Indoor positioning method and system based on multi-sensor fusion

Abstract

The invention discloses an indoor positioning method and system based on multi-sensor fusion, and relates to the technical field of indoor positioning, and the method comprises the following steps: deploying a Wi-Fi transceiver and each sensor; carrying out Wi-Fi CSI to-be-detected area range detection; carrying out refined detection on the sub-regions by a PIR sensor; calculating centroid positioning coordinates; accurate positioning through ultrasonic ranging; and fusing sensor positioning results. According to the technical method, the sensors are deployed, CSI information, voltage signals of the PIR sensors and ultrasonic signals are collected, personnel detection of subareas is refined through a double-dynamic threshold method, weighted positioning is carried out based on the signal intensity of the PIR sensors, and the relative positions of personnel in the areas are calculated. And by combining centimeter-level accurate distance measurement of an ultrasonic sensor, accurate two-dimensional coordinates of the personnel are obtained through a triangulation method. And finally, fusing PIR centroid positioning and ultrasonic ranging results through a Kalman filtering algorithm, realizing real-time updating and dynamic tracking, and outputting high-precision personnel position information.

Description

Technical Field

[0001] The present invention relates to the field of indoor positioning technology, and in particular to an indoor positioning method and system based on multi-sensor fusion. Background Art

[0002] With the rapid development of smart home, security monitoring, medical care and other fields, the demand for indoor personnel positioning is increasing. Indoor positioning technology refers to the detection and positioning of active personnel inside buildings. Compared with outdoor GPS positioning, the indoor environment is more complex, and signal propagation is interfered by obstacles such as walls. Traditional positioning methods such as Bluetooth, infrared or ultrasonic positioning are limited in accuracy, stability and applicability indoors. Therefore, realizing a low-cost, high-precision indoor positioning technology that does not rely on portable terminal devices and tags, so that administrators can obtain personnel location information in real time, has become an important research direction.

[0003] At present, researchers have proposed a variety of methods for indoor positioning, especially in terms of sensor fusion, to ensure the accuracy of positioning. For example, the "Indoor positioning method and system based on the hybrid of WiFi and ultrasound" first obtains WiFi fingerprints and builds a fingerprint library; then, a clustering algorithm is used to perform cluster analysis on the collected WiFi fingerprint data; finally, the target location is locked in a single sub-area, and ultrasonic ranging is combined to perform subdivision positioning of the local area. This method relies on electronic tags and requires the construction of a WiFi fingerprint library. When the device layout changes, the fingerprint library needs to be continuously updated, which increases the maintenance cost. Moreover, relying solely on ultrasound is easily affected by reflection interference from physical obstacles, which affects the positioning accuracy. Therefore, the use of fusion algorithms can not only make up for the weaknesses of a single sensor, but also optimize the positioning accuracy through dynamic adjustment. Summary of the invention

[0004] The technical solution of the present invention to solve the above technical problem is to provide an indoor positioning method based on multi-sensor fusion, comprising the following steps:

[0005] Step 1, deploy Wi-Fi transceiver equipment and various sensors: deploy Wi-Fi signal transceiver equipment, PIR sensors and ultrasonic sensors in the area to be located, where the Wi-Fi signal transceiver equipment is used to provide overall wireless signal coverage, the PIR sensor is used to detect human infrared radiation to sense human activities, and the ultrasonic sensor is used to measure distance;

[0006] Step 2: Wi-Fi CSI test area range detection: collect CSI information, pre-process the CSI signal, extract the main change components of the CSI data, and perform maximum value normalization. Observe the frequency characteristics of signal amplitude changes through short-time Fourier transform, extract features of phase difference, build a training data set and train the SVM classification model to distinguish the activity status of people in the environment.

[0007] Step 3, detailed detection of the sub-area by the PIR sensor: according to the voltage signal of the PIR sensor, the voltage peak and climbing slope characteristics are extracted, and the double dynamic threshold method is used to judge the movement state of the person and determine the person's activities in the sub-area;

[0008] Step 4: Calculate the centroid coordinates: Based on the signal strength of multiple PIR sensors, calculate the relative position (x PIR ,y PIR );

[0009] Step 5: Ultrasonic ranging precise positioning: Using the known coordinates of the ultrasonic sensor and the measured distance value, the precise position of the person (x ultra ,y ultra );

[0010] Step 6, fusion sensor positioning results: obtain PIR centroid positioning coordinates and ultrasonic ranging coordinates, define state variables and observation vectors, perform state prediction and update through Kalman filtering, calculate Kalman gain, combine observation results with predicted values, correct the current coordinates and speed of the target, and update the error covariance matrix, and finally output the fused target position (x k ,y k ).

[0011] Furthermore, in step 1, a modular splicing method is used to deploy sensors and divide sub-areas so as to overlap and cover the detection areas of the sensors.

[0012] Furthermore, in step 2, the CSI information includes:

[0013] H(f i )=|H(f i )|e jθ(fi) ;

[0014] Among them, H(f i ) is the CSI value of the i-th subcarrier; |H(f i )| is the amplitude of CSI; θ(f i ) is the phase of CSI; f i is the frequency of the ith subcarrier;

[0015] Preprocess the CSI signal: Use the PCA principal component analysis method to extract the main change components in the CSI information, and decompose the data into multiple principal components through PCA. The formula is as follows:

[0016] X=UΣV T ;

[0017] Where X is the CSI data matrix; U and V are eigenvector matrices respectively; T represents the transpose operation of the eigenvector matrix; Σ is the singular value matrix, representing the principal components of the signal;

[0018] Perform maximum normalization on the CSI amplitude:

[0019]

[0020] Among them, |H norm (f i ,t)| is the normalized CSI amplitude value, indicating that at frequency f i The result after normalization of the CSI amplitude at time t; |H(f i ,t)| is the original CSI amplitude value, indicating that at frequency f i and the unnormalized CSI amplitude information obtained from the Wi-Fi device at time t; max(|H(f i ,t)|) is the maximum value of the CSI amplitude at all frequencies and time points of the current CSI data, which is used as the normalization benchmark;

[0021] The frequency characteristics of signal amplitude changes at different times are obtained by short-time Fourier transform STFT; the CSI amplitude signal is converted from the time domain |H norm (f i ,t)|convert to the frequency domain S(f,t), using the formula:

[0022]

[0023] Where S(f,t) represents the CSI time-frequency characteristic value at frequency f and time t, which is used to reflect the change of CSI amplitude with frequency; -j2πfτ is the kernel function of Fourier transform, where j is the imaginary unit, π is a mathematical constant, τ is the integral variable, and dτ represents the differential element of the integral;

[0024] Extracting phase difference features;

[0025] The training data set is constructed by extracting the amplitude change frequency features and phase difference features for training the classification model. The SVM classifier is trained using the extracted CSI features. During the training process, the annotated CSI feature data is used to distinguish between the situations of "personnel activity" and "no personnel activity". The SVM classifier model is as follows:

[0026] f(x)=w T φ(x)+b;

[0027] Among them, f(x) represents the predicted output of input x; x is the input feature, namely the amplitude change and phase difference features; w is the weight of the SVM model; φ(x) represents the feature vector obtained after the input x passes through the feature mapping function φ; b is the deviation.

