CN112261578A - Indoor fingerprint positioning method based on mode filtering - Google Patents
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Abstract
The invention discloses an indoor fingerprint positioning method based on mode filtering, which specifically comprises the steps of S1-S6; the invention fully utilizes the distribution characteristics of RSSI data, improves the quality of the fingerprint database relative to the average filtering and ensures the operation efficiency; compared with a single filtering method, the invention provides a novel RSSI data processing method, applies the mode to the filtering method for the first time, and provides a new idea for subsequent research; compared with the prior art, the method can avoid the RSSI ranging problem based on the Received Signal Strength (RSSI) position fingerprint positioning algorithm, and can construct a high-quality fingerprint database through filtering processing. Aiming at the problems of long operation time of Gaussian filtering, large positioning error of mean filtering and the like, the operation efficiency is superior to that of Gaussian filtering, and meanwhile, compared with mean filtering, mode filtering improves the positioning precision by about 0.15m, the maximum positioning error is limited within 2m under the condition of 95%, and the quality of a fingerprint database can be effectively improved.
Description
Technical Field
The invention belongs to the technical field of position fingerprint positioning with low indoor space complexity, and particularly relates to an indoor fingerprint positioning method based on mode filtering.
Background
With the rapid development of Wireless Local Area Network (WLAN) technology and the wide deployment of WLAN worldwide, a location positioning technology based on WLAN coverage becomes a hot spot for location service research in indoor environment. The position service based on the wireless local area network has the characteristics of simple realization, wide coverage range, high application integration level and the like, and can realize higher positioning precision; the location positioning technique using the wireless lan includes a variety of methods, among which there are mainly the strongest base station method, time of arrival (TOA) of a signal, angle of arrival (AOA) of a signal, time difference of arrival (TDOA) of a signal, and signal strength measurement. The indoor positioning based on the signal intensity also comprises a transmission loss method and a fingerprint positioning method, and the transmission loss method causes the problem of positioning accuracy due to the multipath effect generated by the through-wall of WLAN air signals and other reasons; the fingerprint positioning method can directly utilize the existing WLAN wireless environment, and can realize positioning without changing hardware equipment to carry out time synchronization and angle measurement, so that the environmental adaptability is strong, the cost is low, and the method becomes a hotspot of the research of domestic and foreign communication industries; meanwhile, the RSSI ranging problem can be avoided based on a Received Signal Strength (RSSI) position fingerprint positioning algorithm, and a high-quality fingerprint database can be constructed through filtering processing.
The so-called fingerprint positioning method comprises two stages of off-line detection and on-line positioning. The off-line detection is to plan and set a plurality of sampling points in a certain range, and store the information of the sampling points and the sampled signal strength into a database, which is a position fingerprint database. In the on-line positioning stage, the strength of the acquired signals is compared with the fingerprints stored in the database so as to determine the position. Various algorithms for determining the position are proposed at present, mainly a data correlation method, but the existing fingerprint positioning method has more sampling points participating in the calculation of the data correlation method, larger calculation workload and incapability of realizing higher positioning precision;
therefore, it is necessary to provide an indoor fingerprint positioning method based on mode filtering, which comprehensively considers the operation efficiency and the positioning accuracy.
Disclosure of Invention
The invention aims to solve the defects in the prior art, the RSSI data distribution characteristic is fully utilized, the quality of a fingerprint database is improved compared with the average filtering, and the operation efficiency is ensured; compared with a single filtering method, the invention provides a novel RSSI data processing method, applies the mode to the filtering method for the first time, and provides a new idea for subsequent research; the invention can avoid RSSI ranging problem based on the Received Signal Strength (RSSI) position fingerprint positioning algorithm, and can construct a high-quality fingerprint database through filtering processing. Aiming at the problems that the Gaussian filtering operation time is long and the average filtering positioning error is large, a mode filtering method is provided based on the characteristic that partial RSSI repeatedly appears by analyzing the distribution rule of the RSSI, the mode in the current data is selected successively, and the characteristic RSSI is extracted by averaging until the ratio of the accumulated frequency sum of the selected mode to the total data amount is greater than 95%, so that the indoor fingerprint positioning method based on the mode filtering is provided.
