CN116828596B - High-precision positioning method using RSSI - Google Patents
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Abstract
The application discloses a high-precision positioning method using RSSI, which comprises the following steps of receiving a plurality of groups of RSSI data and performing filtering treatment to reduce fluctuation between an RSSI measured value and a true value; modeling and calibrating according to the relation between the RSSI measured value and the distance after the filtering processing in the step one by adopting a lognormal distribution model; reading the calibrated information in the second step and estimating distance information; and then carrying out positioning calculation, obtaining distance estimation results corresponding to the number of the gateways, grouping the gateways, and then calculating the positioning positions to obtain a final positioning result. The application provides various schemes for positioning by using the RSSI ranging result, and by the positioning schemes, the influence of the gateway with larger RSSI error on the final result can be obviously improved, and the positioning precision is improved.
Description
Technical Field
The application relates to the technical field of wireless communication, in particular to a high-precision positioning method using RSSI.
Background
In wireless communication, positioning is receiving more and more attention as a basis for intelligent perception. The method of positioning includes positioning using angle measurement information, positioning using delay information, and positioning using RSSI (Received Signal Strength Indicator). The use of angle information for positioning requires a high precision angle measuring device, and the precision of angle positioning is further deteriorated as the scene becomes larger. The use of delay information for positioning requires high-precision synchronization between a plurality of devices, and high-precision crystal oscillator and synchronization schemes can cause high positioning cost. As for the positioning using RSSI, the scheme is simple and easy to realize as a synchronous and high-precision angle measurement scheme is not needed. In addition, the RSSI can be easily obtained from common WiFi equipment and Internet of things equipment, so that the scheme can be used in various low-cost positioning scenes and has wide application scenes.
However, in the prior art, parameters need to be updated in real time, so that the calculated amount is large; in addition, some techniques also need to use correlations between multiple moments in a mobile scenario, which can be used to solve the tracking problem, but are not applicable to a positioning scenario where the positions of the target at each moment are independent.
Disclosure of Invention
The application aims to solve the problems of large calculated amount and insufficient positioning precision in the RSSI positioning in the prior art, and provides a high-precision positioning method using RSSI.
In order to achieve the above purpose, the technical scheme adopted by the application is as follows: a high accuracy positioning method using RSSI, comprising the steps of:
step one, receiving a plurality of groups of RSSI data and performing filtering processing to reduce fluctuation between an RSSI measured value and a true value;
modeling and calibrating the relation between the RSSI measured value and the distance after the filtering processing in the first step by adopting a lognormal distribution model;
step three, reading the calibrated information in the step two and estimating distance information;
and fourthly, positioning and resolving, namely acquiring distance estimation results corresponding to the number of the gateways, grouping the gateways, performing RSSI estimation errors on each group of grouped distance estimation results, and calculating the positioning positions to obtain a final positioning result.
Further, the third step is specifically as follows: after the calibration stage is completed in the second step, the calibrated environmental factors and the calibrated correction values are read or the environmental factors and the environmental correction values are read from the outside, and then the estimated value of the distance is calculated through the lognormal distribution model.
Further, step two uses a lognormal distribution model to model the relationship between the RSSI measurement and the distance as follows
Wherein the method comprises the steps ofRepresentative distance isThe time-filtered RSSI information is used to determine,the representative distance is 1 meter,representative distance isThe RSSI measurements at the time of the time,representing the environmental factor of the human body,is an environmental correction value.
Further, the methodThe specific operation of the filtering process in the first step is as follows, let the number of received RSSI measurement data packets be K packets, wherein a certain packet isAnd K is more than or equal to 1 and less than or equal to K, M RSSI values are acquired from large to small, and median output is calculated and used as RSSI measurement values after the positioning point is filtered, and then the M RSSI measurement results are
The filtered RSSI result is the median of the above M RSSI measurements, namely:
if M is an odd number, then
If M is an even number, then
）/2
Or the M RSSI measurements after being averaged and output as the filtered measurement result, namely
，
Wherein the method comprises the steps ofAnd representing the filtered RSSI measurement result, wherein M represents the mth RSSI measurement result, and M is more than or equal to 1 and less than or equal to M.