[0028] Furthermore, in step 3,

[0029] Set voltage peak threshold: Dynamically adjust the voltage peak detection threshold V according to the ambient noise level and typical human activity signals th,peak ; When the voltage peak of the sensor exceeds the set threshold, it indicates that there is human activity in the area; the voltage peak judgment conditions are as follows:

[0030] V peak >V th,peak ;

[0031] Among them, V peak is the peak voltage output by the PIR sensor; V th,peak It is the voltage peak threshold that is dynamically adjusted according to environmental conditions;

[0032] Set the climbing slope threshold: Calculate the voltage climbing slope by sampling the voltage signal change rate of the sensor:

[0033]

[0034] Where dV is the voltage increment; dt is the time increment;

[0035] Dynamically set the slope threshold α according to different exercise intensities th,slope ; When the slope of the voltage change exceeds the threshold, it indicates that there is a strong motion state; the climbing slope determination conditions are as follows:

[0036] α slope >α th,slope ;

[0037] Among them, α slope is the voltage climbing slope currently measured; α th,slope It is the slope threshold that is dynamically adjusted according to the intensity of exercise;

[0038] Different types of human activities are judged by combining the two dynamic thresholds of voltage peak value and climbing slope. When the following conditions are met, it is determined that there are people active in the sub-area:

[0039]

[0040] Furthermore, in step 4, when a person enters the detection area of ​​the PIR sensor, the PIR sensor will output a signal strength S related to the distance between the person and the sensor. i , n PIR sensors are arranged in a sub-area, and the sensor coordinates are (x i ,y i ), where i = 1, 2, ..., n;

[0041] The signal strength of each PIR sensor is used as a weight, and the relative position of the person is calculated through weighted average based on the spatial position of the sensor;

[0042] Estimate the centroid coordinates using a weighted average method:

[0043]

[0044] Among them, w i is the signal strength of the ith PIR sensor; x PIR and PIR is the coordinate of the centroid of the person in the sub-area, i.e. the estimated relative position; x i and i is the coordinate of the i-th sensor; ∑w i x i and ∑w i y i Indicates that the coordinate x of each point i and i With its weight w i The result of multiplication and summation; ∑w i represents the sum of all weights and is the normalization factor;

[0045] The relative position of the personnel in the sub-area (x PIR ,y PIR ).

[0046] Furthermore, in step 5, for the sub-area where the person is located, the coordinates of each ultrasonic sensor are marked as (x i ,y i ), where i = 1, 2, 3; use triangulation to calculate the precise coordinates of the person through the distance measurements of multiple ultrasonic sensors; let the actual position of the person be (x ultra ,y ultra ), combine the following equations to solve the coordinate (x ultra ,y ultra ):

[0047] The distance d1 obtained from ultrasonic sensor 1:

[0048]

[0049] The distance d2 obtained from ultrasonic sensor 2:

[0050]

[0051] The distance d3 obtained from ultrasonic sensor 3:

[0052]

[0053] The solution is obtained by numerical methods (such as Newton iteration method or least square method); the precise position of the personnel in the sub-area (x ultra ,y ultra ).

[0054] Furthermore, in step 6,

[0055] Define the state variable X of the target k for:

[0056]

[0057] Among them, x k and k is the two-dimensional coordinate position of the target at time k; v x and v y are the speeds of the target in the x and y directions.

[0058] Define the observation vector as Z k :

[0059]

[0060] in, and is the relative position provided by the PIR centroid positioning at time k; and is the precise position provided by ultrasonic ranging at time k.

[0061] The prediction model of Kalman filter is based on the state X at the previous moment. k-1 Calculate the target position and speed at the current moment;

[0062] The state transfer equation is defined as:

[0063] X k|k-1 =F·X k-1 +B·U k ;

[0064] Among them, X k|k-1 is the current state predicted based on the previous state at time k; B is the control matrix, which can be ignored if there is no control input; U kis the control input vector, which can be ignored if there is no control input; F is the state transfer matrix, assuming that the position changes steadily over time:

[0065]

[0066] Calculate the Kalman gain: Kalman gain K k Defined as:

[0067] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0068] Among them, P k|k-1 is the predicted state error covariance matrix, which represents the uncertainty of the predicted value at the current moment; R is the measurement noise covariance matrix; H is the observation matrix:

[0069]

[0070] State update: After calculating the Kalman gain, the observation result Z is updated k With the predicted value X k|k-1 Combined with the state update, the state update equation is:

[0071] X k =X k|k-1 +K k ·(Z k -H·X k|k-1 );

[0072] Among them, Z k is the measurement result of PIR and ultrasonic sensor; H·X k|k-1 is the observed value obtained from the prediction;

[0073] Update the state error covariance matrix P k , the error covariance update equation is:

[0074] P k =(IK k H)P k|k-1 ;

[0075] Where I is the identity matrix;

[0076] Output the fused target position (x k ,y k ).

[0077] In order to solve the above technical problems, the present invention further proposes an indoor positioning system based on multi-sensor fusion, which is used to execute the indoor positioning method based on multi-sensor fusion as described above, comprising:

[0078] Divide the sub-areas and deploy sensor modules. Set the area of ​​the positioning area to S, and divide the area into n sub-areas according to the size of the area to be positioned.

[0079] The data acquisition module includes the Wi-Fi access point and the receiving device to collect the channel status information of the Wi-Fi signal, including the amplitude and phase changes of each subcarrier; the PIR sensor collects the infrared radiation intensity signal and outputs the voltage signal that changes with the movement of the person; the ultrasonic sensor transmits and receives the ultrasonic signal, measures the time difference of the reflected signal, and collects the signal reflecting the distance between the person and the sensor;

[0080] Data processing module, used to process the channel status information of Wi-Fi signals, voltage signals of PIR sensors, and ultrasonic signals;

[0081] The fusion algorithm module uses Kalman filtering to fuse the data of the PIR sensor and the ultrasonic sensor, defines the state variables and observation vectors, predicts and updates the state through Kalman filtering, calculates the Kalman gain, combines the observation results with the predicted values, corrects the current coordinates and speed of the target, and updates the error covariance matrix, and finally outputs the fused target position.