In order to achieve the purpose, the invention provides the following technical scheme:
the indoor fingerprint positioning method based on mode filtering comprises the following steps:
s1, according to the indoor wireless signal propagation characteristics, the RSSI is removed by using a box plot methodiAn abnormal data value in the sequence;
s2, counting the total RSSI data, establishing frequency and mode statistics of each group and initializing an optimal data ratio Z;
s3, selecting RSSI with large frequency by using the optimal data ratio Z to carry out mean filtering to calculate the characteristic value of the RSSI group;
s4, constructing a fingerprint database based on the RSSI characteristic value and the position coordinates of the corresponding reference point;
s5, selecting an optimal data ratio Z by using an exhaustion method;
and S6, solving the position of the indoor undetermined point based on the optimal fingerprint database through a WKNN (Weighted K-Nearest Neighbor) algorithm.
Preferably, the step S1 includes the steps of:
s1.1, forming a sequence by RSSI data collected from various known reference points indoors;
s1.2, abnormal value detection is carried out on each group of data through a box plot: if an abnormal value exists, replacing the abnormal value by the average value before and after the abnormal value; otherwise, the next step is performed.
Preferably, the step S2 is: and receiving RSSI data of each indoor wireless signal transmitting station for each reference point to create statistics, recording the total quantity of each group of RSSI data as N, and initializing a data ratio Z to be 0.6.
Preferably, the step S3 includes the steps of:
s3.1, recording current RSSI sequence mode RSSIiAnd the frequency m of the data is set to K +1, P is set to P + m, and the data ratio Z is set to P/N;
s3.2, eliminating RSSI in the current sequenceiForming a new RSSI sequence;
s3.3, judging whether the mode filtering search Z is less than P/N and meets the stop condition: if yes, continuing to step S2.6; otherwise, returning to the step S3.2;
and S3.4, calculating the arithmetic mean value of K RSSI of the selected frequency rows before the change of the array as the characteristic value of the group of RSSI.
Preferably, the step S4 is: calculating characteristic values of all known indoor reference points by using the mode filtering method based on the initial data ratio Z to form fingerprint data; obtaining position coordinates (X, Y) of all indoor reference points by a measuring method; combining the fingerprint data and the position coordinates together to construct a fingerprint database.
Preferably, the step S5 includes the steps of:
s5.1, selecting 0.05 as an exhaustion step length, and enabling Z to be Z + 0.05;
s5.2, resetting the frequency number accumulation statistic P, selecting the mode number accumulation K and the total RSSI data quantity N, and enabling P to be 0, K to be 0 and N to be the number of the current RSSI array;
s5.3, repeating the steps S4 and S5, and establishing a fingerprint database based on the current Z value;
s5.4, calculating the error in the current positioning through a WKNN algorithm;
and S5.5, judging whether the exhaustive Z is 1 and whether the stopping condition is met: if yes, continuing to step S5.6; otherwise, returning to the step S5.1;
s5.6, comparing the total positioning errors of the fingerprint databases corresponding to the different Z values, and selecting the Z corresponding to the fingerprint database with the minimum error as the optimal data ratio.
Preferably, the step S6 is: collecting the point data at an indoor undetermined point through receiving equipment, and calculating fingerprint data of the point based on an optimal data Z value determined by an exhaustion method; and searching and comparing the fingerprint data of the point with a fingerprint database, and selecting K adjacent points to perform weighted average calculation on coordinates (X, Y) of the undetermined point.
