CN112423387A

CN112423387A - Indoor positioning method for terminal equipment with multiple sensor fusion

Info

Publication number: CN112423387A
Application number: CN202011325893.9A
Authority: CN
Inventors: 刘秀萍; 王程
Original assignee: Individual
Current assignee: Individual
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2021-02-26

Abstract

The invention provides an indoor positioning method of terminal equipment with multiple sensor fusion, which aims at solving the problem that the prior art does not have an indoor positioning method which can simultaneously meet the requirements of high precision, high stability, low cost and convenience, provides a combined method based on sensor and wireless local area network positioning through a sensor of an intelligent equipment terminal and an indoor positioning technology based on a wireless local area network, effectively inhibits the positioning result distortion phenomenon caused by signal disturbance in a K value nearest neighbor algorithm, is an indoor positioning method with remarkable innovativeness and outstanding advantages, adopts Kalman filtering to analyze and fuse data, has the advantages of strong fault tolerance, high intelligent degree, high real-time dynamic property, high positioning speed, high positioning precision and the like, is favorable for truly realizing indoor and outdoor seamless positioning and butt joint, and has high utilization value in practical application.

Description

Indoor positioning method for terminal equipment with multiple sensor fusion

Technical Field

The invention relates to a terminal indoor positioning method, in particular to a terminal equipment indoor positioning method with multiple sensors fused, and belongs to the technical field of indoor positioning methods.

Background

Currently, the information revolution represented by the internet and IT technology is rapidly spreading worldwide, and human beings have entered the information age. The information influences the aspects of social life, and the position information is a crucial item in the information. Since the 80 s of the twentieth century, the use of GPS has advanced dramatically in GNSS technology, and a very complete outdoor positioning system has been formed, which can basically meet various outdoor positioning requirements of people. However, since GNSS positioning relies on satellite signals, in indoor environments, GNSS signals are severely interfered or shielded and cannot be used for precise positioning at all. Therefore, the indoor positioning technology becomes a hotspot for research and application, and is called as the last kilometer technology for solving accurate positioning.

The development of internet technology has resulted in more advanced mobile electronic devices. In the internet era, all information is transmitted on the internet, the internet greatly reduces the transmission cost of the information, the information is explosively increased, all activities of the economic society are closely connected through the internet, and concepts such as the internet of things and the like are carried forward. The combination of intelligent equipment and the internet enables intelligent hardware to gradually become a hotspot for research and application, and particularly along with the rapid development of mobile internet, the popularization and the use of smart phones enable the demand for acquiring daily information, particularly position information, of people to be vigorous. Indoor positioning by using the smart phone and the terminal also becomes a popular research and development and application field, and the service based on the position is more and more concerned, so that a series of commercial and public services based on indoor accurate positions, such as smart home, smart shopping mall, and smart hospital navigation, are formed.

The application scenes of indoor positioning are diversified, and the indoor positioning system relates to multiple aspects including business, home, travel, personal and public safety, public service and the like. In the commercial field, aiming at the complex situation of a large-scale market, indoor positioning information can be utilized for navigation and shopping guide; for the online-to-offline business mode, accurate advertisement putting can be carried out on the user after the position information of the user is known; in the personal safety field, especially the searching of lost children and old people, the indoor positioning technology can better track the position of personnel, inform the family members in time and play a good role in protecting the personal safety. In the traveling process, automatic parking can be carried out by utilizing indoor positioning; in public service, hospitals are typical application scenes, complex floors of departments of hospitals are numerous, indoor positioning is utilized to help patients and relatives to find ways better, monitoring of the patients is well achieved, old people are monitored in nursing homes, position information of the old people can be utilized to find out emergencies in time, and danger and rescue can be found out in time. In conclusion, the indoor positioning has wide application prospect, and can bring many benefits to the daily life and public social service of people.

The indoor positioning in the prior art is mainly divided into: AGPS positioning technology, wireless positioning technology (ultrasonic, radio frequency wireless label, infrared, wireless local area network signal, UWB positioning technology, etc., other positioning technology (computer vision identification, geomagnetic navigation, etc.). The wireless positioning technology is the mainstream of the current indoor positioning technology, the wireless positioning principle is generally divided into an arrival time method, an arrival time difference method, an arrival angle method and a signal intensity method, the first three methods are positioning methods based on geometric intersection, have high requirements on time and angle mapping, are not ideal in positioning effect, the method based on signal intensity is a hotspot researched and developed at present, the signal intensity method is divided into a strongest base station method, a base station with strongest signal nearby is selected as an approximation of the self position, a propagation model method, a propagation model is established by utilizing the propagation rule of the signal in space, so that the self position is calculated, a position fingerprint method, the method is a scene analysis method and comprises two stages of off-line library establishment and on-line positioning, and the basic idea is that the distribution rule of signals in an approximate scene is also approximate, so that the collected signal characteristics can be matched to obtain the position of the self-body. The storage mode of the fingerprint database can be divided into a numerical mode and a probability distribution mode, wherein the numerical mode stores the average value of the signal intensity within a certain time, and the probability distribution mode stores the probability distribution of the signal intensity within a certain time.

The indoor positioning method in the prior art mainly has the following defects to be improved:

first, indoor positioning puts higher demands on positioning accuracy, dynamic response capability, and stability, and the single positioning mode and the single sensor in the prior art have been unable to meet the demands. With the development of the micro-electro-mechanical system, a plurality of types of sensors are deployed on terminal equipment in a large scale, and favorable conditions are provided for analyzing and fusing data of the plurality of types of sensors. The analysis and fusion of the data of various sensors is to comprehensively process the sensor data of different information sources meeting the conditions through certain standards to obtain more accurate description of the target. The indoor positioning method in the prior art is characterized in that the improvement points are continuously shown, the system reliability is improved, the space-time coverage range is enlarged, the system information content is increased, the system stability is improved, the information certainty is enhanced, the fuzziness, the randomness and the uncertainty of system output are reduced by adopting data fusion, and the decision correctness is improved;

secondly, an indoor positioning method which can simultaneously meet the requirements of high precision, high stability, low price and convenience does not exist in the prior art, a sensor passing through an intelligent device terminal and an indoor positioning technology based on a wireless local area network are lacked, and a combined method based on the sensor and the wireless local area network positioning is lacked, so that the prior art has poor efficiency and accuracy, lacks of an indoor positioning technology which meets the requirements of high precision, stability and convenience, cannot really realize indoor and outdoor seamless positioning and butt joint, and has low utilization value in practical application;

