Wireless indoor positioning data processing method and system for multi-scene application

Abstract

The invention discloses a wireless indoor positioning data processing method and system for multi-scene application, and the method comprises the following steps: obtaining a positioning point or a fingerprint point, and collecting the signal intensity data received from a wireless device; analyzing and detecting the signal intensity data by using a wavelet algorithm; if the detection result has valid signal intensity data and invalid data, obtaining a wavelet coefficient graph by using a wavelet algorithm, determining the position of the invalid data, and performing data filtering and data recombination; analyzing the recombined signal intensity data by using a wavelet algorithm, and detecting whether abnormal data exist or not; and if the abnormal data does not exist, taking the recombined signal intensity data as the optimal positioning data. The method solves the problem that the positioning result is affected by the time-varying characteristic of the signal based on the multi-wavelet algorithm, and has the advantages of high precision, easy implementation and the like.

Description

Technical Field

[0001] The present invention relates to the field of indoor positioning technology, and more particularly to a wireless indoor positioning data processing method and system. Background Art

[0002] With the continuous development of computer technology and network technology, the demand for location information services has been increasingly studied and applied. Especially in large shopping malls, exhibition halls and other large areas, a complete indoor positioning system is needed for navigation and positioning services.

[0003] The current GPS technology can meet the needs of outdoor positioning, but it cannot be well applied to indoor environments because the GPS signal is blocked by indoor walls. The current indoor positioning technologies are mainly based on WLAN, Bluetooth, RFID and ZigBee. Positioning is mainly completed by measuring distance and fingerprint information matching. Among them, WLAN technology and fingerprint information matching methods have been widely used due to their low equipment cost and high positioning accuracy.

[0004] The fingerprint positioning method based on WLAN needs to collect more fingerprint point wireless signal data information. However, due to the influence of factors such as indoor environment changes, people walking and equipment instability, the received signal strength has a large time-varying property. The wireless signal characteristics with large errors will bring large positioning errors, which brings problems to the popularization of current WLAN fingerprint positioning.

[0005] Therefore, how to solve the impact of signal time variability has become a major technical problem in the popularization of WLAN positioning technology. Summary of the invention

[0006] In view of this, the present invention provides a wireless indoor positioning data processing method and system for multi-scenario applications to solve the problem that the current signal time-varying property affects the positioning accuracy.

[0007] In order to achieve the above object, the present invention adopts the following technical solution:

[0008] In a first aspect, a wireless indoor positioning data processing method for multi-scenario applications comprises the following steps:

[0009] S1. Obtain positioning points or fingerprint points, and collect signal strength data received from wireless devices;

[0010] S2, analyzing and detecting the signal strength data using a wavelet algorithm;

[0011] S3. If the detection result contains valid signal strength data and invalid data, a wavelet coefficient map is obtained by using a wavelet algorithm, and the location of the invalid data is determined to filter out the data and reorganize the data;

[0012] S4, using a wavelet algorithm to analyze the reorganized signal strength data and detect whether there is abnormal data;

[0013] S5. If there is no abnormal data, the reorganized signal strength data is used as the optimal positioning data.

[0014] Furthermore, the method further includes: S6, if there is abnormal data, analyzing the reliability of the abnormal data based on the previous and next data and making corrections, and using the corrected signal strength data as the optimal positioning data.

[0015] Furthermore, in step S1, the signal strength data is {s1, s2, ..., s N}sequence, where N≥2.

[0016] Further, step S2, using a wavelet algorithm to analyze and detect the signal strength data, specifically includes:

[0017] The wavelet algorithm is used to perform wavelet decomposition on the signal strength data to obtain the wavelet coefficients of each scale and frequency;

[0018] A wavelet coefficient threshold range is generated using the Tukey method, and valid data and invalid data are detected based on the wavelet coefficients within the threshold range.

[0019] Furthermore, the signal strength data is decomposed by wavelet algorithm to obtain wavelet coefficients of each scale and frequency; specifically, the following steps are included:

[0020] {s1,s2,...,s N} is used as the input signal sequence of the wavelet algorithm, and the data itself is used as the initial approximate coefficient a0[i]:

[0021] a0[i]=s i ,i=0,1,...,N

[0022] Starting from layer 0, low-pass filter h[i] and high-pass filter g[i] are applied layer by layer to perform downsampling and decompose into approximate coefficients and detail coefficients of multiple scales;

[0023] Get the approximate coefficient a of the jth layer j [i] and detail coefficient c j [i]:

[0024]

[0025] Among them, j represents the number of decomposition levels, k represents the intermediate integer variable, and its value is k∈[1,N].