[0082] Compared with the prior art, this application has the following beneficial effects:

[0083] 1. The present invention provides an expandable multi-sensor joint layout strategy for indoor human body positioning. The modular layout strategy can be flexibly adjusted according to the actual situation of the scene to be tested. By overlapping the sensor detection area, the detection blind area is avoided, and the detection accuracy and reliability are improved. When the detection of a single sensor is biased or the signal is weakened, the system can still perform compensation detection through other sensors.

[0084] 2. The present invention provides a multi-sensor indoor positioning method from coarse to fine. The method realizes accurate positioning from coarse to fine, from large range to small area. It has good adaptability to environmental changes, ensures accurate tracking of moving targets, and can continuously correct the position of personnel. Compared with the traditional single Wi-Fi or PIR positioning method, this method can improve the accuracy of personnel positioning to within 5 cm.

[0085] 3. The present invention provides a multi-sensor indoor positioning system. The indoor positioning method is systematized and modularized, so that it can be extended to practical applications in different scenarios. Its efficient layout strategy, accurate positioning method and reliable fusion algorithm enable the system to provide excellent detection and positioning services in different scenarios, providing strong support for intelligent management and security monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0087] Figure 1 This is a flowchart of the steps of an indoor positioning method based on multi-sensor fusion according to the present invention;

[0088] Figure 2 A schematic diagram of sensor layout of the indoor positioning method based on multi-sensor fusion according to the present invention;

[0089] Figure 3 It is a multi-sensor fusion flow chart of the indoor positioning method based on multi-sensor fusion of the present invention;

[0090] Figure 4 This is a structural block diagram of the indoor positioning system based on multi-sensor fusion provided by the present invention. DETAILED DESCRIPTION

[0091] The present invention proposes an indoor positioning method based on multi-sensor fusion, aiming to design an indoor personnel detection and positioning method based on the combination of Wi-Fi CSI, PIR sensor and ultrasonic ranging.

[0092] The following is an explanation of an indoor positioning method based on multi-sensor fusion proposed by the present invention in a specific embodiment:

[0093] Embodiment 1:

[0094] In the technical solution of this embodiment, Figure 1 As shown, an indoor positioning method based on multi-sensor fusion includes the following steps:

[0095] Step 1, deploy Wi-Fi transceiver equipment and various sensors: deploy Wi-Fi signal transceiver equipment, PIR sensors and ultrasonic sensors in the area to be located, where the Wi-Fi signal transceiver equipment is used to provide overall wireless signal coverage, the PIR sensor is used to detect human infrared radiation to sense human activities, and the ultrasonic sensor is used to measure distance;

[0096] Step 2: Wi-Fi CSI test area range detection: collect CSI information, pre-process the CSI signal, extract the main change components of the CSI data, and perform maximum value normalization. Observe the frequency characteristics of signal amplitude changes through short-time Fourier transform, extract features of phase difference, build a training data set and train the SVM classification model to distinguish the activity status of people in the environment.

[0097] Step 3, detailed detection of the sub-area by the PIR sensor: according to the voltage signal of the PIR sensor, the voltage peak and climbing slope characteristics are extracted, and the double dynamic threshold method is used to judge the movement state of the person and determine the person's activities in the sub-area;

[0098] Step 4: Calculate the centroid coordinates: Based on the signal strength of multiple PIR sensors, calculate the relative position (x PIR ,y PIR );

[0099] Step 5: Ultrasonic ranging precise positioning: Using the known coordinates of the ultrasonic sensor and the measured distance value, the precise position of the person (x ultra ,y ultra );

[0100] Step 6, fusion sensor positioning results: obtain PIR centroid positioning coordinates and ultrasonic ranging coordinates, define state variables and observation vectors, perform state prediction and update through Kalman filtering, calculate Kalman gain, combine observation results with predicted values, correct the current coordinates and speed of the target, and update the error covariance matrix, and finally output the fused target position (x k ,y k ).

[0101] Furthermore, in step 1, a modular splicing method is used to deploy sensors and divide sub-areas so as to overlap and cover the detection areas of the sensors.

[0102] Furthermore, in step 2, the CSI information includes:

[0103]

[0104] Among them, H(f i ) is the CSI value of the i-th subcarrier; |H(fi )| is the amplitude of CSI; θ(f i ) is the phase of CSI; f i is the frequency of the ith subcarrier;

[0105] Preprocess the CSI signal: Use the PCA principal component analysis method to extract the main change components in the CSI information, and decompose the data into multiple principal components through PCA. The formula is as follows:

[0106] X=UΣV T ;

[0107] Where X is the CSI data matrix; U and V are eigenvector matrices respectively; T represents the transpose operation of the eigenvector matrix; Σ is the singular value matrix, representing the principal components of the signal;

[0108] Perform maximum normalization on the CSI amplitude:

[0109]

[0110] Among them, |H norm (f i ,t)| is the normalized CSI amplitude value, indicating that at frequency f i The result after normalization of the CSI amplitude at time t; |H(f i ,t)| is the original CSI amplitude value, indicating that at frequency f i and the unnormalized CSI amplitude information obtained from the Wi-Fi device at time t; max(|H(f i ,t)|) is the maximum value of the CSI amplitude at all frequencies and time points of the current CSI data, which is used as the normalization benchmark;

[0111] The frequency characteristics of signal amplitude changes at different times are obtained by short-time Fourier transform STFT; the CSI amplitude signal is converted from the time domain |H norm (f i ,t)|convert to the frequency domain S(f,t), using the formula:

[0112]

[0113] Where S(f,t) represents the CSI time-frequency characteristic value at frequency f and time t, which is used to reflect the change of CSI amplitude with frequency; -j2πfτ is the kernel function of Fourier transform, where j is the imaginary unit, π is a mathematical constant, τ is the integral variable, and dτ represents the differential element of the integral;

[0114] Extracting phase difference features;

[0115] The training data set is constructed by extracting the amplitude change frequency features and phase difference features for training the classification model. The SVM classifier is trained using the extracted CSI features. During the training process, the annotated CSI feature data is used to distinguish between the situations of "personnel activity" and "no personnel activity". The SVM classifier model is as follows:

[0116] f(x)=w T φ(x)+b;

[0117] Among them, f(x) represents the predicted output of input x; x is the input feature, namely the amplitude change and phase difference features; w is the weight of the SVM model; φ(x) represents the feature vector obtained after the input x passes through the feature mapping function φ; b is the deviation.