thirdly, in the indoor positioning method for matching wireless local area network fingerprints in the prior art, due to disturbance of wireless network signals, indoor wireless network signal distribution is different from signal distribution during library building, even if K nearest fingerprint points are determined, the condition that the difference between the coordinates of some points in the K fingerprint points and actual coordinates is too large cannot be eliminated, the positioning result is distorted due to the condition, the positioning accuracy is seriously affected, the industrial requirements cannot be met, and meanwhile, the defects of weak interaction performance, low intelligent degree, low positioning speed, low positioning accuracy and the like exist;

fourthly, the application scenario of indoor positioning determines that a user cannot wait for obtaining a positioning result for a long time, the positioning process is real-time dynamic, when the equipment terminal is in motion, a wireless local area network signal is more easily interfered by environmental changes, the effect of real-time updating of the position by simply utilizing the wireless local area network positioning in the prior art is not ideal, different error sources of different positioning modes are not adopted, data analysis and fusion are carried out, a part of errors are eliminated, the positioning result is accurate and poor in stability, the fault tolerance performance is poor, the real-time dynamic performance is low, the positioning speed is low, and the positioning precision is low.

The invention provides a combined indoor positioning technology based on multiple sensors of terminal equipment, and improves the precision and dynamic response capability of indoor positioning.

Disclosure of Invention

The indoor positioning method for the terminal equipment with the fusion of the multiple sensors, provided by the invention, has the advantages that: firstly, the reliability of the system is improved, and if a certain sensor fails or the target cannot be mapped due to interference and other reasons, other sensors can still provide effective information; secondly, the space-time coverage range is enlarged, and due to the existence of a plurality of sensors, the targets are observed at different time, places and visual angles, so the space-time working space of the system can be expanded; thirdly, the system information amount is increased, and as a plurality of types of sensors distributed on a frequency domain, a time domain and a space are adopted, the target information is complemented at different levels, and the system error is weakened or even eliminated to a certain extent; fourthly, the system stability is improved, multiple sensors are adopted for observation, and system redundancy and information redundancy are achieved, so that the system stability is enhanced; fifthly, the certainty of the information is enhanced, the fuzziness, the randomness and the uncertainty of the system output are reduced by adopting data fusion, and the correctness, the reliability and the superiority of the decision are improved.

In order to achieve the technical effects, the technical scheme adopted by the invention is as follows:

a terminal equipment indoor positioning method with multiple sensor fusion sequentially provides two multi-sensor terminal indoor positioning methods with progressive relation through a sensor of an intelligent equipment terminal and an indoor positioning technology based on a wireless local area network, and is a multi-sensor fusion positioning method matched with line segments and a Kalman filtering positioning method fused with a track and the wireless local area network respectively, data analysis and fusion are carried out at a feature level, data representing an object state and data representing an object feature are associated and registered, dimensionality describing the object state and the feature is increased, a combined feature state or a feature vector is obtained, related target knowledge is enabled to be more accurate and complete, the robustness of a system is improved by utilizing the information redundancy of the system, and errors of a single method are compensated by the information fusion of multiple information sources;

processing initial sensor data by utilizing high-pass filtering, low-pass filtering and Kalman filtering on a sensor system of an android platform, determining the direction of an equipment terminal, obtaining an accurate motion track of the equipment terminal, determining the track by adopting a two-step method, judging the direction to determine the track type in the first step, detecting the distance in the second step, determining track displacement, and finally calculating to obtain a track vector; the invention adopts a wireless local area network positioning method with self-adaptability, and a dynamic K value nearest neighbor improved algorithm is used as a wireless local area network positioning method in the combination fusion positioning of a plurality of sensors, the method is improved on the basis of the K value weighted nearest neighbor algorithm, the selection of the K value is more adaptive, and the fingerprint point information is utilized as much as possible;

the invention provides a multi-sensor fusion positioning method for line segment matching, which is a multi-sensor fusion positioning method for determining line segment matching with a wireless local area network based on a track, wherein a wireless local area network signal is compared with a fingerprint library to determine nearest fingerprint points at the head end and the tail end of a straight line to form a fingerprint matching space, the fingerprint points in the head fingerprint space and the tail fingerprint space are paired in pairs respectively to obtain a matching space of a fingerprint vector, the vectors at the head end and the tail end can also be obtained through track determination, so that a vector constraint condition is obtained, the fingerprint vectors in the fingerprint vector matching space are screened through the constraint condition to obtain m pairs of fingerprint vectors which most accord with a motion track, and finally, the coordinates of two end points on the straight line are obtained through weighted average of the fingerprint points at the head end and the tail end;

the invention further provides a Kalman filtering positioning method for fusing the track and the wireless local area network, which comprises the steps of setting an initial value, determining the track to obtain a continuous track, constraining the track by using a positioning result of the wireless local area network, estimating the position by using Kalman filtering, estimating the position by using the track determination, taking the positioning result of the wireless local area network as an observed value, and estimating the position in real time during movement.

The invention provides a terminal equipment indoor positioning method with multiple sensors fused, and further provides a line segment matching multi-sensor fusion positioning method, wherein the method comprises the steps of determining the motion track of equipment by using multiple sensors, obtaining effective equipment terminal motion information by filtering and calculating sensor data, and providing the information of the change of an equipment terminal in space by using the motion information; the wireless local area network positions the spatial position of the computing equipment terminal on a certain point, the sensor and the wireless local area network provide two pieces of information about the spatial position, the redundancy of the information is utilized, the error generated by a single positioning method is inhibited, and the precision and the reliability of indoor positioning are improved; the line segment matching is started from the simplest straight line, and the position is determined by utilizing the analysis and fusion of various sensor data;

matching the head end and the tail end of a straight line, when the equipment terminal advances on the straight line, obtaining a linear motion vector of the equipment terminal according to a result determined by a track, wherein the vector is characterized by relative displacement in the x and y directions and orientation information of the straight line, and obtaining J1 and J2 pieces of nearest fingerprint point information respectively determined by the equipment terminal at the head end and the tail end of the line through calculation of a dynamic K value nearest improved algorithm; when the equipment terminal moves linearly, the relative position of the equipment terminal on the straight line is accurate, and the relative position information can be used as matched characteristic information.

The mapping result of the sensor and the positioning process of the wireless local area network are fused, the fingerprint points are further matched and screened by utilizing the relative position information, the fingerprint points are pairwise matched to calculate the relative positions, several groups of fingerprint points with the relative positions closest to the matching characteristics are selected as the nearest fingerprint points, and finally the final positioning result is obtained by weighted averaging.

