to obtain the position information of the receiving end;

[0111] A fusion module, configured to perform tight combination fusion on the distance between the receiving end and the transmitting end and the position information of the receiving end based on the particle filter algorithm to obtain the predicted pedestrian position.

[0112] Among them, the visible light module specifically includes:

[0113] An offline calibration module, configured to calibrate the Lambert order M of the transmitting end of the Lambert channel model according to the original visible light data measured by the receiving end at different tilt angles;

[0114] According to the original visible light data measured by the receiving end at different positions, as well as the distance and incident angle between the receiving end and the transmitting end, calibrate the power parameter a of the Lambert channel model and the half-power angle m of the receiving end;

[0115] The online ranging module is used for the receiving end to collect the original visible light data within a period of time, extract the corresponding received signal strength through fast Fourier transform, and substitute the received signal strength into the calibrated Lambert channel model to obtain the distances from different transmitting ends to the receiving end.

[0116] Further, the calculation formula for the distance from the transmitting end to the receiving end is:

[0117]

[0118] where d is the distance from the receiving end to the transmitting end, h is the height difference from the receiving end to the transmitting end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving end, a is the power parameter of the ranging system, m is the half-power angle of the receiving end, and M is the Lambert order of the transmitting end.

[0119] The state equation in the fusion module is:

[0120]

[0121] where X k+1 represents the set of the position and heading of the pedestrian at the (k + 1)-th moment, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian's step length.

[0122] Further, the model of the state error in this embodiment is:

[0123] δX = [δx δy δz δθ]

[0124] where δX is the error state, δx is the positioning error state on the x-axis, δy is the positioning error state on the y-axis, δz is the positioning error state on the z-axis, and δθ is the heading angle error state.

[0125] The measurement model in this embodiment is:

[0126] Z k = ||P pdr - ρ|| - d

[0127] where Z k is the measurement error vector; ρ is the coordinate of the transmitting end; P pdr is the position vector updated by the pedestrian dead reckoning; d is the distance between the receiving end and the transmitting end calculated by the calibrated Lambert channel model.

[0128] The pedestrian navigation system based on optical signal and inertial intelligent fusion provided by the embodiment of the present invention can execute the method of the pedestrian navigation system based on optical signal and inertial intelligent fusion provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method, which will not be elaborated herein.

[0129] Experimental verification

[0130] Figure 3a and Figure 3b are the experimental results of the comparison between the present invention and other methods. In the figure, Reference represents the reference trajectory; PDR represents the pedestrian dead reckoning result; VLP represents the visible light positioning result; LC represents the loose integrated positioning result; EDLC, CR, EDCR, and EDDR are the tightly integrated positioning results proposed by the present invention, and the difference lies in the configuration of the filtering parameters. From Figure 3a and Figure 3b , it can be seen that the proposed integrated method has obvious improvement compared with the traditional methods.

[0131] Note that the above is only the preferred embodiment of the present invention and the applied technical principle. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described here. Various obvious changes, re-adjustments, and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A pedestrian navigation method based on the intelligent fusion of optical signals and inertia, characterized in that Including: S1. Calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and the receiving end and the original visible light data collected by the receiving end; S2. Use the pedestrian dead reckoning method to deduce the collected inertial data to obtain the position information of the receiving end; S3. Based on the particle filter algorithm, tightly combine and fuse the distance between the receiving end and the transmitting end and the position information of the receiving end to obtain the predicted pedestrian position.

2. The method according to claim 1, wherein The S1 includes an offline calibration step and an online ranging step, where: The offline calibration step includes: calibrating the Lambert order M of the transmitting end of the Lambert channel model according to the original visible light data measured by the receiving end at different tilt angles; According to the original visible light data measured by the receiving end at different positions, as well as the distance and incident angle between the receiving end and the transmitting end, calibrate the power parameter a of the Lambert channel model and the half-power angle m of the receiving end; The online ranging step includes: the receiving end collects the original visible light data for a period of time, extracts the corresponding received signal strength through fast Fourier transform, and substitutes the received signal strength into the calibrated Lambert channel model to obtain the distances from different transmitting ends to the receiving end.

3. The method according to claim 2, characterized in that, The calculation formula for the distance from the transmitting end to the receiving end is: where d is the distance from the receiving end to the transmitting end, h is the height difference from the receiving end to the transmitting end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving end, a is the power parameter of the ranging system, m is the half-power angle of the receiving end, and M is the Lambert order of the transmitting end.

4. The method according to claim 1, wherein The state equation for tight combined fusion in S3 is: Among them, X k+1 represents the position and heading set of the pedestrian at time k + 1, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian's step length.

5. The method according to claim 4, characterized in that The model for the corresponding state error in S3 is: δX = [δx δy δz δθ] Where, δX is the error state, δx is the x-axis positioning error state, δy is the y-axis positioning error state, δz is the z-axis positioning error state, and δθ is the heading angle error state.

6. The method according to claim 4, wherein The measurement model corresponding to S3 is: Z k = ||P pdr - ρ|| - d Among them, Z k is the measurement error vector; ρ is the coordinate of the transmitting end; P pdr is the position vector updated by pedestrian dead reckoning; d is the distance between the receiving end and the transmitting end calculated by the calibrated Lambert channel model.

7. A pedestrian navigation system integrating optical signals and inertial intelligence, characterized in that, Including: A visible light ranging module, configured to calculate the distance between the receiving end and the transmitting end according to the positions of the visible light transmitting end and the receiving end and the original visible light data collected by the receiving end; A pedestrian dead reckoning module, configured to deduce the collected inertial data to obtain the position information of the receiving end; A fusion module, configured to tightly combine and fuse the distance between the receiving end and the transmitting end and the position information of the receiving end based on the particle filter algorithm to obtain the predicted pedestrian position.

8. The system according to claim 7, characterized in that The visible light module specifically includes: An offline calibration module, configured to calibrate the Lambert order M of the transmitting end of the Lambert channel model according to the original visible light data measured by the receiving end at different tilt angles; According to the original visible light data measured by the receiving end at different positions, as well as the distance and incident angle between the receiving end and the transmitting end, calibrate the power parameter a of the Lambert channel model and the half-power angle m of the receiving end; An online ranging module, configured to collect the original visible light data for a period of time by the receiving end, extract the corresponding received signal strength through fast Fourier transform, and substitute the received signal strength into the calibrated Lambert channel model to obtain the distances from different transmitting ends to the receiving end.

9. The system according to claim 7, wherein The calculation formula for the distance from the transmitting end to the receiving end is: where d is the distance from the receiving end to the transmitting end, h is the height difference from the receiving end to the transmitting end, P ri is the received signal strength, θ is the angle between the incident light and the normal vector of the receiving end, a is the power parameter of the ranging system, m is the half-power angle of the receiving end, and M is the Lambert order of the transmitting end.

10. The system according to claim 7, characterized in that, The state equation in the fusion module is: Among them, X k+1 represents the position and heading set of the pedestrian at time k + 1, (x, y, z) represents the three-dimensional coordinates in the navigation coordinate system, θ represents the heading angle calculated by gyroscope integration, and l is the pedestrian's step length.

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