Further, the filtering process is performed in the first step to delete the outlier by using the characteristic of large outlier fluctuation, specifically, the number of the received RSSI measurement value data packets is K packets, wherein any packet isK is more than or equal to 1 and less than or equal to K, and an outlier judgment threshold is set as
Wherein the method comprises the steps ofRepresenting an abnormal value judgment threshold, enabling I RSSI measured values to remain after deletion, then taking the median to filter the deleted measured results,
if I is an odd number
If I is an even number
Or average the RSSI measured value result after deleting the abnormal value
Where I is any one of the remaining I RSSI measurements after deletion.
Further, the filtering processing in the first step is a filtering method using gaussian weighting and thresholding, specifically, the number of received RSSI measurement data packets is K packets, where a certain packet isAnd K is more than or equal to 1 and less than or equal to K, and the mean and variance of the multi-packet RSSI measured values are calculated, whereinThe average value of the RSSI is shown,representing variance of RSSI
The measured value of RSSI is then weighted according to a Gaussian model
If the weight isLess than or equal to the threshold, then the RSSI measurement is deleted, e represents the base of the natural constant, let the number of RSSIs left after deletion be J, where the measurement of the RSSI of the jth packet is expressed asThe weights are expressed as，
The weight factors are then normalized
The final RSSI filtered result is
。
Further, the filtering in the first step is a filtering method using the deviation value of the RSSI measurement value as the weighted filtering weight, specifically, the number of the received RSSI measurement value data packets is K packets, wherein a certain packet isCalculating the average value of the multi-packet RSSI measurement results as
Then calculate the weight of each packet of data ifThenOtherwise
The weights of the RSSI are then normalized
The final RSSI filtered result is
，
In the formulaRepresents normalized weights, whereThe average value of RSSI is shown.
Further, in the positioning calculation in the fourth step, the gateway is firstly grouped into groups 3L P and 3L P according to L, and is divided into two groupsGroups of whichRepresenting the combined solving symbol, wherein P is the number of gateways, or Q gateways L which are the largest in RSSI measurement result are selected from the P gateways, and then the Q gateways are grouped according to each group of L gateways.
Further, in the positioning calculation, after grouping, the method for performing the position estimation is as follows: estimating a target position using the grouped z-th group distance information, letting
Wherein the method comprises the steps ofRepresenting the x-direction coordinates of the L-th gateway,representing the y-direction coordinates of the L-th gateway, x and y representing the x-direction and y-direction coordinates of the terminal to be estimated respectively,representing a location coordinate matrix constructed from gateway locations,a vector representing the estimated result of the terminal, R represents the square of the original distance of the terminal from the coordinates,
order the
Where n represents an environmental factor, A represents an environmental correction value,represents the L-th gateway filtered RSSI measurement, whereAnd (2) andb represents the distance relationship between the terminal and the gateway constructed from the filtered RSSI results,representing the distance between the L-th gateway and the origin of coordinatesSquare, and constructing a formula according to the above measured values
The estimation result of the terminal position is thatWherein T represents the transpose of the vector and matrix;
the final coordinate position of the terminal is estimated by using the distance weighting specifically as follows
Wherein the method comprises the steps of
Wherein,represents the firstThe distance results of the individual gateways using the filtered RSSI estimates,is the firstThe weight of the group is determined by the weight of the group,representing the normalization factor.
Further, in the positioning calculation, after grouping and position estimation, the position error is calculated in the following manner,
wherein the method comprises the steps ofRepresents the firstThe RSSI estimates for the individual gateways,representative distance isRSSI measurements at time;representing ambient correction values, whereRepresenting the distance between the terminal position estimation result and the gateway, expressed as
Defining the estimation error of the z-th set of results as
In addition, the position estimation result of the z-th group is defined as
Then deleting Y positioning results with the largest estimation error and the corresponding estimation error from the Z group positioning results, wherein Y is more than or equal to 0 and less than or equal to Z-1, the number of the positioning results left after deletion is Z ', and Z' is any group of positioning results in Z ', and then carrying out weighted fusion on the positioning results of the rest groups of gateways, wherein the weight of the Z' group is
The weights are then normalized
Finally, a weighting method is used for obtaining the position of the target, whereinAs a final positioning result of
Z' in the formula is the number of positioning results left after deletion,for the position estimation result of the z' th group,the results are normalized for the weights of the z' th group.