The invention provides a dynamic K value nearest neighbor improved algorithm as a wireless local area network positioning algorithm, a positioning result is obtained by weighting K fingerprint point positions with the nearest Euclidean distance, an empirical value is set for determining the K value, and the positioning precision of the wireless local area network is influenced by the size of the K value;

the method comprises the steps of dynamically determining the size of a K value through a nearest neighbor improvement algorithm of the dynamic K value, carrying out preliminary detection on nearby fingerprint points by setting a critical value of Euclidean distance, averaging Euclidean distances of preliminary detection results, taking an average value as a judgment condition of the nearest fingerprint points, adding the Euclidean distance to the nearest fingerprint points when the Euclidean distance is smaller than the average value, finally obtaining K nearest fingerprint points, and utilizing information of the nearby fingerprint points as much as possible;

when a critical value is used for screening a K value, a correlation coefficient is introduced for constraint; the correlation coefficient reflects the correlation between two signal intensity sequences, the correlation coefficient method can judge the correlation between two points, irrelevant fingerprint detection points can be effectively removed by constraining through the correlation coefficient, and the calculation formula of the correlation coefficient is as follows:

wherein x and y represent undetermined point and fingerprint point respectively, x_k、y_kRespectively represent the RSS values of the kth AP,

respectively representing the mean values of the AP sequence of the undetermined point and the AP sequence of the fingerprint point; the process of constraining with the correlation coefficient is: when the Euclidean distance of the fingerprint points is smaller than the critical value, a critical value of a correlation coefficient is set at the same time, only when the Euclidean distance and the correlation coefficient of the fingerprint points are in the corresponding critical values, the fingerprint points are considered to be the closest points, points which are close to the Euclidean distance but not related are removed, and the stability and the reliability of the system are improved.

The indoor positioning method of the terminal equipment with the fusion of various sensors is further characterized in that in the step of the multi-sensor fusion positioning method with line segment matching, the line segment matching is a data analysis and fusion process, a motion track can be planned into a plurality of straight-line paths, and the coordinates of all passing points on the motion track can be obtained by only performing line segment matching on each path during matching;

the initial data values input by the method are as follows: the method comprises the steps of firstly, an acceleration queue, secondly, a direction queue and thirdly, a wireless network signal queue; the acceleration queue is an acceleration queue preprocessed by accelerometer data, the direction queue is a direction queue obtained by analyzing, fusing and filtering data according to the direction determined by a gravity sensor and a magnetic sensor and the direction determined by a gyroscope, and the wireless network queue is a wireless network signal strength value queue obtained by synchronous sampling in the walking process.

The indoor positioning method of the terminal equipment with the fusion of various sensors further comprises the following steps of time domain modification and division: the time domain division is carried out on the three sensor queues, the data obtained by mapping the three sensors are ensured to be in the same time domain, the division is carried out according to the fact that the index of the track changing point obtained by the direction queue is used as a discontinuous point for dividing the time domain, and the time domain is modified.

The indoor positioning method of the terminal equipment with the fusion of various sensors comprises the following steps of: traversing each track, determining a matching result on the track, and finally storing the track detected by the direction and the acceleration into a linear form, wherein each linear form is characterized by the length and the direction of the linear form;

when traversing the ith track, extracting information of wireless networks in the ith time period, and matching the head and tail signal strengths of the ith wireless network queue as the starting and stopping point signal strengths, wherein the matching method adopts a dynamic K value nearest improved algorithm, and J1 and J2 fingerprint points which are nearest to the starting and stopping points are obtained through calculation, wherein J1 is J1 fingerprints which are nearest to the starting position, and J2 is J2 fingerprints which are nearest to the stopping position;

after obtaining J1 and J2 corresponding fingerprints, pairwise matching J1 and J2 fingerprint points of the starting point and the ending point into fingerprint point vectors, wherein each fingerprint in J1 fingerprints of the starting point is matched with J2 fingerprints of the ending point to finally form J1 × J2 pair fingerprint vectors.

After obtaining the fingerprint vector pair, creating a matching space, wherein certain vectors are closest to the real motion trail in the fingerprint vector pair, so that n pairs of fingerprint vectors can be determined only by giving the closest judgment basis;

respectively carrying out weighted average on the coordinates of the start point and the stop point of the n vectors to obtain the coordinates of the start point and the stop point, taking the weighted average of the coordinates of the start point of all the vectors as the coordinates of the start point of the track, taking the weighted average of the coordinates of the stop point of all the vectors as the coordinates of the stop point of the track, determining the weight of the weighted average by the Euclidean distance, matching the next section of track after determining the coordinates of the current track, wherein the matching process is the same as the above process, and thus matching the m tracks and finally outputting the coordinates of the start point of the m tracks, namely the whole process of line segment matching.

The invention further provides a Kalman filtering positioning method for fusing a track and a wireless local area network, aiming at different error sources of different positioning modes, the Kalman filtering positioning method for fusing the track and the wireless local area network can be used for analyzing and fusing data so as to reduce a part of errors to a great extent, so that the positioning result is more accurate and stable, and the Kalman filtering is used for analyzing and fusing data;

the track positioning and the wireless local area network positioning reflect the spatial position value of the same place at the same time, but the position estimation values of the two methods are not consistent, the track positioning result is used as a predicted value, the wireless local area network positioning result is used as an observed value, and a filter is initialized at a given starting point, so that a filtering value can be obtained at each time, the value is used as an estimation value of the terminal position of equipment, and the concept framework of Kalman filtering for the track and the wireless local area network positioning is realized.

The indoor positioning method of the terminal equipment with the fusion of various sensors further comprises the following steps of: the method comprises the following steps that position information can be obtained by a track and a wireless local area network, a position determined by the track is used as a state prediction value, a position determined by the positioning of the wireless local area network is used as an observed value, Kalman filtering is carried out, empirical values are adopted for state prediction model noise and process noise in a filtering equation, and the filtering process is first-order linear;

the position is updated by Kalman filtering by using a track and a wireless local area network, and the updating time of the invention is selected by the following three methods:

the method comprises the steps that position updating is carried out according to track types, and the filtering time selected in the form is that the continuity of the track detected by a sensor is reserved to the greatest extent when the track changes every time, and the accuracy of the sensor is depended on;

the second method is that the position is updated according to the change of the wireless local area network signal, and the position is updated when the wireless local area network signal changes greatly each time;

and a third method, namely position updating with a fixed sampling rate, wherein the position updating mode adopts the fixed sampling rate and updates the position once every sampling time interval.