[0118] Furthermore, in step 3,

[0119] Set voltage peak threshold: Dynamically adjust the voltage peak detection threshold V according to the ambient noise level and typical human activity signals th,peak ; When the voltage peak of the sensor exceeds the set threshold, it indicates that there is human activity in the area; the voltage peak judgment conditions are as follows:

[0120] V peak >V th,peak ;

[0121] Among them, V peak is the peak voltage output by the PIR sensor; V th,peak It is the voltage peak threshold that is dynamically adjusted according to environmental conditions;

[0122] Set the climbing slope threshold: Calculate the voltage climbing slope by sampling the voltage signal change rate of the sensor:

[0123]

[0124] Where dV is the voltage increment; dt is the time increment;

[0125] Dynamically set the slope threshold α according to different exercise intensities th,slope ; When the slope of the voltage change exceeds the threshold, it indicates that there is a strong motion state; the climbing slope determination conditions are as follows:

[0126] α slope >α th,slope ;

[0127] Among them, α slope is the voltage climbing slope currently measured; α th,slope It is the slope threshold that is dynamically adjusted according to the intensity of exercise;

[0128] Different types of human activities are judged by combining the two dynamic thresholds of voltage peak value and climbing slope. When the following conditions are met, it is determined that there are people active in the sub-area:

[0129]

[0130] Furthermore, in step 4, when a person enters the detection area of ​​the PIR sensor, the PIR sensor will output a signal strength S related to the distance between the person and the sensor. i , n PIR sensors are arranged in a sub-area, and the sensor coordinates are (x i ,y i ), where i = 1, 2, ..., n;

[0131] The signal strength of each PIR sensor is used as a weight, and the relative position of the person is calculated through weighted average based on the spatial position of the sensor;

[0132] Estimate the centroid coordinates using a weighted average method:

[0133]

[0134] Among them, w i is the signal strength of the ith PIR sensor; x PIR and PIR is the coordinate of the centroid of the person in the sub-area, i.e. the estimated relative position; x i and i is the coordinate of the i-th sensor; ∑w i x i and ∑w i y i Indicates that the coordinate x of each point i and i With its weight w i The result of multiplication and summation; ∑w i represents the sum of all weights and is the normalization factor;

[0135] The relative position of the personnel in the sub-area (x PIR ,y PIR ).

[0136] Furthermore, in step 5, for the sub-area where the person is located, the coordinates of each ultrasonic sensor are marked as (x i ,y i ), where i = 1, 2, 3; use triangulation to calculate the precise coordinates of the person through the distance measurements of multiple ultrasonic sensors; let the actual position of the person be (x ultra ,y ultra ), combine the following equations to solve the coordinate (x ultra ,yultra ):

[0137] The distance d1 obtained from ultrasonic sensor 1:

[0138]

[0139] The distance d2 obtained from ultrasonic sensor 2:

[0140]

[0141] The distance d3 obtained from ultrasonic sensor 3:

[0142]

[0143] The solution is obtained by numerical methods (such as Newton iteration method or least square method); the precise position of the personnel in the sub-area (x ultra ,y ultra ).

[0144] Furthermore, in step 6,

[0145] Define the state variable X of the target k for:

[0146]

[0147] Among them, x k and k is the two-dimensional coordinate position of the target at time k; v x and v y are the speeds of the target in the x and y directions.

[0148] Define the observation vector as Z k :

[0149]

[0150] in, and is the relative position provided by the PIR centroid positioning at time k; and is the precise position provided by ultrasonic ranging at time k.

[0151] The prediction model of Kalman filter is based on the state X at the previous moment. k-1 Calculate the target position and speed at the current moment;

[0152] The state transfer equation is defined as:

[0153] X k|k-1 =F·X k-1 +B·U k ;

[0154] Among them, X k|k-1 is the current state predicted based on the previous state at time k; B is the control matrix, which can be ignored if there is no control input; U k is the control input vector, which can be ignored if there is no control input; F is the state transfer matrix, assuming that the position changes steadily over time:

[0155]

[0156] Calculate the Kalman gain: Kalman gain K k Defined as:

[0157] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0158] Among them, P k|k-1 is the predicted state error covariance matrix, which represents the uncertainty of the predicted value at the current moment; R is the measurement noise covariance matrix; H is the observation matrix:

[0159]

[0160] State update: After calculating the Kalman gain, the observation result Z is updated k With the predicted value X k|k-1 Combined with the state update, the state update equation is:

[0161] X k =X k|k-1 +K k ·(Z k -H·X k|k-1 );

[0162] Among them, Z k is the measurement result of PIR and ultrasonic sensor; H·X k|k-1 is the observed value obtained from the prediction;

[0163] Update the state error covariance matrix P k , the error covariance update equation is:

[0164] P k =(IK k H)P k|k-1 ;

[0165] Where I is the identity matrix;

[0166] Output the fused target position (x k ,y k ).

[0167] In order to solve the above technical problems, the present invention further proposes an indoor positioning system based on multi-sensor fusion, which is used to execute the indoor positioning method based on multi-sensor fusion as described above, comprising:

[0168] Divide the sub-areas and deploy sensor modules. Set the area of ​​the positioning area to S, and divide the area into n sub-areas according to the size of the area to be positioned.

[0169] The data acquisition module includes the Wi-Fi access point and the receiving device to collect the channel status information of the Wi-Fi signal, including the amplitude and phase changes of each subcarrier; the PIR sensor collects the infrared radiation intensity signal and outputs the voltage signal that changes with the movement of the person; the ultrasonic sensor transmits and receives the ultrasonic signal, measures the time difference of the reflected signal, and collects the signal reflecting the distance between the person and the sensor;

[0170] Data processing module, used to process the channel status information of Wi-Fi signals, voltage signals of PIR sensors, and ultrasonic signals;

[0171] The fusion algorithm module uses Kalman filtering to fuse the data of the PIR sensor and the ultrasonic sensor, defines the state variables and observation vectors, predicts and updates the state through Kalman filtering, calculates the Kalman gain, combines the observation results with the predicted values, corrects the current coordinates and speed of the target, and updates the error covariance matrix, and finally outputs the fused target position.