The invention has the technical effects and advantages that: the invention fully utilizes the distribution characteristics of RSSI data, improves the quality of a fingerprint database relative to average filtering and ensures the operation efficiency;
compared with a single filtering method, the invention provides a novel RSSI data processing method, applies the mode to the filtering method for the first time, and provides a new idea for subsequent research;
compared with the prior art, the indoor fingerprint positioning method based on the mode filtering can avoid the RSSI ranging problem based on the Received Signal Strength (RSSI) position fingerprint positioning algorithm, and can construct a high-quality fingerprint database through filtering processing. Aiming at the problems that the Gaussian filtering operation time is long and the mean filtering positioning error is large, the RSSI distribution rule is analyzed, a mode filtering method is provided based on the characteristic that partial RSSI repeatedly appears, the mode in the current data is selected successively, and the characteristic RSSI is extracted by averaging until the ratio of the accumulated frequency sum of the selected mode to the total data amount is larger than 95%. Experiments show that the fingerprint database constructed based on the mode filtering is similar to the Gaussian filtering positioning precision under 73% of conditions, but the operation efficiency is superior to that of Gaussian filtering. Meanwhile, compared with mean value filtering, mode filtering improves the positioning precision by about 0.15m, limits the maximum positioning error within 2m under the condition of 95%, and can effectively improve the quality of the fingerprint database.
Drawings
FIG. 1 is a schematic box plot diagram illustrating the elimination of outliers in initial data using RSSI in accordance with an embodiment of the present invention;
FIG. 2 is a graph of RSSI data distribution characteristics used in the practice of the present invention;
FIG. 3 is a schematic diagram of a boxplot for outlier detection for a set of RSSI data in accordance with an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a mode filtering process in accordance with an embodiment of the present invention;
FIG. 5 is a graph illustrating an exhaustive selection of optimal data to Z values in mode filtering in accordance with an embodiment of the present invention;
fig. 6 is a schematic flow structure diagram of the indoor fingerprint positioning method based on mode filtering according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The technical solution of the present invention is further described below with reference to the accompanying drawings 1-6 and the detailed description: the specific embodiment discloses an indoor fingerprint positioning method based on mode filtering, which comprises the following steps:
s1, according to the indoor wireless signal propagation characteristics, the RSSI is removed by using a box plot methodiThe abnormal data value in the sequence specifically comprises the following steps:
s1.1, forming a sequence by RSSI data collected from various known reference points indoors;
s1.2, abnormal value detection is carried out on each group of data through a box plot: if an abnormal value exists, replacing the abnormal value by the average value before and after the abnormal value; otherwise, the next step is performed.
The boxplot is a statistical graph used for displaying a set of data dispersion situation data, as shown in fig. 1, and an abnormal value exclusion rule is formulated by two upper and lower quartiles in a set of data as shown in the following formula, and abnormal values are detected as shown in fig. 3 without prior knowledge of data distribution.
The lower quartile of the whole set of data is shown in the formula, and the upper quartile of the whole set of data is shown in the formula.
The specific implementation mode of the invention is based on a certain indoor positioning laboratory of Nanjing university of industry, and MOL iBeacon equipment of a homing company is used as base station equipment, and nova3i of Huacheng smart phone is used as receiving equipment to acquire RSSI data. And arranging 80 known reference points in the chamber, and testing 17 known points.
The acquired RSSI data is subjected to data outlier rejection by the box plot of step S1.