The invention discloses an indoor positioning method of terminal equipment with multiple sensors integrated, and further discloses three updating time selection methods, wherein the three updating time selection methods are consistent with the basic process of a method for performing Kalman filtering by using a track determination and a wireless local area network, and the first method is recommended to be adopted for updating the position according to the track type.

Compared with the prior art, the invention has the following contributions and innovation points:

firstly, the indoor positioning method of the terminal equipment with the fusion of the multiple sensors, provided by the invention, aims at the higher requirements of indoor positioning on positioning precision, dynamic response capability and stability, and the single positioning mode and the single sensor can not meet the requirements, the data of the sensors of different information sources meeting the conditions are analyzed and fused by adopting the data of the multiple sensors, and the more accurate description of the target is obtained through certain standard comprehensive processing; the advantages of the invention for analyzing and fusing the data of various sensors are as follows: firstly, the reliability of the system is improved, and if a certain sensor fails or the target cannot be mapped due to interference and other reasons, other sensors can still provide effective information; secondly, the space-time coverage range is enlarged, and due to the existence of a plurality of sensors, the targets are observed at different time, places and visual angles, so the space-time working space of the system can be expanded; thirdly, the system information amount is increased, and as a plurality of types of sensors distributed on a frequency domain, a time domain and a space are adopted, the target information is complemented at different levels, and the system error is weakened or even eliminated to a certain extent; fourthly, the system stability is improved, multiple sensors are adopted for observation, and system redundancy and information redundancy are achieved, so that the system stability is enhanced; fifthly, the certainty of the information is enhanced, the fuzziness, the randomness and the uncertainty of system output are reduced by adopting data fusion, and the correctness, the reliability and the superiority of decision making are improved;

secondly, aiming at the fact that an indoor positioning method which is high in precision, high in stability, low in cost and convenient does not exist in the prior art, a sensor of an intelligent device terminal and an indoor positioning technology based on a wireless local area network are used, a combination method based on the sensor and the wireless local area network is provided, and experimental results prove that the method is high in efficiency and accuracy, is an indoor positioning technology which meets high precision, is stable and convenient, is beneficial to truly realizing indoor and outdoor seamless positioning and butt joint, and has high utilization value in practical application;

thirdly, the indoor positioning method of the terminal device with multiple sensor fusion provided by the invention aims at the indoor positioning method of the wireless local area network fingerprint matching in the prior art, because the wireless network signal has disturbance, the indoor wireless network signal distribution is different from the signal distribution when the database is built, even if K nearest fingerprint points are determined, the situation that the difference between the coordinates of some points in the K fingerprint points and the actual coordinates is too large cannot be eliminated, the situation can cause the distortion of the positioning result, and the positioning precision is seriously influenced, the invention provides the multi-sensor fusion positioning method of line segment matching with multiple sensor data analysis fusion, which effectively inhibits the distortion phenomenon of the positioning result caused by the signal disturbance in the nearest algorithm of the K value, and is an indoor positioning method with remarkable innovation and outstanding advantages;

fourthly, the indoor positioning method of the terminal equipment with the multiple sensor fusion provided by the invention determines that a user cannot wait for obtaining a positioning result for a long time aiming at an application scene of indoor positioning, the positioning process is real-time dynamic, when the equipment terminal is in motion, a wireless local area network signal is more easily interfered by environmental changes, if the real-time updating effect of the position is not ideal by simply utilizing the wireless local area network positioning, the characteristic of good response of the sensor to the motion state enables the analysis and fusion positioning of the data of the multiple sensors to become an ideal scheme for determining the real-time dynamic problem of the indoor positioning, different error sources of different positioning modes are adopted for carrying out data analysis and fusion to reduce a part of errors, and the positioning result is more accurate and stable The intelligent degree is high, the real-time dynamic is high, the positioning speed is fast, the positioning accuracy is high, and the like.

Drawings

FIG. 1 is a diagram of the principle steps of the dynamic K-nearest neighbor improvement algorithm of the present invention.

Fig. 2 is a flow chart of the method for matching indoor positioning line segments of the terminal equipment.

FIG. 3 is a flow chart of a Kalman filtering positioning method with track and WLAN integration according to the present invention.

Detailed Description

The technical solution of the indoor positioning method for terminal equipment with multiple sensor fusion provided by the present invention is further described below with reference to the accompanying drawings, so that those skilled in the art can better understand the present invention and can implement the present invention.

Indoor positioning puts higher requirements on positioning accuracy, dynamic response capability and stability, and a single positioning mode and a single sensor cannot meet the requirements. With the development of the micro-electro-mechanical system, a plurality of types of sensors are deployed on terminal equipment in a large scale, and favorable conditions are provided for analyzing and fusing data of the plurality of types of sensors. The analysis and fusion of the data of various sensors is to comprehensively process the sensor data of different information sources meeting the conditions through certain standards to obtain more accurate description of the target. Analyzing and fusing various sensor data: firstly, the reliability of the system is improved, and if a certain sensor fails or the target cannot be mapped due to interference and other reasons, other sensors can still provide effective information; secondly, the space-time coverage range is enlarged, and due to the existence of a plurality of sensors, the targets are observed at different time, places and visual angles, so the space-time working space of the system can be expanded; thirdly, the system information amount is increased, and as a plurality of types of sensors distributed on a frequency domain, a time domain and a space are adopted, the target information is complemented at different levels, and the system error is weakened or even eliminated to a certain extent; fourthly, the system stability is improved, multiple sensors are adopted for observation, and system redundancy and information redundancy are achieved, so that the system stability is enhanced; fifthly, the certainty of the information is enhanced, the fuzziness, the randomness and the uncertainty of the system output are reduced by adopting data fusion, and the decision correctness is improved. Indoor positioning, especially indoor positioning based on terminal equipment, has ever-increasing practical value. The research on indoor positioning in the prior art tends to be mature, but no positioning method which can simultaneously meet the requirements of high precision, high stability, low cost and convenience exists at present. The invention provides a combined method based on sensor and wireless local area network positioning through the sensor of the intelligent equipment terminal and the indoor positioning technology based on the wireless local area network, and the experimental result proves the high efficiency and accuracy of the method.

The invention provides two multi-sensor terminal indoor positioning methods with progressive relation in sequence, namely a multi-sensor fusion positioning method matched with line segments and a Kalman filtering positioning method fused with a track and a wireless local area network, wherein the method comprises the steps of analyzing and fusing data at a feature level, associating and registering data representing object states and data representing object features, increasing dimensionalities for describing the object states and the features, and obtaining a combined feature state or a feature vector, so that related target knowledge is more accurate and complete. The invention finally tests and evaluates the precision of several indoor positioning methods of the terminal equipment, and evaluates the accuracy and effectiveness of the methods.