[0172] Embodiment 2:

[0173] An indoor positioning method based on multi-sensor fusion includes the following steps:

[0174] Step 1: Deploy Wi-Fi transceivers and sensors:

[0175] First, Wi-Fi signal transceivers, PIR sensors, and ultrasonic sensors need to be deployed in the area to be located. The deployment method of sensors and the division of sub-areas are as follows: Figure 2 As shown. The Wi-Fi signal transceiver device is installed in the center of the area to be tested to provide overall wireless signal coverage. The black solid circle in the figure indicates the position of the PIR sensor, and the dotted hollow circle indicates the effective detection area of ​​the PIR sensor. Since the detection radius of the PIR sensor is affected by the installation height, this embodiment determines the radius of the detection area based on the typical indoor installation height and the model of the common PIR sensor. The gray square in the figure indicates the installation position of the ultrasonic sensor. S1, S2, S3, and S4 represent sub-areas divided by four squares, and the side length of each sub-area is equal to the detection radius of the PIR sensor. The black bold square represents a detection unit, which is composed of four sub-areas spliced ​​together.

[0176] The basic detection unit adopts modular splicing, so that the detection area of ​​the sensor can be reasonably planned through overlapping coverage. This layout method can flexibly adjust the splicing according to the actual indoor shape and area of ​​the scene to be tested. At the same time, each additional basic detection unit can not only effectively expand the detection range, but also avoid adding additional sensors, thereby improving the utilization rate of each sensor. In addition, this method significantly reduces hardware costs.

[0177] Step 2: Wi-Fi CSI test area range detection:

[0178] Based on the layout of step 1, first, the Wi-Fi AP is used as the signal transmitter, and the receiving device collects CSI information in real time through the wireless network card to obtain the amplitude and phase of each subcarrier. CSI information is obtained from multiple subcarriers of the wireless device. For each subcarrier, CSI information includes:

[0179]

[0180] Among them, H(f i ) is the CSI value of the i-th subcarrier; |H(f i )| is the amplitude of CSI; θ(f i ) is the phase of CSI; f i is the frequency of the ith subcarrier.

[0181] Secondly, the CSI signal is preprocessed: the PCA principal component analysis method is used to extract the main change components in the CSI data, remove irrelevant noise components, decompose the data into multiple principal components through PCA, and select the first few principal components that can explain most of the signal changes. The formula is as follows:

[0182] X=UΣV T ;

[0183] Where X is the CSI data matrix; U and V are eigenvector matrices respectively; T represents the transpose operation of the eigenvector matrix; Σ is the singular value matrix, representing the principal components of the signal;

[0184] Third, perform maximum normalization on the CSI amplitude:

[0185]

[0186] Fourth, through the short-time Fourier transform STFT, the frequency characteristics of the signal amplitude changes at different times are observed. The frequency domain representation is achieved by converting the CSI amplitude signal from the time domain |H norm (f i ,t)|convert to the frequency domain S(f,t), using the formula:

[0187]

[0188] Wherein, S(f,t) represents the CSI time-frequency characteristic value at frequency f and time t, which is used to reflect the change of CSI amplitude with frequency.

[0189] Fifth, feature extraction is performed on the phase difference to eliminate systematic noise.

[0190] Sixth, a training data set is constructed through the amplitude change frequency features and phase difference features extracted in the previous steps to train the classification model. Each data contains the amplitude change frequency features and phase difference features. The SVM classifier is trained using the extracted CSI features. During the training process, the annotated CSI feature data is used to distinguish between the situations of "personnel activity" and "no personnel activity". The SVM classifier model is as follows:

[0191] f(x)=w T φ(x)+b;

[0192] Among them, f(x) represents the predicted output of input x; x is the input feature, namely the amplitude change and phase difference features; w is the weight of the SVM model; φ(x) represents the feature vector obtained after the input x passes through the feature mapping function φ; b is the deviation.

[0193] The trained SVM model can classify human activities in the environment based on the CSI features extracted in real time. If the CSI features exceed a certain threshold or show significant amplitude and phase changes, the SVM model will output a judgment of "human activity".

[0194] Step 3: PIR sensor refines detection of sub-areas:

[0195] The PIR sensor generates a voltage signal by sensing infrared radiation. The movement of the human body will cause the voltage output of the PIR sensor to change. By collecting the voltage output of the PIR sensor and processing it, the characteristics reflecting the intensity of human activity are extracted. The two key characteristics of the voltage change are the voltage peak and the climbing slope.

[0196] In order to accurately detect different intensities of human motion, the dual dynamic threshold method is used. This method sets two dynamic thresholds, namely the voltage peak threshold and the climbing slope threshold, to ensure that the sensor can adapt to different motion states and identify small and violent movements.

[0197] Voltage peak threshold setting: Dynamically adjust the voltage peak detection threshold V according to the environmental noise level and typical human activity signals th,peak When the voltage peak of the sensor exceeds the set threshold, it indicates that there is human activity in the area. The voltage peak judgment conditions are as follows:

[0198] V peak >V th,peak ;

[0199] Among them, V peak is the peak voltage output by the PIR sensor; V th,peak It is the voltage peak threshold that is dynamically adjusted according to environmental conditions.

[0200] Climbing slope threshold setting: Calculate the voltage climbing slope by sampling the voltage signal change rate of the sensor:

[0201]

[0202] Where dV is the voltage increment and dt is the time increment.

[0203] Dynamically set the slope threshold α according to different exercise intensities th,slope When the slope of the voltage change exceeds the threshold, it indicates a strong motion state. The conditions for determining the climbing slope are as follows:

[0204] α slope >α th,slope ;

[0205] Among them, α slope is the voltage climbing slope currently measured; α th,slope It is the slope threshold that is dynamically adjusted according to the intensity of exercise.

[0206] Different types of human activities are judged by combining the two dynamic thresholds of voltage peak value and climbing slope. When the following conditions are met, it is determined that there are people active in the sub-area:

[0207]

[0208] Step 4, calculate the centroid positioning coordinates:

[0209] When a person enters the detection area of ​​the PIR sensor, the PIR sensor will output a signal strength S related to the distance between the person and the sensor. i , which is usually the voltage output of the sensor. The closer the distance, the stronger the signal strength, and the farther the distance, the weaker the signal strength. n PIR sensors are arranged in a sub-area, and the sensor coordinates are (x i ,y i ), where i = 1, 2, ..., n. The detection areas of these sensors overlap to a certain extent and can detect the activities of people at the same time.