Compared with the prior art, the application has the following advantages:
1. the filtering scheme of the scheme can improve the ranging accuracy based on RSSI;
2. the positioning scheme of the scheme can obtain higher positioning precision;
3. the credibility of the positioning result under the group can be given through the estimation error of the scheme;
4. the influence of the large-error RSSI gateway on the positioning result can be obviously reduced by the estimation error weighting and selecting method;
5. according to the filtering scheme of multi-packet data in the RSSI positioning process, the reliability of the ranging result of the RSSI after filtering is improved. According to the grouping scheme of the plurality of gateway data, the RSSI ranging result is utilized to conduct positioning, and through the positioning schemes, the influence of the gateway with larger RSSI error on the final result can be remarkably improved, and the positioning accuracy is improved.
Drawings
Fig. 1 is a schematic block flow diagram of the present application.
Description of the embodiments
To make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be clearly and completely described below, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, based on the embodiments of the application, which are apparent to those of ordinary skill in the art without inventive faculty, are intended to be within the scope of the application. Accordingly, the detailed description of the embodiments of the application provided below is not intended to limit the scope of the application as claimed, but is merely representative of selected embodiments of the application.
Example 1
A high-precision positioning method using RSSI is shown in figure 1, which is a flow chart of the application, and is mainly realized by the processes of filtering, modeling RSSI information and distance information, calibrating, reading fitting calibration information, estimating the distance information and positioning and resolving.
The filtering scheme is that the RSSI measurement information is easily influenced by environment and various interferences, so that larger fluctuation is caused. Therefore, when the RSSI information is used for positioning and ranging, the present embodiment uses the multi-packet (a plurality of data units represented by the multi-packet data) RSSI data and performs filtering processing, so as to reduce the environmental impact and improve the range estimation accuracy, as follows.
Let the number of received data packets be K packets, wherein a certain packet isAnd K is more than or equal to 1 and less than or equal to K, and the filtering operation is as follows
The median output is calculated using the largest first M RSSI values as the anchor filtered RSSI measurement, since the RSSI measurement is subject to large fluctuations due to noise, whereas the larger the RSSI value, the larger the signal energy is represented and therefore the less the measurement is subject to noise. Therefore, the original K RSSI measurement results are ordered from large to small, and M (M is less than or equal to 1 and less than or equal to K) RSSI measurement results are downwards taken from the maximum value as
The filtered RSSI result is the median of the above M RSSI measurements, namely:
if M is an odd number, then
If M is an even number, then
）/2
Wherein the method comprises the steps ofRepresenting the filtered RSSI measurements.
Or the signal processing flow of the filtering scheme averages the K sorted RSSI measurement results and outputs the averaged RSSI measurement results as the filtered measurement results, namely
。
RSSI information and distance information modeling and calibration
The scheme uses a lognormal distribution model to model the relation between RSSI measurement values and distance
。
Wherein the method comprises the steps ofRepresentative distance isThe time-filtered RSSI information is used to determine,the representative distance is 1 meter,representative distance isThe RSSI measurements at the time of the time,representing the environmental factor of the human body,is an environmental correction value. Environmental factorAnd an environmental correction valueThe classical values of (c) are shown in the table below,
in the lognormal distribution model, there are 2 unknowns, which are respectively environmental factorsAnd correction value. First it is necessary to determine if the system is in the calibration phase, we estimate the environmental factor using a number of known location informationAnd an environmental correction value. Or using classical values of environmental factors (table aboveDescribed) as an environmental factorAnd correction valueThis block calibration is estimated as prior art and is therefore not described in detail.
Reading fitting calibration information and estimating distance information
After the calibration phase is completed, the calibrated environmental factors are readAnd correction value A or from outside (environmental factorAnd an environmental correction valueClassical value table of (a) read experience environmental factorAnd an environmental correction value A, and then calculating an estimated value of the distance by a lognormal distribution model.
Location solution
When there are P gateways (or base stations, WIFI devices) (the present application is applicable to the gateways and the base stations or WIFI devices, hereinafter collectively referred to as gateways for convenience), P distance estimation results may be obtained, where the distance estimation result of the P (1. Ltoreq. P) th gateway is. Next, it will be explained how the position of the target is estimated using the distance information.