On a sensor system of an android platform, the method utilizes high-pass filtering, low-pass filtering and Kalman filtering to process initial data of a sensor, utilizes a sensor data analysis fusion method to determine the direction of an equipment terminal, obtains an accurate motion track of the equipment terminal, adopts a two-step method to determine the track, firstly carries out direction discrimination to determine the track type, secondly carries out distance detection to determine track displacement, and finally calculates to obtain a track vector; experiments prove that the detection method has higher precision and is very effective.

The invention adopts a wireless local area network positioning method with self-adaptability, and a dynamic K value nearest neighbor improved algorithm is used as a wireless local area network positioning method in the combination fusion positioning of a plurality of sensors, and the method is improved on the basis of the K value weighted nearest neighbor algorithm, so that the selection of the K value is more adaptive, the fingerprint point information can be effectively utilized as much as possible, and the positioning precision and stability are improved.

Based on the positioning of the sensor and the wireless local area network, the invention provides a positioning method with a plurality of sensor combinations, provides the necessity and the advantages of information fusion of a plurality of sensors, effectively improves the robustness of the system by utilizing the information redundancy of the system, and compensates the error of a single method by the information fusion of a plurality of types of information sources.

The invention provides a multi-sensor fusion positioning method for line segment matching, which is a multi-sensor fusion positioning method for determining line segment matching with a wireless local area network based on a track. The experiment of line segment matching verifies that the positioning precision can be effectively improved by utilizing the line segment matching, and the precision and the stability of the positioning result are obviously higher than the result obtained by simply utilizing the wireless local area network for positioning.

Since in the actual positioning process, the positioning is often dynamic. In this case, both the sensor and the wireless local area network signal generate a large error, a result of positioning by using the wireless local area network alone generates a large error, a result of positioning by using track dead reckoning alone generates track distortion, and a positioning error of a single sensor can be suppressed by using line segment matching. Therefore, the invention further provides a Kalman filtering positioning method fusing the track and the wireless local area network, the track can be determined to obtain a continuous track by setting an initial value, but the track is likely to generate larger distortion due to sensor errors, the track is constrained by using a positioning result of the wireless local area network, the position estimation can be well solved by using Kalman filtering, the position estimation is carried out by using the track determination, the positioning result of the wireless local area network is used as an observed value, and the position is estimated in real time during movement. Experiments prove that the Kalman filtering method of the track and the wireless local area network can obtain high precision and ensure the continuity of the track.

The invention is based on the positioning of the sensor of the terminal equipment and the wireless local area network, provides line segment matching and trace and wireless local area network man-filtering methods aiming at the trace state in indoor positioning, and both methods can obtain ideal effects.

Line segment matching multi-sensor fusion positioning method

In the indoor positioning method for matching the wireless local area network fingerprint in the prior art, the core idea of fingerprint matching is that under the condition of not changing indoor environment, the distribution of wireless local area network signals on the space is stable, and the approximation of the position is presumed by utilizing the approximation of the signal intensity; the nearest K fingerprint points can be obtained by utilizing a K value nearest algorithm, however, due to the fact that wireless network signals are disturbed, indoor wireless network signal distribution is different from signal distribution during library building in the positioning process, even if the K nearest fingerprint points are determined, the situation that the difference between the coordinates of some points in the K fingerprint points and actual coordinates is too large cannot be eliminated, and the situation can cause distortion of a positioning result and seriously affect positioning accuracy. Aiming at the situation, the invention provides a multi-sensor fusion positioning method for line segment matching of multiple sensor data analysis fusion, which effectively inhibits the positioning result distortion phenomenon caused by signal disturbance in a K value nearest neighbor algorithm.

Concept architecture

The motion track of the equipment is determined by utilizing various sensors, effective equipment terminal motion information is obtained through sensor data filtering and calculation, and the motion information can provide information of the equipment terminal changing in space; the wireless local area network positions the spatial position of the computing equipment terminal on a certain point, the sensor and the wireless local area network provide two pieces of information about the spatial position, and by utilizing the redundancy of the information, the error generated by a single positioning method can be effectively inhibited, and the precision and the reliability of indoor positioning are improved. Line segment matching starts with the simplest straight line and the location is determined using a variety of sensor data analysis fusion.

Firstly, considering the simplest condition, matching the head end and the tail end of a straight line, when an equipment terminal travels on the straight line, according to the result of track determination, obtaining the linear motion vector of the equipment terminal, wherein the vector is characterized by relative displacement in the x and y directions and the orientation information of the straight line, and obtaining the J1 and J2 pieces of nearest fingerprint point information respectively determined by the equipment terminal at the head end and the tail end of the line through the calculation of a dynamic K value nearest neighbor improved algorithm.

Because the sensor has errors, the result of the track determination is not a real motion track but an approximation of the real motion track; because of the disturbance of the wlan signal, the influence of the problematic fingerprint point among the K nearest fingerprint points cannot be completely eliminated.

In the track determination, the track is greatly influenced by the direction, the gyroscope is used for determining the direction, so that when the gyroscope is interfered or generates a large drift error, the whole curve is shifted, which is also a reason for generating a large error when long-time dead reckoning is carried out, but when the equipment terminal moves linearly, the relative position of the equipment terminal on the straight line is accurate, and the relative position information can be used as matched characteristic information.

Dynamic K value nearest neighbor improved algorithm

The invention provides a dynamic K value nearest neighbor improved algorithm as a wireless local area network positioning algorithm, wherein in the K value nearest neighbor algorithm, a positioning result is obtained by weighting the positions of K fingerprint points with the nearest Euclidean distance. However, for determining the K value, an empirical value needs to be set, and the size of the K value affects the accuracy of the wlan positioning.

Due to the complex indoor environment, signals are easily interfered, and it is difficult to find a standard K value suitable for all positioning processes. Therefore, an efficient method for determining the value of K must be found.

The algorithm carries out preliminary detection on nearby fingerprint points by setting a critical value of Euclidean distance, averages the Euclidean distance of preliminary detection results, takes the average value as a judgment condition of the nearest fingerprint point, adds the Euclidean distance into the nearest fingerprint point when the Euclidean distance is smaller than the average value, finally obtains K nearest fingerprint points, utilizes the information of the nearby fingerprint points as much as possible, and improves the positioning precision and reliability.