[0026] Further, a wavelet coefficient threshold range is generated by using the Tukey method, and valid data and invalid data are detected based on the wavelet coefficients in the threshold range; specifically including:

[0027] The detail wavelet coefficient set {c1,c2,...,c N}, sort in ascending order to determine the median value Med; and calculate the interquartile range IQR, IQR = Q3-Q1; where Q1 represents the first quartile and Q3 represents the third quartile;

[0028] Get the upper limit c of the wavelet coefficient threshold range H =Q3+1.5*IQR, the lower limit of the wavelet coefficient threshold range c L =Q3-1.5*IQR;

[0029] like Then c i The corresponding signal s i Invalid signal strength data;

[0030] If c i ∈[c L ,c H ], (i=1,2,...N), then c i The corresponding signal s i It is the valid signal strength data.

[0031] Furthermore, in step S3, a wavelet coefficient map is obtained by using a wavelet algorithm, and the location of the invalid data is determined, and data filtering and data reorganization are performed; specifically, the following steps are included:

[0032] Use wavelet algorithm to construct wavelet coefficient graph;

[0033] Find and remove the data of the position segment where the wavelet coefficient fluctuates greatly in the wavelet coefficient graph;

[0034] Reorganize the remaining location segment data.

[0035] Further, step S4, using a wavelet algorithm to analyze the reorganized signal strength data and detect whether there is abnormal data; specifically includes:

[0036] The reorganized signal strength data is decomposed by wavelet algorithm to obtain the reorganized wavelet coefficient map;

[0037] And find out whether there is a drift position, and take the data corresponding to the drift position as abnormal data.

[0038] Further, in step S6, the reliability of the abnormal data based on the previous and next data is analyzed and corrected, and the corrected signal strength data is used as the optimal positioning data; specifically including:

[0039] The abnormal data is analyzed based on the reliability of the previous and next data, and the signal strength data segment with a stronger signal strength is output as the optimal positioning data.

[0040] In a second aspect, a wireless indoor positioning data processing system for multi-scenario applications includes the following modules:

[0041] Data acquisition module: used to obtain positioning points or fingerprint points and collect signal strength data received from wireless devices;

[0042] Analysis and detection module: used to analyze and detect the signal strength data using wavelet algorithm;

[0043] Data processing module: If there is valid signal strength data in the detection result and invalid data, it is used to obtain the wavelet coefficient map using the wavelet algorithm, determine the location of the invalid data, and perform data filtering and data reorganization;

[0044] Data anomaly detection module: used to analyze the reorganized signal strength data using wavelet algorithm and detect whether there is abnormal data;

[0045] Data output module: if there is no abnormal data, it is used to use the reorganized signal strength data as the optimal positioning data;

[0046] If there is abnormal data, the reliability of the abnormal data based on the previous and next data is analyzed and corrected, and the corrected signal strength data is used as the optimal positioning data.

[0047] It can be seen from the above technical solution that compared with the prior art, the present invention discloses a wireless indoor positioning data processing method and system for multi-scenario applications, which collects signal strength data and performs multiple wavelet analyses to remove invalid data and abnormal data, solve the data drift problem, output stable signal strength data, and improve positioning accuracy.

[0048] The present invention is mainly aimed at various scene problems that occur indoors in the wireless indoor positioning method based on the fingerprint method, which cannot be solved by the traditional method. It solves the problem that the accuracy of the positioning result is affected by factors such as environmental changes and personnel movement in the traditional wireless indoor positioning method based on the fingerprint method. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] 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 embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0050] Figure 1 A flow chart of a wireless indoor positioning data processing method for multi-scenario applications provided by an embodiment of the present invention.

[0051] Figure 2 A signal strength data graph after wavelet analysis provided in an embodiment of the present invention.

[0052] Figure 3 The wavelet coefficient graph after wavelet analysis provided by the embodiment of the present invention.

[0053] Figure 4 A signal strength data diagram after removing invalid data provided by an embodiment of the present invention.

[0054] Figure 5 A second wavelet coefficient graph with data drift phenomenon provided by an embodiment of the present invention.

[0055] Figure 6 The second wavelet coefficient map without data drift phenomenon provided by the embodiment of the present invention.

[0056] Figure 7 A schematic diagram of the position of a door in an open state provided by an embodiment of the present invention.

[0057] Figure 8 A schematic diagram of the position of a door in a closed state provided by an embodiment of the present invention.

[0058] Fig. 9 A diagram of signal strength data before and after a door state change provided by an embodiment of the present invention.