[0210] The signal strength of each PIR sensor is used as a weight, and based on the spatial positions of these sensors, the relative position of the person is calculated by weighted average. Specifically, the greater the sensor signal strength, the closer the person is to the sensor, so the coordinates of the sensor contribute more to the final centroid position.

[0211] Estimate the centroid coordinates using a weighted average method:

[0212]

[0213] Among them, w i is the signal strength of the ith PIR sensor; x PIR and PIR is the coordinate of the centroid of the person in the sub-area, i.e. the estimated relative position; x i and i is the coordinate of the i-th sensor; ∑w i x i and ∑w i y i Indicates that the coordinate x of each point i and i With its weight w i The result of multiplication and summation; ∑w i represents the sum of all weights and is a normalization factor.

[0214] The relative position of the personnel in the sub-area (x PIR ,y PIR ).

[0215] Step 5: Ultrasonic ranging and precise positioning:

[0216] For the sub-area where the person is located, the known coordinates of each ultrasonic sensor are marked as (x i ,y i ), where i = 1, 2, 3. The coordinates of the ultrasonic sensor are fixed and are usually pre-measured and stored when the sensor is installed. Using triangulation, the precise coordinates of the person are calculated using the distance measurements of multiple ultrasonic sensors. Let the actual position of the person be (x ultra ,y ultra ), combine the following equations to solve the coordinate (x ultra ,y ultra ):

[0217] The distance d1 obtained from ultrasonic sensor 1:

[0218]

[0219] The distance d2 obtained from ultrasonic sensor 2:

[0220]

[0221] The distance d3 obtained from ultrasonic sensor 3:

[0222]

[0223] Since this is a nonlinear system of equations, it is usually necessary to solve it using numerical methods (such as Newton's iteration method or least squares method).

[0224] By solving the equations, the system obtains the precise position of the person in the sub-area (x ultra ,y ultra ).

[0225] Step 6: Fusion sensor positioning results:

[0226] like Figure 3 As shown, first, obtain the PIR centroid positioning coordinates obtained in step 4 and the ultrasonic ranging coordinates obtained in step 5, and ensure that the output data of the PIR and ultrasonic sensors have the same timestamp to ensure that the measurement results are based on the personnel position at the same time. To this end, timestamp the output data of each sensor to ensure data synchronization.

[0227] Second, define the state variable and observation vector. The state variable is the core of Kalman filtering, which reflects the real motion state of the target, while the observation vector is the measurement value from the sensor, which is used to correct the state prediction. The two need to cooperate with each other to achieve accurate positioning of the target.

[0228] Define the state variables of the target, which are used to describe the target's motion state, usually including position and velocity, indicating the target's motion state. Define the state vector X k for:

[0229]

[0230] Define the observation vector Z k for:

[0231]

[0232] in, and is the relative position provided by the PIR centroid positioning at time k; and is the precise position provided by ultrasonic ranging at time k.

[0233] Third, the prediction model estimates the target position and speed at the current moment based on the state at the previous moment to obtain a preliminary estimate at the current moment (without combining the actual measurement value).

[0234] Through the prediction model of Kalman filtering, the system predicts the position and speed of the target at the current moment based on the previous state estimation. The prediction process assumes that the target motion is smooth (uniform motion model), and the target position and speed are predicted through the linear state transfer matrix:

[0235] The state transfer equation is defined as:

[0236] X k|k-1 =F·X k-1 +B·U k ;

[0237] Assume the position changes steadily over time:

[0238]

[0239] At this time, the predicted target position X k|k-1 It is an estimate based on previous states and is not combined with measurements.

[0240] Fourth, calculate the Kalman gain. The state prediction is corrected by the measurement results of the PIR and ultrasonic sensors. The Kalman gain is used to balance the weight distribution between the predicted value and the measured value. The larger the Kalman gain, the more reliable the sensor's measurement data, so the measurement value will have a greater impact on the final positioning result.

[0241] The Kalman gain is defined as:

[0242] K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ;

[0243] Among them, P k|k-1 is the predicted state error covariance matrix, which represents the uncertainty of the predicted value at the current moment; R is the measurement noise covariance matrix, which reflects the uncertainty in the PIR and ultrasonic measurement results; H is the observation matrix, which describes how the measurement is mapped to the state space:

[0244]

[0245] Fifth, state update. After calculating the Kalman gain, the system updates the state by transforming the observation result Z k With the predicted value X k|k-1 Combined, the state is updated to correct the current coordinates and speed of the target. The final state Z k It is the exact position after fusion.

[0246] The state update equation is:

[0247] Xk =X k|k-1 +K k ·(Z k -H·X k|k-1 );

[0248] Among them, Z k is the measurement result of PIR and ultrasonic sensor; H·X k|k-1 is the observed value obtained from the prediction.

[0249] Fifth, update the error covariance. The system updates the state error covariance matrix P k , updating the system’s estimate of uncertainty to ensure that the prediction for the next time step is more accurate. The smaller the covariance matrix, the more confident the system is in its prediction.

[0250] The error covariance update equation is:

[0251] P k =(IK k H)P k|k-1 ;

[0252] This step is used to adjust the system's uncertainty estimate for the next prediction, ensuring that the Kalman filter can continue to optimize positioning accuracy in a dynamically changing environment.

[0253] Finally, the fused precise position is output. After the state prediction and update of the Kalman filter, the system finally outputs the precise position of the person (x k ,y k ). By combining the rough information of PIR centroid positioning with the precise results of ultrasonic ranging, the coordinates adapt to various changes in dynamic environments and can provide high-precision, real-time updated personnel location information.

[0254] Embodiment 3:

[0255] An indoor positioning system based on multi-sensor fusion, such as Figure 4 As shown, it includes dividing the sub-areas and deploying sensor modules, data acquisition modules, data processing modules, fusion algorithm modules and coordinate output.

[0256] In dividing the sub-areas and deploying the sensor modules, assuming that the area of ​​the positioning area is S, the area is divided into n sub-areas according to the size of the area to be positioned. In this embodiment, the present invention is described in detail by taking the area to be measured as divided into 4 sub-areas as an example, and the layout method can be expanded and spliced ​​and applied to a larger area to be positioned.

[0257] In the data acquisition module, the Wi-Fi access point and the receiving device collect the channel status information of the Wi-Fi signal, including the amplitude and phase changes of each subcarrier; the PIR sensor collects the infrared radiation intensity signal and outputs a voltage signal that changes with the movement of the person; the ultrasonic sensor transmits and receives ultrasonic signals, measures the time difference of the reflected signals, and collects the signal reflecting the distance between the person and the sensor.