In order to increase the reliability of the system, when the critical value is used for K value screening, a correlation coefficient is introduced for constraint. The correlation coefficient reflects the correlation between two signal intensity sequences, and the critical value for the euclidean distance can only be restricted on the total euclidean distance, which may be the case when the total euclidean distance is close but not close to the point. The correlation coefficient rule can judge the correlation between two points, irrelevant fingerprint detection points can be effectively removed by constraining through the correlation coefficient, and the calculation formula of the correlation coefficient is as follows:

respectively represent the mean values of the AP sequence of the undetermined point and the AP sequence of the fingerprint point.

FIG. 1 is a diagram of the principle steps of a dynamic K-nearest neighbor refinement algorithm, and the process of constraint by using correlation coefficients is as follows: when the Euclidean distance of the fingerprint points is smaller than the critical value, a critical value of a correlation coefficient is set at the same time, only when the Euclidean distance and the correlation coefficient of the fingerprint points are in the corresponding critical values, the fingerprint points are considered to be the closest points, points which are close to the Euclidean distance but not related are removed, and the stability and the reliability of the system are improved.

(III) method step

The line segment matching is a data analysis and fusion process, is different from single-point wireless local area network positioning, the multi-sensor fusion positioning method for line segment matching effectively utilizes spatial information redundancy, and in the actual motion process, a motion track can be planned into a plurality of linear paths, so that during matching, only line segment matching needs to be carried out on each path, and finally the coordinates of all passing points on the motion track can be obtained. FIG. 2 is a specific method flow for line segment matching using data analysis fusion:

the initial data values input by the method are as follows: the method comprises the steps of firstly, an acceleration queue, secondly, a direction queue and thirdly, a wireless network signal queue; the acceleration queue is an acceleration queue preprocessed by accelerometer data, and the direction queue is a direction queue obtained by analyzing, fusing and Kalman filtering data according to the direction determined by a gravity sensor and a magnetic sensor and the direction determined by a gyroscope. The wireless network queue is a wireless network signal strength value queue obtained by synchronous sampling in the walking process.

1. Time-domain modification partitioning

The method comprises the specific steps that time domains of three sensor queues are divided, data obtained by mapping of three sensors are ensured to be in the same time domain, the division basis is that indexes of track changing points obtained by a direction queue are used as discontinuous points for dividing the time domains, but sampling rates of acceleration and direction are very high and can reach 30ms for once sampling, wireless network signals cannot be obtained through the high sampling rate, and therefore the time domains need to be modified.

2. Trajectory matching

Traversing each track, determining a matching result on the track, and finally storing the track detected by the direction and the acceleration into a linear form, wherein each linear is characterized by the length and the direction of the linear.

When traversing the ith track, extracting information of wireless networks in the ith time period, and matching the head and tail signal strengths of the ith wireless network queue as the signal strengths of the start point and the stop point, wherein the matching method adopts a dynamic K value nearest improved algorithm, and J1 and J2 fingerprint points which are nearest to the start point and the stop point are obtained through calculation, wherein J1 refers to J1 fingerprints which are nearest to the start position, and J2 refers to J2 fingerprints which are nearest to the end position.

After obtaining the fingerprint vector pairs, a matching space is created, some vectors are always closest to the real motion trail in the fingerprint vector pairs, so that n pairs of fingerprint vectors can be determined only by providing the closest judgment basis.

(IV) analysis of the results of the experiment

In order to verify the feasibility and the accuracy of line segment matching, a straight-line segment matching experiment is carried out, straight-line walking is carried out in a passageway in the experimental process, the coordinates of a walking starting point are (0, 2.9), the coordinates of a walking finishing point are (6.9, 2.9), the coordinate unit is meter, and the acceleration, the direction and a wireless network signal queue are respectively recorded in the walking process.

1. Track determination result

The method comprises the steps of determining the walking distance and direction in the time period by a track determining method through a direction queue and an acceleration queue, calculating track vectors to obtain components of the track on x and y axes, wherein the direction has a jump due to a data recording rule of a sensor, initial data of the direction is in an interval of (-180 degrees and 180 degrees), and when the direction is modified to 360 degrees, the jump exists near a zero value and needs to be processed.

For the jump of the direction near the zero value, an interval programming method is adopted, the interval programming method is to specify an interval in which the jump is possible to exist, the interval is respectively (340 degrees, 360 degrees) and (0 degree and 10 degrees), the direction is possible to jump in the two intervals, and the direction of the equipment terminal is not changed too much actually, therefore, the situation is respectively modified, if the direction falls in the jump interval and the change of the direction is larger than 340 degrees with the previous value, the jump is considered to be generated, and the jump result is modified to be in the same order of magnitude as the previous result. For example, a 350 degree jump to 10 degrees is modified to 350 degrees, and a 5 degree jump to 355 degrees is modified to 5 degrees by subtracting the degree of the jump from 360 degrees.

After the direction is modified, the direction is stabilized near a certain value in the linear motion, and the stable and accurate direction value can be obtained by averaging after the low-pass filtering is carried out on the direction queue. The error of the track determination is within 2m, and the motion state and the relative position information are well reflected, so that the fingerprint points positioned by the head-to-tail wireless local area network are restrained by using the relative position.

2. Selecting nearest neighbor points in fingerprint matching

And (3) utilizing the signal intensity obtained by the wireless local area network at the head and the tail of the track, and substituting the signal intensity into a dynamic K value nearest improved algorithm to obtain the fingerprint point nearest to the head and the tail. And matching the head and the tail of the fingerprint points, calculating the coordinate difference of the fingerprint points after matching, and enabling the fingerprint points with the coordinate difference within 2m to be included in the best matching vector.

3. Location estimation from nearest neighbor fingerprint points

And respectively carrying out weighted average on the head and the tail of the vector to obtain head and tail coordinates matched with the final line segment.

4. Positioning contrast with wireless local area network

And in comparison, the wireless network signals of the line segment matching head and tail are respectively subjected to dynamic K-value nearest neighbor improved algorithm wireless local area network positioning.