Firstly, the gateways are grouped according to L (L is more than or equal to 3 and less than or equal to P) and are divided into the following groups togetherGroups of whichRepresentative combination determinationDe-symbolizing to make the number after grouping be。
When the number of gateways is large, for example, the number of gateways is 10, and grouping is performed according to every 3 gateways, then the gateways need to be divided into 120 groups) In some scenarios, such a huge amount of computation cannot be tolerated. Therefore, the scheme can be simplified to firstly select Q gateways (L.ltoreq.Q.ltoreq.P) with the largest RSSI measurement result from P gateways, and then group the Q gateways according to L groups, so that the grouping number can be remarkably reduced.
Then, position estimation is performed: estimating a target position using the grouped z-th group distance information, letting
Wherein the method comprises the steps ofRepresenting a location coordinate matrix constructed from gateway locations,a vector representing the result of the estimation of the terminal,representing the x-direction coordinates of the L-th gateway,representing the y-direction coordinate of the L-th gateway, R represents the square of the original distance between the terminal and the coordinate, x and y represent the x-direction coordinate and the y-direction coordinate of the terminal to be estimated respectively, so that
Where b represents the distance relationship between the terminal and the gateway constructed from the filtered RSSI results, n represents the environmental factor, a represents the environmental correction value,represents the L-th gateway filtered RSSI measurement, whereAnd (2) and，representing the square of the distance between the L-th gateway and the origin of coordinates. And constructing a formula according to the measured values
The final terminal position estimation result is that
。
Then calculate the error of the packet estimation result
Wherein the method comprises the steps ofRepresents the firstRSSI estimates for individual gateways, whereRepresenting the distance between the terminal position estimation result and the gateway, expressed as
Defining the estimation error of the z-th set of results as
In addition, the position estimation result of the z-th group is defined as
Then weighting and fusing the positioning results of a plurality of groups of gateways to make the weight of the z-th group be as follows
The weights are then normalized
Finally, a weighting method is used for obtaining the position of the target, whereinFinal positioning result for terminal
。
Or the weighting method may take the group estimation result corresponding to the minimum estimation error ERR as the final positioning result, for example, if the 3 rd is the minimum error estimation, then considerFor the smallest estimation error in all groups, the final positioning result is
。
Or weighting method can be used to estimate the error by ZAnd according to the sequence from large to small, deleting the estimation results corresponding to the Y measurement errors from large to small, and carrying out weighted fusion on the rest Z-Y estimation errors of the results and the estimation results according to a necessary scheme to obtain a final positioning result.
Example 2
The embodiment is based on embodiment 1, and mainly improves the filtering scheme in the embodiment to completely conform to other technical contents, and the specific filtering scheme is as follows;
let the number of received data packets be K packets, wherein a certain packet isAnd K is equal to or greater than 1 and K is equal to or less than K, the filtering operation is as follows, and the median output is calculated by using the largest first M RSSI values as the RSSI measured value after the positioning point is filtered, and the RSSI measured result is susceptible to noise to cause larger fluctuation, but when the RSSI value is larger, the signal energy is represented to be larger, and therefore the influence of the noise on the measured result is smaller. The original K RSSI measurements are therefore ordered from large to small.
The scheme uses the characteristic of large abnormal value fluctuation to delete abnormal value, firstly the abnormal value judgment threshold is
Wherein the method comprises the steps ofRepresenting an outlier decision threshold, leaving I RSSI measurements after deletion, and then filtering the deleted measurements using a median-taking scheme.
If I is an odd number
If I is an even number
。
Or by averaging the result after deleting the outlier, the result is
I is any one of the remaining I RSSI values.
Example 3
The embodiment is based on embodiment 1, and mainly improves the filtering scheme in the embodiment to be completely consistent with other technical contents, and the embodiment adopts a Gaussian weighted sum thresholding filtering method
The specific filtering scheme is as follows;
first, the mean and variance of multi-packet RSSI information is calculated, whereinThe average value of the RSSI is shown,representing variance of RSSI
The measured value of RSSI is then weighted according to a Gaussian model
If the weight isLess than (or equal to) the threshold valueThen the RSSI measurement is deleted.
Let the number of RSSIs left after deletion be J, where the measurement of the RSSI of the jth packet is expressed asThe weights are expressed as。
The weight factors are then normalized
The final RSSI filtered result is
。
Example 4
The present embodiment is based on embodiment 1, and mainly improves the filtering scheme in the embodiment to make other technical contents completely consistent, and the present embodiment uses the deviation value of RSSI as the weighted filtering, and the specific filtering scheme is as follows;
first, calculate the average value of multi-packet RSSI measurement results
Then calculate the weight of each packet of data ifThen. Otherwise
The weights of the RSSI are then normalized
The final RSSI filtered result is
。
Wherein the method comprises the steps ofIs the average value of the RSSI value,the weight normalized value representing the RSSI,representative ofThe weight of the value.