[0059] Fig.10 A schematic diagram of a wireless indoor positioning data processing system for multiple scenario applications provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] Embodiment 1

[0062] The embodiment of the present invention discloses a wireless indoor positioning data processing method for multi-scenario applications, comprising the following steps S1-S6:

[0063] S1. Obtain positioning points or fingerprint points, and collect signal strength data received from wireless devices;

[0064] Collect the received signal strength data of the positioning point or fingerprint point from a certain AP (i.e. wireless WiFi device), i.e. RSS data; the fingerprint point data mainly refers to the received signal strength data collected at a fixed location in the offline stage.

[0065] In this embodiment, the data set continuously collected by the positioning point or fingerprint point from a certain AP (i.e., wireless WiFi device) is {s1, s2, ..., s N}, where s i ,(i=1,2,...,N), N≥2, represents the i-th RSS value collected. Due to equipment instability, environmental changes, and personnel occlusion during the collection process, there may be data anomalies or data drift in the data set, resulting in large errors in the collected data values.

[0066] S2, analyzing and detecting the signal strength data using a wavelet algorithm;

[0067] In this embodiment, the data set {s1, s2, ..., s N}Perform wavelet decomposition to obtain wavelet coefficients of each scale and frequency, and use Tukey method to generate a wavelet coefficient threshold range. The data corresponding to the wavelet coefficients exceeding the threshold range are considered to be invalid RSS signal data;

[0068] The signal set sequence {s1,s2,...,s N} for analysis; {s1,s2,...,s N} is the input signal sequence, and the initial approximation coefficient a0[i] is the signal itself:

[0069] a0[i]=s i ,i=0,1,...,N

[0070] Layer-by-layer decomposition: Starting from layer 0, apply low-pass filter h[i] and high-pass filter g[i] layer by layer, and downsample the results to obtain the approximate coefficient a of layer j j [i] and detail coefficient c j [i]:

[0071]

[0072] Among them, j represents the number of decomposition levels and k represents.

[0073] Repeat decomposition: eventually the required number of decomposition levels can be obtained.

[0074] The final signal can be decomposed into detail coefficients of multiple scales; the detail coefficients can reflect the local changes or detail information of the signal.

[0075] This embodiment analyzes and obtains a j-layer detail wavelet coefficient set. For the first layer detail wavelet coefficient set {c1, c2, ..., c N}, the wavelet coefficient threshold range can be obtained by using Tukey method [c L ,c H The specific process of Tukey method is as follows:

[0076] The detail wavelet coefficient set {c1,c2,...,c N} Sort in ascending order and determine the median value Med; calculate the first quartile Q1, Q1 is the value at the 25% position; calculate the third quartile Q3, Q3 is the value at the 75% position. Calculate the interquartile range IQR, IQR = Q3-Q1. The upper limit c in the wavelet coefficient threshold range H =Q3+1.5*IQR, the lower limit of the wavelet coefficient threshold range c L =Q3-1.5*IQR.

[0077] like Then c i The corresponding signal s i Invalid RSS signal data.

[0078] If c i ∈[c L ,c H ], (i=1,2,...N), then c i The corresponding signal s i It is the valid signal strength data.

[0079] S3. If the detection result contains valid signal strength data and invalid data, a wavelet coefficient map is obtained by using a wavelet algorithm, and the location of the invalid data is determined to filter out the data and reorganize the data;

[0080] If there is no valid signal strength data in the detection result, repeat step S1; re-collect the data to obtain a new data set, and perform steps S1-S3 in sequence;

[0081] If the detection result contains valid signal strength data and no invalid data, the signal strength data is used as the optimal positioning data;

[0082] In this embodiment, refer to Figure 2 As shown, it is a signal strength data diagram after wavelet analysis; a wavelet coefficient diagram is constructed according to the wavelet coefficients obtained in step S2, referring to Figure 3 As shown in the figure, the wavelet coefficients from the 13th to the 16th collected data are relatively large. The RSS data corresponding to the larger wavelet coefficients can be regarded as invalid data values ​​using the Tukey method.

[0083] The abnormal data values ​​are filtered out and reorganized to obtain a new data sequence. In this embodiment, the new data sequence has 26 data points remaining. The new data sequence is plotted for reference. Figure 4 As shown, due to the existence of drift, the new data sequence obtained cannot be used as stable positioning data.