5. Analysis of results

Through a line segment matching experiment, the precision of a wireless local area network matching result at a straight line starting point is higher than that of a line segment matching result, the precision of a wireless local area network matching result at a line ending point is lower than that of a line segment matching result, and when the wireless local area network is matched and positioned, the positioning precision depends on the approximation degree of an acquired signal and a signal of a point to be measured when a library is built, so that for the positioning of the wireless local area network, a rough precision range is obtained, but the precision can not be guaranteed to be stabilized on a certain accurate value, because the change of the wireless local area network signal does not have a model suitable for all conditions, and the fluctuation and the interference of the signal are all the unstable factors of the positioning of the wireless local. Wireless local area network positioning can achieve very high accuracy but depends on the quality of the signal. The data analysis and fusion are carried out by utilizing various sensor combinations, so that the overall precision and stability of positioning can be effectively improved. The errors of the two positioning results matched by line segments are within 1m, which shows that the analysis and fusion of various sensor data are favorable for the stability of positioning, the positioning result can be constrained within a certain error range even if the signal quality of the wireless local area network is reduced, and the positioning precision is much higher than that of the positioning by the wireless local area network alone.

Kalman filtering positioning method integrating track and wireless local area network

The application scenario of indoor positioning determines that a user cannot wait for obtaining a positioning result for a long time, the positioning process is real-time and dynamic, and when the equipment terminal is in motion, a wireless local area network signal is more easily interfered by environmental changes. Therefore, the effect of real-time location update by using wlan positioning is not ideal. The characteristic that the sensor has good response to the motion state enables the analysis, fusion and positioning of data of various sensors to become an ideal scheme for solving the real-time dynamic problem of indoor positioning.

Concept architecture

The position estimation using the trajectory detected by the sensor mainly has two errors: firstly, the error of the starting position and secondly, the error of inaccurate direction; the initial position error can cause system deviation of the positioning result, and the inaccurate azimuth error can cause deviation between the track of the positioning result and the actually passed track, which can cause serious influence on the precision of the positioning result.

There are also two errors in positioning with wireless local area networks: firstly, the jump error of the wireless local area network signal can exceed 10dm when the external interference is large, and the deviation of a true value of a positioning result can be caused; secondly, the wrong nearest fingerprint point is selected, and the positioning precision is greatly reduced by introducing the error caused by the wrong fingerprint point.

Aiming at different error sources of two different positioning modes, partial error can be reduced to a great extent by carrying out data analysis and fusion, and the positioning result is more accurate and stable.

The track positioning and the wireless local area network positioning reflect the spatial position value of the same place at the same time, but the position estimation values of the two methods are not consistent, the track positioning result is used as a predicted value, the wireless local area network positioning result is used as an observed value, and the filter initialization is carried out at a given starting point, so that a filtering value can be obtained at each time, the value is used as an estimation value of the terminal position of the equipment, and the basic idea of carrying out Kalman filtering on the track and the wireless local area network positioning is realized.

(II) method step

The locus and the wireless local area network can obtain position information, the position determined by the locus is used as a state predicted value, the position determined by the wireless local area network positioning is used as an observed value, Kalman filtering is carried out, empirical values are adopted for state prediction model noise and process noise in a filtering equation, and the filtering process is first-order linear.

Fig. 3 is a flow chart of a kalman filtering positioning method with a track and a wireless local area network fused, where the track and the wireless local area network are used to perform kalman filtering for location updating, and the following methods are selected as the updating timing.

1. Location updating based on trajectory type

The filtering timing of this type of selection is each time the trajectory changes, which has the advantage of ensuring smooth trajectory, because frequent position estimation is not a good choice for indoor positioning, especially under dynamic conditions, the fluctuation of the wireless lan signal interferes with the positioning result, and the position is updated only in case of trajectory change, so that the continuity of the trajectory detected by the sensor is preserved to the greatest extent. The estimation method depends on the accuracy of the sensor, so that when the sensor has a large error, the positioning result is also influenced seriously.

2. Location update based on WLAN signal changes

The position updating according to the signal change of the wireless local area network is to update the position when the signal of the wireless local area network changes greatly each time, and the form considers the dynamic situation and has good effect of correcting the track deviation, but the defect is that the error generated by the signal disturbance of the wireless local area network is difficult to weaken under the dynamic situation, the signal disturbance of the wireless local area network has great influence, and the track determining effect may be unstable if the updating frequency of the signal of the wireless local area network is fast.

3. Fixed sample rate location update

The position is updated at a fixed sampling rate, and the position is updated every sampling time interval. The position is updated in the mode, the size of the sampling interval can be adjusted, a proper sampling interval is selected, and the sensor error and the wireless local area network signal disturbance error are balanced to a certain degree.

In any way, the basic process of the method for performing Kalman filtering by using the track determination and the wireless local area network is consistent, and the invention adopts the first method of performing position updating according to the track type.

(III) analysis of the results of the experiment

In order to verify the effectiveness of the Kalman filtering method for fusing the track and the wireless local area network, a dynamic positioning experiment is carried out. The experimental process is that (2.3, 0) coordinates are used as a starting point, a straight line passes through the coordinates (7.1, 0) and rotates by 90 degrees to reach the coordinates (7.1, 9.5) finally, and acceleration, direction and wireless local area network information are collected in the motion process respectively. And respectively estimating the position by using a wireless local area network, track dead reckoning, line segment matching and a Kalman filtering method of the track and the wireless local area network.

1. Trajectory determination

The track determination by using the sensor is not absolutely stable, because the direction determination has errors, the components projected to the x and y axes from the distance are distorted, the distortion reaches 4.7 meters, and the factors causing the direction errors are many, wherein the indoor electromagnetic interference accounts for a great proportion, and because the direction determination depends on the magnetic field data of the magnetic sensor, when the indoor electromagnetic interference is serious, the magnetic field jumps, and the detected direction generates errors. The method for determining the track by using the sensor has the advantages that the track matching result using the track as constraint can be influenced by track deviation, and the results and the accuracy of dead reckoning positioning, wireless local area network positioning, line segment matching and Kalman filtering positioning of the track and the wireless local area network are compared.

2. Track dead reckoning results

The track position estimation is simply utilized, when the track is determined accurately, the precision within 2 meters can be achieved, but when the track is distorted, a large error is generated, and the error exceeds 3 meters, so that the method for dead reckoning by simply utilizing the track is very unstable, and the distortion of the whole track can be caused as long as an error occurs in a certain link in the positioning process.

3. WLAN positioning results

The error is large under the dynamic condition by singly utilizing the wireless local area network positioning, the average positioning error exceeds 2m, because the signal disturbance of the wireless local area network is large under the dynamic condition, and because of dynamic detection, a large number of sampling values cannot be obtained to carry out average calculation to eliminate the possible jump of the signal.