Example 5
The present embodiment is an improvement based on any one of embodiments 1 to 4, and is mainly to optimize a position estimation scheme in a positioning calculation link, and other technical schemes are consistent with those in the foregoing embodiments. The position estimation scheme in the positioning calculation link is specifically as follows: estimating a target position using the grouped z-th group distance information, letting
Wherein the method comprises the steps ofRepresenting the x-direction coordinates of the L-th gateway,representing the y-direction coordinates of the L-th gateway. x and y represent the x-direction and y-direction coordinates of the terminal to be estimated, respectively
Order the
Where n represents an environmental factor, A represents an environmental correction value,represents the L-th gateway filtered RSSI measurement, whereAnd (2) and
the final Z terminal position estimation results are
The present embodiment then uses a distance weighted scheme to estimate the terminal position with coordinates of
Wherein the method comprises the steps of
Wherein,represents the firstThe distance results of the individual gateways using the filtered RSSI estimates (where the calculation formula of the distance results please refer to the lognormal distribution model),representing the normalization factor.
Example 6
The present embodiment is an improvement based on any one of embodiments 1 to 4, and is mainly to optimize a position estimation scheme in a positioning calculation link, and other technical schemes are consistent with those in the foregoing embodiments. The position estimation scheme in the positioning calculation link is specifically as follows: estimating a target position using the grouped z-th group distance information, letting
Wherein the method comprises the steps ofRepresenting the x-direction coordinates of the L-th gateway,representing the y-direction coordinates of the L-th gateway. x and y represent the x-direction and y-direction coordinates of the terminal to be estimated, respectively
Order the
Where n represents an environmental factor, A represents an environmental correction value,represents the L-th gateway filtered RSSI measurement, whereAnd (2) and
the final terminal position estimation result is that
The present embodiment then uses a distance weighted scheme to estimate the terminal position, i.e
Wherein,represents the firstDistance result obtained by using filtered RSSI estimation of each gateway, and formulaE.g. l=3, where the firstThe gateway calculates the weight, thenRepresented asAnd so on.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that the above-mentioned preferred embodiment should not be construed as limiting the application, and the scope of the application should be defined by the appended claims. It will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the spirit and scope of the application, and such modifications and adaptations are intended to be comprehended within the scope of the application.
Claims (6)
1. A high precision positioning method using RSSI, comprising the steps of:
step one, receiving a plurality of groups of RSSI data and performing filtering processing to reduce fluctuation between an RSSI measured value and a true value;
modeling and calibrating the relation between the RSSI measured value and the distance by adopting a lognormal distribution model according to the relation between the RSSI measured value and the distance after the filtering processing in the step one, and modeling the relation between the RSSI measured value and the distance by adopting the lognormal distribution model as follows
Wherein RSSI filter Representing the filtered RSSI information at a distance d, d 0 Representing a distance of 1 meter, P 0 Representative distance d 0 RSSI measured value, n represents environmental factor, A is environmental correction value;
step three, reading the calibrated information in the step two and estimating distance information;
step four, positioning calculation, namely obtaining distance estimation results corresponding to the number of the gateways, grouping the gateways, performing RSSI estimation errors on each group of grouped distance estimation results, and calculating the positioning positions to obtain a final positioning result;
the grouping in the positioning calculation adopts the following mode that the gateway is firstly grouped into groups with L being more than or equal to 3 and less than or equal to P according to L, and the groups are divided into the following groups togetherThe method comprises the steps of a plurality of groups, wherein C represents a combined solving symbol, P is the number of gateways, or Q gateways L which are the largest in RSSI measurement result are selected from the P gateways, and then the Q gateways are grouped according to L groups;
in the positioning calculation, after grouping, the method for performing the position estimation is as follows: estimating a target position using the grouped z-th group distance information, letting