S2, initializing an optimal data ratio Z to be 0.6, establishing each group of frequency accumulation amount and mode statistic for each group of RSSI data, and recording the total quantity N of the group of RSSI data; the same data ratio Z is adopted for each group of data when carrying out mode filtering;
s3, selecting the RSSI with larger frequency by using the optimal data ratio Z to carry out mean filtering to calculate the group of RSSI characteristic values, and concretely comprising the following steps:
s3.1, recording current RSSI sequence mode RSSIiAnd the frequency m of the data is set to K +1, P is set to P + m, and the data ratio Z is set to P/N;
s3.2, eliminating RSSI in the current sequenceiForming a new RSSI sequence;
s3.3, judging whether the mode filtering search Z is less than P/N and meets the stop condition: if yes, continuing to step S2.6; otherwise, returning to the step S3.2;
s3.4, calculating an arithmetic mean value of K RSSI of the selected frequency row before the change of the array as the characteristic value of the group of RSSI;
s4, calculating characteristic values of all known indoor reference points by using the mode filtering method based on the initial data ratio Z to form fingerprint data; obtaining position coordinates (X, Y) of all indoor reference points by a measuring method; combining the fingerprint data and the position coordinates together to construct a fingerprint database, which is as follows:
and S5, selecting the optimal data ratio Z by using an exhaustion method. The method specifically comprises the following steps:
s5.1, selecting 0.05 as an exhaustion step length, and enabling Z to be Z + 0.05;
s5.2, resetting the frequency number accumulation statistic P, selecting the mode number accumulation K and the total RSSI data quantity N, and enabling P to be 0, K to be 0 and N to be the number of the current RSSI array;
s5.3, repeating the steps S4 and S5, and establishing a fingerprint database based on the current Z value;
s5.4, calculating the error in the current positioning through a WKNN algorithm;
and S5.5, judging whether the exhaustive Z is 1 and whether the stopping condition is met: if yes, continuing to step S5.6; otherwise, returning to the step S5.1;
s5.6, comparing the total positioning errors of the fingerprint databases corresponding to the Z values under different Z values, and selecting the Z value corresponding to the minimum error as the optimal data ratio, as shown in FIG. 4.
The invention fully utilizes the distribution characteristics of RSSI data, improves the quality of a fingerprint database relative to average filtering and ensures the operation efficiency; compared with a single filtering method, the invention provides a novel RSSI data processing method, applies the mode to the filtering method for the first time, and provides a new idea for subsequent research;
s6, collecting the point data at the indoor undetermined point through receiving equipment, and calculating the fingerprint data of the point based on the optimal data Z value determined by an exhaustion method; and (3) searching and comparing the point fingerprint data with a fingerprint database, selecting K adjacent points according to the formula (a), calculating a weight value according to the formula (b), and calculating coordinates (X, Y) of the undetermined point according to the formula (c).
Mode filtering, median filtering and Gaussian filtering are selected to compare and analyze the positioning result precision and the operation efficiency, and indoor fingerprint RSSI data are collected through the specific implementation mode of the invention to verify the uniqueness of the mode filtering.
TABLE 1 comparison table of positioning effect of different filtering methods
In comparison with the results in table 1, gaussian, median and mode filters increased processing time relative to mean, but mode filters increased minimally by only 15%, whereas gaussian increased by about 150% and median increased by about 260%. Meanwhile, compared with the mean value filtering, the mode filtering effectively reduces the root mean square error by about 0.15m, is similar to the median filtering, and has higher efficiency than the median filtering.
Through the above analysis, it was found that:
1) mode filtering utilizes the distribution characteristics of RSSI fingerprint data, proposes that mode is applied to characteristic value extraction, and provides a new idea for follow-up research;
2) compared with the average filtering, the mode filtering positioning precision is improved by about 0.15m, and the operation time is only increased by 15%. Meanwhile, mode filtering can control the maximum error within 2m under 95% of conditions.
In conclusion, the method makes full use of the distribution characteristics of the RSSI data, improves the quality of the fingerprint database compared with the average filtering, and ensures the operation efficiency;
compared with a single filtering method, the invention provides a novel RSSI data processing method, applies the mode to the filtering method for the first time, and provides a new idea for subsequent research;
compared with the prior art, the indoor fingerprint positioning method based on the mode filtering can avoid the RSSI ranging problem based on the Received Signal Strength (RSSI) position fingerprint positioning algorithm, and can construct a high-quality fingerprint database through filtering processing. Aiming at the problems that the Gaussian filtering operation time is long and the mean filtering positioning error is large, the RSSI distribution rule is analyzed, a mode filtering method is provided based on the characteristic that partial RSSI repeatedly appears, the mode in the current data is selected successively, and the characteristic RSSI is extracted by averaging until the ratio of the accumulated frequency sum of the selected mode to the total data amount is larger than 95%.