[0084] S4, using a wavelet algorithm to analyze the reorganized signal strength data and detect whether there is abnormal data;

[0085] In this embodiment, the new data sequence still has abnormal values, and the second wavelet coefficient map can be obtained by wavelet analysis. The role of this wavelet analysis is not to remove abnormal signal values, but to determine the drift position.

[0086] If drift occurs, refer to Figure 5 As shown, this is the second wavelet coefficient diagram with data drift; if there is no drift, refer to Figure 6 As shown, this is the second wavelet coefficient diagram without data drift.

[0087] S5. If there is no abnormal data, the reorganized signal strength data is used as the optimal positioning data;

[0088] S6. If there is abnormal data, analyze the reliability of the abnormal data based on the previous and next data and make corrections, and use the corrected signal strength data as the optimal positioning data.

[0089] This embodiment determines the drift position according to the abnormal data position, analyzes the reliability of the previous and next data, and generally outputs the data segment with stronger signal strength as the signal, which is the optimal positioning data.

[0090] The method of the present invention solves the problem that the accuracy of positioning results is affected by factors such as environmental changes and personnel movement in the traditional fingerprint-based wireless indoor positioning method. For RSS sequences with only invalid data values, wavelet analysis can analyze the invalid data position and delete it, and output stable RSS signal data; for RSS sequences with drift, wavelet analysis can not only analyze the invalid data position and delete it, but also find the drift position according to the abnormal data and output stable RSS signal data.

[0091] The method of the present invention is mainly aimed at various scene problems that occur indoors in the wireless indoor positioning method based on the fingerprint method, which cannot be solved by the traditional method. Using this method, in an environment with relatively complex personnel, the indoor positioning error can be reduced by about 50%, thereby improving the engineering applicability of the wireless indoor positioning method.

[0092] Reference Figure 7 As shown in the figure, user A collects wireless signals from AP1 during the positioning process, and person B is just next to the door, which is open. Since B closes the door, refer to Figure 8 As shown in the figure, when user A collects wireless signal strength data from AP1 during positioning, it is likely to produce anomalies and drift. Fig. 9 As shown, the traditional filtering algorithm cannot solve this problem well, but the method of the present invention is used to delete invalid signals and analyze abnormal signals, so that the accurate positioning of A can be obtained.

[0093] Embodiment 2

[0094] The embodiment of the present invention discloses a wireless indoor positioning data processing system for multi-scenario applications. Fig.10 As shown, it includes the following modules:

[0095] Data acquisition module: used to obtain positioning points or fingerprint points and collect signal strength data received from wireless devices;

[0096] Analysis and detection module: used to analyze and detect the signal strength data using wavelet algorithm;

[0097] Data processing module: If there is valid signal strength data in the detection result and invalid data, it is used to obtain the wavelet coefficient map using the wavelet algorithm, determine the location of the invalid data, and perform data filtering and data reorganization;

[0098] Data anomaly detection module: used to analyze the reorganized signal strength data using wavelet algorithm and detect whether there is abnormal data;

[0099] Data output module: if there is no abnormal data, it is used to use the reorganized signal strength data as the optimal positioning data;

[0100] If there is abnormal data, the reliability of the abnormal data based on the previous and next data is analyzed and corrected, and the corrected signal strength data is used as the optimal positioning data.

[0101] The system of the present invention can solve the problem that the time-varying nature of the signal caused by environmental changes, human body occlusion and the like affects the positioning result, and has the advantages of high precision and easy implementation.

[0102] The system of the present invention can be used to track the location of people or objects in smart buildings and smart homes to improve management efficiency; in large shopping malls or supermarkets, it can provide navigation services for customers to help them find goods quickly; in industrial manufacturing and logistics warehousing, in transportation hubs such as airports and stations, its adaptability in multiple scenarios makes it an effective tool for improving spatial perception and optimizing user experience.

[0103] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0104] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A wireless indoor positioning data processing method for multi-scenario applications, characterized in that: The following steps are involved: S1. Obtain positioning points or fingerprint points, and collect signal strength data received from wireless devices; S2, analyzing and detecting the signal strength data using a wavelet algorithm; S3. If the detection result contains valid signal strength data and invalid data, a wavelet coefficient map is obtained by using a wavelet algorithm, and the location of the invalid data is determined to filter out the data and reorganize the data; S4, using a wavelet algorithm to analyze the reorganized signal strength data and detect whether there is abnormal data; S5. If there is no abnormal data, the reorganized signal strength data is used as the optimal positioning data.

2. A wireless indoor positioning data processing method for multi-scenario applications as claimed in claim 1, characterized in that: Also includes: S6. If there is abnormal data, analyze the reliability of the abnormal data based on the previous and next data and make corrections, and use the corrected signal strength data as the optimal positioning data.