Wherein x is L Representing the x-direction coordinate, y of the L-th gateway L Represents the y-direction coordinate of the L-th gateway, x and y represent the x-direction coordinate and the y-direction coordinate of the terminal to be estimated respectively, A 'represents a position coordinate matrix constructed according to the gateway position, theta' represents the vector of the estimation result of the terminal, R represents the square of the original distance between the terminal and the coordinate,
order the
Where n represents an environmental factor, A represents an environmental correction value,represents the filtered RSSI measurement of the L-th gateway, wherein +.>And r=x 2 +y 2 B represents the distance relationship between the terminal and the gateway constructed from the filtered RSSI result, R L Represents the square of the distance between the L-th gateway and the origin of coordinates, and constructs a formula based on the above measured values
A'×θ'＝b
The terminal position estimation result is θ '= ((a') tox (a ')) -1× (a') tox b, where T represents the transpose of the vector and matrix;
the final coordinate position of the terminal is estimated by using the distance weighting specifically as follows
Wherein the method comprises the steps of
Wherein,represents the distance result, w, of the first gateway using the filtered RSSI estimate l Weight of the first group, w norm Representing a normalization factor;
in the positioning calculation, after grouping and position estimation, the position error is calculated in the following manner,
wherein the method comprises the steps ofRepresents the RSSI estimate of the first gateway, P 0 Representative distance d 0 RSSI measurements at time; a represents an environmental correction value, wherein->Representing the distance between the terminal position estimation result and the gateway, expressed as
Defining the estimation error of the z-th set of results as
The position estimation result of the z-th group is defined as theta' z ，
Then deleting Y positioning results with the largest estimation error and the corresponding estimation error from the Z group positioning results, wherein Y is more than or equal to 0 and less than or equal to Z-1, the number of the positioning results left after deletion is Z ', and Z' is any group of positioning results in Z ', and then carrying out weighted fusion on the positioning results of the rest groups of gateways, wherein the weight of the Z' group is
w z' ＝1/ERR z'
The weights are then normalized to be the same,
finally, a weighting method is used for obtaining the position of the target, wherein theta final As a final positioning result of
Z 'in the formula is the number of positioning results left after deletion, theta' z' For the position estimation result of the z 'th group, w' z' The results are normalized for the weights of the z' th group.
2. The high-precision positioning method using RSSI as set forth in claim 1, wherein the third step is as follows: after the calibration stage is completed in the second step, the calibrated environmental factors and the calibrated correction values are read or the environmental factors and the environmental correction values are read from the outside, and then the estimated value of the distance is calculated through the lognormal distribution model.
3. The method of claim 1 wherein the filtering in step one is performed by setting the number of received RSSI measurement data packets to K packets, wherein one packet is RSSI k And K is more than or equal to 1 and less than or equal to K, M RSSI values are acquired from large to small, and median output is calculated and used as RSSI measurement values after the positioning point is filtered, and then the M RSSI measurement results are
RSSI′ 1 ,RSSI′ 2 …RSSI′ M
The filtered RSSI result is the median of the above M RSSI measurements, namely:
if M is an odd number, then
If M is an even number, then
Or the M RSSI measurements after being averaged and output as the filtered measurement result, namely
Wherein RSSI' filter And representing the filtered RSSI measurement result, wherein M represents the mth RSSI measurement result, and M is more than or equal to 1 and less than or equal to M.
4. A high accuracy positioning using RSSI as set forth in claim 1The method is characterized in that the filtering process is performed in the first step by deleting the outlier by utilizing the characteristic of large outlier fluctuation, specifically, the number of the received RSSI measurement value data packets is K packets, wherein any packet is RSSI k K is more than or equal to 1 and less than or equal to K, and an outlier judgment threshold is set as
Wherein th1 represents an outlier decision threshold, so that I RSSI measurement values remain after deletion, then median is taken to filter the deleted measurement results,
if I is an odd number
RSSI′ filter ＝RSSI (I+1)/2
If I is an even number
Or average the RSSI measured value result after deleting the abnormal valueWhere I is any one of the remaining I RSSI measurements after deletion.