Experiments show that the fingerprint database constructed based on the mode filtering is similar to the Gaussian filtering positioning precision under 73% of conditions, but the operation efficiency is superior to that of Gaussian filtering. Meanwhile, compared with mean value filtering, mode filtering improves the positioning precision by about 0.15m, limits the maximum positioning error within 2m under the condition of 95%, and can effectively improve the quality of the fingerprint database.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (7)
1. The indoor fingerprint positioning method based on the mode filtering is characterized in that: the method comprises the following steps:
s1, according to the indoor wireless signal propagation characteristics, the RSSI is removed by using a box plot methodiAn abnormal data value in the sequence;
s2, counting the total RSSI data, establishing frequency and mode statistics of each group and initializing an optimal data ratio Z;
s3, selecting RSSI with large frequency by using the optimal data ratio Z to carry out mean filtering to calculate the characteristic value of the RSSI group;
s4, constructing a fingerprint database based on the RSSI characteristic value and the position coordinates of the corresponding reference point;
s5, selecting an optimal data ratio Z by using an exhaustion method;
and S6, solving the position of the indoor undetermined point based on the optimal fingerprint database through a WKNN (Weighted K-Nearest Neighbor) algorithm.
2. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S1 includes the steps of:
s1.1, forming a sequence by RSSI data collected from various known reference points indoors;
s1.2, abnormal value detection is carried out on each group of data through a box plot: if an abnormal value exists, replacing the abnormal value by the average value before and after the abnormal value; otherwise, the next step is performed.
3. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S2 is: and receiving RSSI data of each indoor wireless signal transmitting station for each reference point to create statistics, recording the total quantity of each group of RSSI data as N, and initializing a data ratio Z to be 0.6.
4. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S3 includes the steps of:
s3.1, recording current RSSI sequence mode RSSIiAnd the frequency m of the data is set to K +1, P is set to P + m, and the data ratio Z is set to P/N;
s3.2, eliminating RSSI in the current sequenceiForming a new RSSI sequence;
s3.3, judging whether the mode filtering search Z is less than P/N and meets the stop condition: if yes, continuing to step S2.6; otherwise, returning to the step S3.2;
and S3.4, calculating the arithmetic mean value of K RSSI of the selected frequency rows before the change of the array as the characteristic value of the group of RSSI.
5. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S4 is: calculating characteristic values of all known indoor reference points by using the mode filtering method based on the initial data ratio Z to form fingerprint data; obtaining position coordinates (X, Y) of all indoor reference points by a measuring method; combining the fingerprint data and the position coordinates together to construct a fingerprint database.
6. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S5 includes the steps of:
s5.1, selecting 0.05 as an exhaustion step length, and enabling Z to be Z + 0.05;
s5.2, resetting the frequency number accumulation statistic P, selecting the mode number accumulation K and the total RSSI data quantity N, and enabling P to be 0, K to be 0 and N to be the number of the current RSSI array;
s5.3, repeating the steps S4 and S5, and establishing a fingerprint database based on the current Z value;
s5.4, calculating the error in the current positioning through a WKNN algorithm;
and S5.5, judging whether the exhaustive Z is 1 and whether the stopping condition is met: if yes, continuing to step S5.6; otherwise, returning to the step S5.1;
s5.6, comparing the total positioning errors of the fingerprint databases corresponding to the different Z values, and selecting the Z corresponding to the fingerprint database with the minimum error as the optimal data ratio.
7. The mode filtering based indoor fingerprint positioning method of claim 1, wherein: the step S6 is: collecting selected point data at an indoor undetermined point through receiving equipment, and calculating selected point fingerprint data based on an optimal data Z value determined by an exhaustion method; and searching and comparing the selected point fingerprint data with a fingerprint database, and selecting K adjacent points to perform weighted average calculation on coordinates (X, Y) of the undetermined point.
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