[0258] In the data processing module, the CSI data processing process of Wi-Fi is as follows: first, the CSI signal is de-noised by principal component analysis, the main change components are extracted, and irrelevant noise is removed; secondly, the CSI amplitude is normalized so that the signal remains consistent under different time and environmental conditions; finally, the CSI signal is converted to the frequency domain, and the frequency characteristics of the CSI signal over time are extracted by spectrum analysis. The PIR signal processing process is as follows: the voltage peak and climbing slope of the PIR sensor are analyzed by the dual dynamic threshold method to detect human activities of different intensities. The ultrasonic data processing process is as follows: the distance between the person and the sensor is calculated by measuring the time difference of ultrasonic propagation. Multiple ranging data are smoothed to reduce noise and errors.

[0259] In the fusion algorithm module, Kalman filtering is mainly used to fuse the data of PIR sensors and ultrasonic sensors, combining centroid positioning and precise ranging results. First, the centroid position is calculated based on the signal strength of the PIR sensor to provide a rough position estimate of the person in the sub-area, and then the centroid coordinates are calculated by weighted average. Secondly, the ranging data of multiple ultrasonic sensors are used to calculate the precise position of the person through triangulation. In the Kalman filtering process, the state variables of the target, including position and velocity, are defined, and the current position of the target is predicted based on the previous state estimate (including position and velocity) through the prediction model. The rough centroid position provided by PIR and the precise result of ultrasonic ranging are fused through Kalman gain. Combined with the measurement results of different sensors, the state estimate is updated and the precise coordinates after fusion are output.

[0260] After processing by the fusion algorithm, the system finally outputs the precise coordinates of the person. Combining PIR centroid positioning and ultrasonic ranging, the fused person's position is output in the form of two-dimensional coordinates. And as the person moves, the system continuously updates the person's location information to ensure that the system can provide the person's precise location in real time.

[0261] Verification test:

[0262] In order to compare the difference between the measured value and the true value and calculate the error, in this embodiment, the Euclidean distance formula is used to calculate the error between the measured value and the true value in two-dimensional space. For a two-dimensional coordinate (x, y), assuming that the true coordinate is (x t ,yt ), the measured coordinates are (x m ,y m ), then the error E is calculated as follows:

[0263]

[0264] Table 1 shows the improvement of positioning accuracy and the reduction of positioning error achieved by the indoor positioning system.

[0265] Table 1

[0266]

[0267] By comparing the table, we can clearly see the step-by-step improvement of the personnel positioning accuracy of the method of the present invention, which better demonstrates the coordinate estimation of each step and the changing trend of the positioning error, and helps to quantitatively analyze the improvement of the positioning accuracy of the entire system.

[0268] The indoor positioning method and system based on multi-sensor fusion realizes a passive indoor positioning technology through multi-level positioning and multi-sensor fusion, avoiding the process of the person being located carrying an electronic tag. The feasibility of this method is further verified by the indoor positioning system. The comparison of the relevant indicators of the single method and the method proposed by the present invention is shown in Table 2:

[0269] Table 2

[0270]

[0271]

[0272] From this comparison table, it can be clearly seen that the method of the present invention has significant improvements in positioning accuracy, response time, system robustness and adaptability compared with the prior art, and can better meet the actual application requirements of indoor positioning systems.

[0273] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An indoor positioning method based on multi-sensor fusion, characterized in that: The following steps are involved: Step 1, deploy Wi-Fi transceiver equipment and various sensors: deploy Wi-Fi signal transceiver equipment, PIR sensors and ultrasonic sensors in the area to be located, where the Wi-Fi signal transceiver equipment is used to provide overall wireless signal coverage, the PIR sensor is used to detect human infrared radiation to sense human activities, and the ultrasonic sensor is used to measure distance; Step 2: Wi-Fi CSI test area range detection: collect CSI information, pre-process the CSI signal, extract the main change components of the CSI data, and perform maximum value normalization. Observe the frequency characteristics of signal amplitude changes through short-time Fourier transform, extract features of phase difference, build a training data set and train the SVM classification model to distinguish the activity status of people in the environment. Step 3, detailed detection of the sub-area by the PIR sensor: according to the voltage signal of the PIR sensor, the voltage peak and climbing slope characteristics are extracted, and the double dynamic threshold method is used to judge the movement state of the person and determine the person's activities in the sub-area; Step 4: Calculate the centroid coordinates: Based on the signal strength of multiple PIR sensors, calculate the relative position (x PIR ,y PIR ); Step 5: Ultrasonic ranging precise positioning: Using the known coordinates of the ultrasonic sensor and the measured distance value, the precise position of the person (x ultra ,y ultra ); Step 6, fusion sensor positioning results: obtain PIR centroid positioning coordinates and ultrasonic ranging coordinates, define state variables and observation vectors, perform state prediction and update through Kalman filtering, calculate Kalman gain, combine observation results with predicted values, correct the current coordinates and speed of the target, and update the error covariance matrix, and finally output the fused target position (x k ,y k ).

2. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 1, a modular splicing method is used to deploy sensors and divide sub-areas so as to overlap the detection areas of the sensors.

3. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 2, the CSI information includes: Among them, H(f i ) is the CSI value of the i-th subcarrier; |H(f i )| is the amplitude of CSI; θ(f i ) is the phase of CSI; f i is the frequency of the ith subcarrier; Preprocess the CSI signal: Use the PCA principal component analysis method to extract the main change components in the CSI information, and decompose the data into multiple principal components through PCA. The formula is as follows: X=UΣV T ; Where X is the CSI data matrix; U and V are eigenvector matrices respectively; T represents the transpose operation of the eigenvector matrix; Σ is the singular value matrix, representing the principal components of the signal; Perform maximum normalization on the CSI amplitude: Among them, |H norm (f i ,t)| is the normalized CSI amplitude value, indicating that at frequency f i The result after normalization of the CSI amplitude at time t; |H(f i ,t)| is the original CSI amplitude value, indicating that at frequency f i and the unnormalized CSI amplitude information obtained from the Wi-Fi device at time t; max(|H(f i ,t)|) is the maximum value of the CSI amplitude at all frequencies and time points of the current CSI data, which is used as the normalization benchmark; The frequency characteristics of signal amplitude changes at different times are obtained by short-time Fourier transform STFT; the CSI amplitude signal is converted from the time domain |H norm (f i ,t)|convert to the frequency domain S(f,t), using the formula: Where S(f,t) represents the CSI time-frequency characteristic value at frequency f and time t, which is used to reflect the change of CSI amplitude with frequency; -j2πfτ is the kernel function of Fourier transform, where j is the imaginary unit, π is a mathematical constant, τ is the integral variable, and dτ represents the differential element of the integral; Extracting phase difference features; The training data set is constructed by extracting the amplitude change frequency features and phase difference features for training the classification model. The SVM classifier is trained using the extracted CSI features. During the training process, the labeled CSI feature data is used to distinguish "personnel activity" or "no personnel activity". The SVM classifier model is as follows: f(x)=w T φ(x)+b; Among them, f(x) represents the predicted output of input x; x is the input feature, namely the amplitude change and phase difference features; w is the weight of the SVM model; φ(x) represents the feature vector obtained after the input x passes through the feature mapping function φ; b is the deviation.

4. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 3, Set voltage peak threshold: Dynamically adjust the voltage peak detection threshold V according to the ambient noise level and typical human activity signals th,peak ; When the voltage peak of the sensor exceeds the set threshold, it indicates that there is human activity in the area; the voltage peak judgment conditions are as follows: V peak >V th,peak ; Among them, V peak is the peak voltage output by the PIR sensor; V th,peak It is the voltage peak threshold that is dynamically adjusted according to environmental conditions; Set the climbing slope threshold: Calculate the voltage climbing slope by sampling the voltage signal change rate of the sensor: Where dV is the voltage increment; dt is the time increment; Dynamically set the slope threshold α according to different exercise intensities th,slope ; When the slope of the voltage change exceeds the threshold, there is a strong motion state; the climbing slope determination conditions are as follows: α slope >α th,slope ； Among them, α slope is the voltage climbing slope currently measured; α th,slope It is the slope threshold that is dynamically adjusted according to the intensity of exercise; The two dynamic thresholds of voltage peak value and climbing slope are combined to judge the activity of personnel; when the following conditions are met, it is determined that there are personnel activities in the sub-area:

5. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 4, when a person enters the detection area of ​​the PIR sensor, the PIR sensor will output a signal strength S related to the distance between the person and the sensor. i , n PIR sensors are arranged in a sub-area, and the sensor coordinates are (x PIRSi ,y PIRSi ), where i = 1, 2, ..., n; The signal strength of each PIR sensor is used as a weight, and the relative position of the person is calculated through weighted average based on the spatial position of the sensor; Estimate the centroid coordinates using a weighted average method: Among them, w i is the signal strength of the ith PIR sensor; x PIR and PIR is the coordinate of the centroid of the person in the sub-area, i.e. the estimated relative position; x PIRSi and PIRSi is the coordinate of the i-th sensor; ∑w i x PIRSi and ∑w i y PIRSi Indicates that the coordinate x of each point PIRSi and PIRSi With its weight w i The result of multiplication and summation; ∑w i represents the sum of all weights and is the normalization factor; The relative position of the personnel in the sub-area (x PIR ,y PIR ).

6. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 5, for the sub-area where the person is located, the coordinates of each ultrasonic sensor are marked as (x UltraSi ,y UltraSi ), where i = 1, 2, 3; use triangulation to calculate the precise coordinates of the person through the distance measurements of multiple ultrasonic sensors; let the actual position of the person be (x ultra ,y ultra ), combine the following equations to solve the coordinate (x ultra ,y ultra ): The distance d1 obtained from ultrasonic sensor 1: The distance d2 obtained from ultrasonic sensor 2: The distance d3 obtained from ultrasonic sensor 3: The solution is obtained by numerical methods (such as Newton iteration method or least square method); the precise position of the personnel in the sub-area (x ultra ,y ultra ).

7. The indoor positioning method based on multi-sensor fusion according to claim 1, characterized in that: In step 6, Define the state variable X of the target k for: Among them, x k and k is the two-dimensional coordinate position of the target at time k; v x and v y is the velocity of the target in the x and y directions. Define the observation vector as Z k : in, and is the relative position provided by the PIR centroid positioning at time k; and is the precise position provided by ultrasonic ranging at time k. The prediction model of Kalman filter is based on the state X at the previous moment. k-1 Calculate the target position and speed at the current moment; The state transfer equation is defined as: X k|k-1 =F·X k-1 +B·U k ; Among them, X k|k-1 is the current state predicted based on the state at the previous moment k; B is the control matrix, which can be ignored if there is no control input; U k is the control input vector, which can be ignored if there is no control input; F is the state transfer matrix, assuming that the position changes steadily over time: Calculate the Kalman gain: Kalman gain K k Defined as: K k =P k|k-1 H T (HP k|k-1 H T +R) -1 ; Among them, P k|k-1 is the predicted state error covariance matrix, which represents the uncertainty of the predicted value at the current moment; R is the measurement noise covariance matrix; H is the observation matrix: State update: After calculating the Kalman gain, the observation result Z is updated k With the predicted value X k|k-1 Combined with the state update, the state update equation is: X k =X k|k-1 +K k ·(Z k -H·X k|k-1 ); Among them, Z k is the measurement result of PIR and ultrasonic sensor; H·X k|k-1 is the observed value obtained from the prediction; Update the state error covariance matrix P k , the error covariance update equation is: P k =(I-K k H)P k|k-1 ; Where I is the identity matrix; Output the fused target position (x k ,y k ).

8. An indoor positioning system based on multi-sensor fusion, used to execute the indoor positioning method based on multi-sensor fusion as claimed in any one of claims 1 to 7, characterized in that: include: Divide the sub-areas and deploy sensor modules. Set the area of ​​the positioning area to S, and divide the area into n sub-areas according to the size of the area to be positioned. The data acquisition module includes the Wi-Fi access point and the receiving device to collect the channel status information of the Wi-Fi signal, including the amplitude and phase changes of each subcarrier; the PIR sensor collects the infrared radiation intensity signal and outputs the voltage signal that changes with the movement of the person; the ultrasonic sensor transmits and receives the ultrasonic signal, measures the time difference of the reflected signal, and collects the signal reflecting the distance between the person and the sensor; Data processing module, used to process the channel status information of Wi-Fi signals, voltage signals of PIR sensors, and ultrasonic signals; The fusion algorithm module uses Kalman filtering to fuse the data of the PIR sensor and the ultrasonic sensor, defines the state variables and observation vectors, predicts and updates the state through Kalman filtering, calculates the Kalman gain, combines the observation results with the predicted values, corrects the current coordinates and speed of the target, and updates the error covariance matrix, and finally outputs the fused target position.

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