4. Line segment matching result

The multi-sensor fusion positioning method based on line segment matching effectively inhibits errors caused by wireless local area network or track dead reckoning, and eliminates fingerprint points with larger errors by the multi-sensor fusion positioning method based on line segment matching, so that the positioning accuracy is improved on the whole. When the signal is stable or the sensor is interfered less, the line segment matching is utilized to carry out positioning, so that the positioning precision is not much superior to that of the wireless local area network, but when the wireless local area network signal and the detection value of the sensor generate large errors, the line segment matching still can obtain better precision, and the positioning advantage is obvious compared with that of the wireless local area network.

In the multi-sensor fusion positioning method for line segment matching, a track is divided into a plurality of line segments during matching, the line segments are continuous in actual conditions, and due to errors of a wireless local area network and sensors, the finally matched lines are not all connected end to end, so that the situation that two matching values appear in the same place can be generated, and in the positioning process of continuous motion, the result obtained through line segment matching can be improved in the aspects of continuity and stability.

5. Manman filtering result of track and wireless local area network card

The problem of discontinuous tracks in line segment matching can be well solved through the Raman filtering of the track and the WLAN, the positioning result is obtained, after three iterations, the position estimation value of the Kalman filtering is very close to the true value, the high efficiency of the track and the WLAN Kalman filtering under the dynamic condition is proved, and the method obtains more accurate position estimation through iteration. Therefore, the method is very suitable for real-time dynamic positioning.

6. Comprehensive experimental results and error statistics

When an accurate initial value is given, the error between the track and the Kalman filtering method of the wireless local area network is minimum, and the positioning effect is best. The line segment matching does not need a given initial value, but can also achieve higher precision. The two combined positioning methods of the invention can obviously improve the positioning precision and improve the dynamic response capability of the system.

Claims

1. The indoor positioning method of the terminal equipment with multiple sensor fusion is characterized in that two multi-sensor terminal indoor positioning methods with progressive relation are sequentially provided through a sensor of an intelligent equipment terminal and an indoor positioning technology based on a wireless local area network, the multi-sensor fusion positioning methods with line segment matching and the Kalman filtering positioning method with track and wireless local area network fusion are respectively used for analyzing and fusing data at a feature level, the data for representing an object state and the data for representing the object feature are associated and registered, the dimension for describing the object state and the feature is increased, and a combined feature state or a feature vector is obtained, so that related target knowledge is more accurate and complete, the robustness of a system is improved by utilizing the information redundancy of the system, and the error of a single method is compensated by the information fusion of multiple information sources;

2. The indoor positioning method of the terminal equipment with the multiple sensors fused according to claim 1, wherein the invention provides a line-segment-matched multi-sensor fusion positioning method, which utilizes multiple sensors to determine the motion track of the equipment, obtains effective motion information of the equipment terminal through sensor data filtering and calculation, and provides information of the change of the equipment terminal in space by utilizing the motion information; the wireless local area network positions the spatial position of the computing equipment terminal on a certain point, the sensor and the wireless local area network provide two pieces of information about the spatial position, the redundancy of the information is utilized, the error generated by a single positioning method is inhibited, and the precision and the reliability of indoor positioning are improved; the line segment matching is started from the simplest straight line, and the position is determined by utilizing the analysis and fusion of various sensor data;

matching the head end and the tail end of a straight line, when the equipment terminal advances on the straight line, obtaining a linear motion vector of the equipment terminal according to a result determined by a track, wherein the vector is characterized by relative displacement in the x and y directions and orientation information of the straight line, and obtaining J1 and J2 pieces of nearest fingerprint point information respectively determined by the equipment terminal at the head end and the tail end of the line through calculation of a dynamic K value nearest improved algorithm; when the equipment terminal moves linearly, the relative position of the equipment terminal on the straight line is accurate, and the relative position information can be used as matched characteristic information;

3. The indoor positioning method of the terminal device with the fusion of the multiple sensors as claimed in claim 1, wherein the invention provides a dynamic K value nearest neighbor improved algorithm as a wireless local area network positioning algorithm, the positioning result is obtained by weighting K fingerprint point positions with the nearest Euclidean distance, an empirical value is set for the determination of the K value, and the size of the K value influences the positioning precision of the wireless local area network;

4. The indoor positioning method of the terminal equipment fused with the multiple sensors as claimed in claim 1, wherein in the step of the multi-sensor fusion positioning method of line segment matching, the line segment matching is a process of data analysis and fusion, a motion track can be planned into a plurality of straight paths, and during matching, only line segment matching needs to be carried out on each path, and finally the coordinates of all passing points on the motion track can be obtained;

5. The multi-sensor-converged terminal device indoor positioning method according to claim 4, wherein the time domain modification division: the time domain division is carried out on the three sensor queues, the data obtained by mapping the three sensors are ensured to be in the same time domain, the division is carried out according to the fact that the index of the track changing point obtained by the direction queue is used as a discontinuous point for dividing the time domain, and the time domain is modified.

6. The indoor positioning method for terminal equipment with multiple sensor fusions of claim 4, characterized in that track matching: traversing each track, determining a matching result on the track, and finally storing the track detected by the direction and the acceleration into a linear form, wherein each linear form is characterized by the length and the direction of the linear form;

after obtaining J1 and J2 corresponding fingerprints, pairwise matching J1 and J2 fingerprint points of the starting point and the ending point into fingerprint point vectors, wherein each fingerprint in J1 fingerprints of the starting point is matched with J2 fingerprints of the ending point to finally form J1 × J2 pairs of fingerprint vectors;

7. The indoor positioning method of the terminal device with the fusion of the plurality of sensors as claimed in claim 1, wherein the Kalman filtering positioning method with the fusion of the track and the wireless local area network, which is further provided by the invention, is characterized in that aiming at different error sources of different positioning modes, data analysis and fusion are carried out to reduce a part of errors to a great extent, so that the positioning result is more accurate and stable, and the Kalman filtering is adopted for data analysis and fusion;

8. The indoor positioning method for terminal equipment with multiple sensors fused according to claim 7, wherein the Kalman filtering positioning method with track and wireless local area network fused comprises the following steps: the method comprises the following steps that position information can be obtained by a track and a wireless local area network, a position determined by the track is used as a state prediction value, a position determined by the positioning of the wireless local area network is used as an observed value, Kalman filtering is carried out, empirical values are adopted for state prediction model noise and process noise in a filtering equation, and the filtering process is first-order linear;

9. The indoor positioning method for terminal equipment with multiple sensors integrated as claimed in claim 8, wherein the three updating timing methods of the present invention are consistent with the basic process of the kalman filtering method using the track determination and the wireless lan, and the first method is recommended to update the position according to the track type.