5. The method of claim 1 wherein the filtering in step one is a gaussian weighted thresholding filtering method, specifically comprising the steps of making the number of received RSSI measurement data packets K packets, wherein one of the packets is RSSI k And K is more than or equal to 1 and less than or equal to K, and the mean and variance of the multi-packet RSSI measured values are calculated, wherein the RSSI is calculated by the method mean Mean value of RSSI, RSSI var Representing variance of RSSI
The measured value of RSSI is then weighted according to a Gaussian model
If weight k Less than or equal to the threshold, then the RSSI measurement is deleted, e represents the base of a natural constant, let the number of RSSIs left after deletion be J, where the measurement of the RSSI of the jth packet is expressed as RSSI' j The weight is expressed as weight' j ，
The weight factors are then normalized
The final RSSI filtered result is
6. The method of claim 1, wherein the filtering in the first step is performed by using a deviation value of the RSSI measurement value as a weighted filtering weight, and specifically, the number of received RSSI measurement value data packets is K packets, wherein a certain packet is RSSI k Calculating the average value of the multi-packet RSSI measurement results as
Then calculate the weight of each packet of data, if RSSI k ＝RSSI mean Then weight is high k =1, otherwise
weight k ＝1-(RSSI k -RSSI mean )/RSSI mean
The weights of the RSSI are then normalized
The final RSSI filtered result is
Weight 'in the formula' k Represents normalized weights, where RSSI mean The average value of RSSI is shown.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103687000A (en) * | 2013-12-17 | 2014-03-26 | 上海交通大学无锡研究院 | Wireless sensor network indoor localization distance measurement optimization method based on RSSIs |
| CN103997717A (en) * | 2014-06-12 | 2014-08-20 | 福建师范大学 | Real-time indoor positioning system and method |
| CN105813020A (en) * | 2016-04-15 | 2016-07-27 | 合肥工业大学 | RSSI corrected wireless sensor network positioning algorithm of self-adaptive environment |
| CN107247260A (en) * | 2017-07-06 | 2017-10-13 | 合肥工业大学 | A kind of RFID localization methods based on adaptive depth confidence network |
| CN108535687A (en) * | 2018-03-20 | 2018-09-14 | 西安电子科技大学 | Indoor wireless positioning method based on the fusion of TOF and RSSI information |
| CN109709513A (en) * | 2019-01-25 | 2019-05-03 | 中广核研究院有限公司 | A kind of indoor application is in nuclear power station localization method and system |
| CN111479231A (en) * | 2020-04-17 | 2020-07-31 | 西安交通大学 | Indoor fingerprint positioning method for millimeter wave large-scale MIMO system |
| CN116008976A (en) * | 2023-03-27 | 2023-04-25 | 四川思凌科微电子有限公司 | Fusion ranging method for CHIRP signal |
| CN116359838A (en) * | 2023-03-24 | 2023-06-30 | 哈尔滨工程大学 | Positioning resolving model for indoor low-power consumption Bluetooth |
-
2023
- 2023-08-28 CN CN202311086631.5A patent/CN116828596B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103687000A (en) * | 2013-12-17 | 2014-03-26 | 上海交通大学无锡研究院 | Wireless sensor network indoor localization distance measurement optimization method based on RSSIs |
| CN103997717A (en) * | 2014-06-12 | 2014-08-20 | 福建师范大学 | Real-time indoor positioning system and method |
| CN105813020A (en) * | 2016-04-15 | 2016-07-27 | 合肥工业大学 | RSSI corrected wireless sensor network positioning algorithm of self-adaptive environment |
| CN107247260A (en) * | 2017-07-06 | 2017-10-13 | 合肥工业大学 | A kind of RFID localization methods based on adaptive depth confidence network |
| CN108535687A (en) * | 2018-03-20 | 2018-09-14 | 西安电子科技大学 | Indoor wireless positioning method based on the fusion of TOF and RSSI information |
| CN109709513A (en) * | 2019-01-25 | 2019-05-03 | 中广核研究院有限公司 | A kind of indoor application is in nuclear power station localization method and system |
| CN111479231A (en) * | 2020-04-17 | 2020-07-31 | 西安交通大学 | Indoor fingerprint positioning method for millimeter wave large-scale MIMO system |
| CN116359838A (en) * | 2023-03-24 | 2023-06-30 | 哈尔滨工程大学 | Positioning resolving model for indoor low-power consumption Bluetooth |
| CN116008976A (en) * | 2023-03-27 | 2023-04-25 | 四川思凌科微电子有限公司 | Fusion ranging method for CHIRP signal |
Non-Patent Citations (2)
| Title |
|---|
| 基于卡尔曼滤波与中位加权的定位算法;肖如良;李奕诺;江少华;梅忠;蔡声镇;;计算机应用(12);全文 * |
| 非视距条件下的测距信息融合定位方法研究;李娅菲;朱烜璋;;电子测量与仪器学报(05);全文 * |
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