3. The method for processing wireless indoor positioning data for multi-scenario applications according to claim 1, characterized in that: In step S1, the signal strength data is {s1, s2, ..., s N }sequence, where N≥2.

4. A wireless indoor positioning data processing method for multi-scenario applications as claimed in claim 3, characterized in that: Step S2, using a wavelet algorithm to analyze and detect the signal strength data; specifically includes: The wavelet algorithm is used to perform wavelet decomposition on the signal strength data to obtain the wavelet coefficients of each scale and frequency; A wavelet coefficient threshold range is generated using the Tukey method, and valid data and invalid data are detected based on the wavelet coefficients within the threshold range.

5. The method for processing wireless indoor positioning data for multi-scenario applications according to claim 4, characterized in that: The signal strength data is decomposed by wavelet algorithm to obtain wavelet coefficients of each scale and frequency; specifically, the following are included: {s1,s2,...,s N } is used as the input signal sequence of the wavelet algorithm, and the data itself is used as the initial approximate coefficient a0[i]: a0[i]=s i ,i=0,1,...,N Starting from layer 0, low-pass filter h[i] and high-pass filter g[i] are applied layer by layer to perform downsampling and decompose into approximate coefficients and detail coefficients of multiple scales; Get the approximate coefficient a of the jth layer j [i] and detail coefficient c j [i]: Among them, j represents the number of decomposition levels, k represents the intermediate integer variable, and its value is k∈[1,N].

6. A wireless indoor positioning data processing method for multi-scenario applications as claimed in claim 5, characterized in that: The method uses the Tukey method to generate a wavelet coefficient threshold range, and detects valid data and invalid data based on the wavelet coefficients in the threshold range; specifically includes: The detail wavelet coefficient set {c1,c2,...,c N }, sort in ascending order to determine the median value Med; and calculate the interquartile range IQR, IQR = Q3-Q1; where Q1 represents the first quartile and Q3 represents the third quartile; Get the upper limit c of the wavelet coefficient threshold range H =Q3+1.5*IQR, the lower limit of the wavelet coefficient threshold range c L =Q3-1.5*IQR; like Then c i The corresponding signal s i Invalid signal strength data; If c i ∈[c L ,c H ], (i=1,2,...N), then c i The corresponding signal s i It is the valid signal strength data.

7. A wireless indoor positioning data processing method for multi-scenario applications as claimed in claim 6, characterized in that: In step S3, a wavelet coefficient map is obtained by using a wavelet algorithm, and the location of the invalid data is determined, and data filtering and data reorganization are performed; specifically, the following steps are included: Use wavelet algorithm to construct wavelet coefficient graph; Find and remove the data of the position segment where the wavelet coefficient fluctuates greatly in the wavelet coefficient graph; Reorganize the remaining location segment data.

8. The method for processing wireless indoor positioning data for multi-scenario applications according to claim 7, characterized in that: Step S4, using a wavelet algorithm to analyze the reorganized signal strength data and detect whether there is abnormal data; specifically includes: The reorganized signal strength data is decomposed by wavelet algorithm to obtain the reorganized wavelet coefficient map; And find out whether there is a drift position, and take the data corresponding to the drift position as abnormal data.

9. The method for processing wireless indoor positioning data for multi-scenario applications as claimed in claim 2, characterized in that: In step S6, the reliability of the abnormal data based on the previous and next data is analyzed and corrected, and the corrected signal strength data is used as the optimal positioning data; specifically, the following steps are included: The abnormal data is analyzed based on the reliability of the previous and next data, and the signal strength data segment with a stronger signal strength is output as the optimal positioning data.

10. A wireless indoor positioning data processing system for multi-scenario applications, characterized in that: Includes the following modules: Data acquisition module: used to obtain positioning points or fingerprint points and collect signal strength data received from wireless devices; Analysis and detection module: used to analyze and detect the signal strength data using wavelet algorithm; Data processing module: If there is valid signal strength data in the detection result and invalid data, it is used to obtain the wavelet coefficient map using the wavelet algorithm, determine the location of the invalid data, and perform data filtering and data reorganization; Data anomaly detection module: used to analyze the reorganized signal strength data using wavelet algorithm and detect whether there is abnormal data; Data output module: if there is no abnormal data, it is used to use the reorganized signal strength data as the optimal positioning data; If there is abnormal data, the reliability of the abnormal data based on the previous and next data is analyzed and corrected, and the corrected signal strength data is used as the optimal positioning data.

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