CN111198365A - Indoor positioning method based on radio frequency signal - Google Patents
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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Abstract
The invention discloses an indoor positioning method based on radio frequency signals, which firstly shortens the online positioning time by constructing a fingerprint database in advance based on a position fingerprint positioning method, acquires the intensity of received signals to construct a fingerprint database, and determines the position of a terminal based on a fingerprint database matching method in an online stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.
Description
Technical Field
The invention relates to the technical field of indoor positioning, in particular to an indoor positioning method based on radio frequency signals.
Background
With the development of positioning technology, people are more and more widely used for positioning, and meanwhile, the requirement of people for positioning is continuously increased. However, most of the currently used positioning technologies are based on GPS services, and GPS signals cannot achieve positioning effects outdoors indoors, so that a new technology needs to be sought to improve the situation of poor indoor positioning accuracy.
Due to the rapid popularization of radio frequency equipment such as WLAN and the like, a hardware technical basis is laid for a positioning method based on radio frequency signals, and the rapid popularization and use are facilitated. At present, a positioning technology based on radio frequency signals at home and abroad has been developed primarily, the positioning technology based on position fingerprints is one of main research directions, and the position fingerprint positioning method is used for completing indoor positioning based on the mapping relation between received signal strength information and position information.
The establishment of the position fingerprint database is one of key technologies for realizing the method, generally, the position fingerprint database stores the received signal strength information acquired in an off-line stage, and the position fingerprint positioning determines the position of the terminal based on a KNN method by matching the radio frequency signal strength acquired in the on-line stage with the received signal strength information stored in the database. However, the signal transmission and reception are easily blocked, which causes errors between the signal strength acquired in real time and the signal in the database, and affects the real-time positioning accuracy of the terminal in the online stage.
Disclosure of Invention
To the deficiency of the prior art, the technical problem to be solved by the present patent application is: how to provide an indoor positioning method based on radio frequency signals, which can effectively estimate track information of a terminal, effectively reduce positioning errors caused by signal shielding and perform real-time positioning of the terminal in an environment with signal shielding.
In order to achieve the purpose, the invention adopts the following technical scheme:
an indoor positioning method based on radio frequency signals comprises the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i，yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i，yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', gamma ') by using a weighted KNN algorithm, entering a step S7 if the position fingerprint database does not contain terminal track information, and entering a step S3 if the position fingerprint database contains the terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1，γ1)；
S4: based on terminal location (x)1，y1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2，y2)；
S6: comparing the corrected predicted position information (x)2，y2) Comparing the error with a threshold value with the position information (x ', y') of the computing terminal, and if the error is higher than the threshold value, (x) is calculated2，γ2) As final terminal position information (x ', γ'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', y'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
Further, in step S1, the location fingerprint database RSS and location information (x)i，yi) The mapping model is as follows:
wherein, N is the number of the collected information, and K is the vector number of RSS.
Further, in step S1, the received signal strength information RSSiThe following were used:
further, in step S2, the weighted KNN algorithm has the following formula:
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri，cAre signals in the database.
Further, in step S3, the method for calculating the predicted terminal position by the autoregressive moving average model is as follows:
Pt＝β1Pt-1+β2Pt-2+…+βPPt-P+Zt
wherein, the track Pt＝(xi，yi) β is the weight of the member star position at each time, and there is also a correlation between the error terms, denoted as Zt＝εt+α1εt-1+α2εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
Further, in step S4, based on the terminal position (x)1，γ1) Generating a population of particles (x, γ), said population generating method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1＝h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj＝g(RSSj-f(Gj))
wherein: f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weight are removed, and a new particle set G and a new weight w are obtained;
further, in step S5, the method for calculating the corrected predicted position includes:
the corrected predicted position (x)2，γ2) And calculating the terminal position (x)1，y1) The error calculation method is delta ═ PN+1-Pk|。
Compared with the prior art, the indoor positioning method based on the radio frequency signal has the following technical effects by adopting the technical scheme:
1) the invention relates to an indoor positioning method based on radio frequency signals, which comprises the steps of constructing a position fingerprint database by collecting received signal strength information and terminal position information in an off-line stage, and reducing positioning time in the on-line stage;
2) according to the indoor positioning method based on the radio frequency signals, in an online stage, an autoregressive sliding model is constructed based on the motion track of the terminal, the position of the terminal is predicted in advance, and positioning errors caused by signal shielding are reduced;
3) according to the indoor positioning method based on the radio frequency signal, the motion trail prediction method of the terminal is optimized based on the particle feedback method, the terminal position distribution is expanded by constructing the particle swarm, and the online positioning precision is improved;
4) the indoor positioning method based on the radio frequency signals calculates the terminal position based on the weighted KNN algorithm, obtains the terminal position by weighting through calculating the distance between the intensity of the real-time collected and received signals and the intensity of the signals stored in the database, and improves the precision of the positioning algorithm;
5) according to the indoor positioning method based on the radio frequency signals, the terminal position obtained through comparison calculation and the terminal position obtained through prediction are obtained, signal shielding information is obtained, and the influence of signal shielding on the positioning result is reduced.
The invention designs an indoor positioning method based on radio frequency signals, and aims to solve the problem that in the positioning process, when shielding exists between a signal source and a terminal, part of terminals cannot receive signals of a reference star, so that a positioning result has large errors. Aiming at the problem of signal shielding in positioning, the offset track is corrected by a particle filter feedback method based on the motion track of the terminal. Firstly, on-line positioning time is shortened by a mode of constructing a fingerprint library in advance based on a position fingerprint positioning method, received signal strength is collected to construct the fingerprint library, and the position of a terminal is determined based on a fingerprint library matching method in an on-line stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention based on an indoor positioning system for RF signals;
FIG. 2 is a schematic diagram of the positioning of the indoor positioning system based on RF signals according to the present invention;
fig. 3 is a comparison diagram of the positioning correction result of the rf signal based indoor positioning system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
As shown in fig. 1-2, an indoor positioning method based on radio frequency signals includes the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i，yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i，yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', y ') by using a weighted KNN algorithm, entering step S7 if the position fingerprint database does not contain terminal track information, and entering step S3 if the position fingerprint database contains terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1，γ1)；
S4: based on terminal location (x)1，γ1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2，y2)；
S6: comparing the corrected predicted position information (x)2，γ2) And calculating the terminal position information (x ', gamma'), comparing the error with a threshold, and if the error is higher than the threshold, (x) is calculated2，y2) As final terminal position information (x ', y'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', γ'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
In this embodiment, in step S1, the location fingerprint database RSS and location information (x)i，yi) The mapping model is as follows:
wherein, N is the number of the collected information, and K is the vector number of RSS.
In this embodiment, in step S1, the received signal strength information RSSiThe following were used:
in this embodiment, in step S2, the weighted KNN algorithm has the following formula:
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri，cAre signals in the database.
In this embodiment, in step S3, the method for calculating the predicted terminal position by the autoregressive moving average model is as follows:
Pt＝β1Pt-1+β2Pt-2+…+βPPt-P+Zt
wherein, the track Pt＝(xi，yi) β is the weight of the member star position at each time, and there is also a correlation between the error terms, denoted as Zt＝εt+α1εt-1+α2εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
In this embodiment, in step S4, the terminal position (x) is used as the basis1，y1) Generating a particle population (x, y), the particle population generation method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1＝h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj＝g(RSSj-f(Gj))
wherein: f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weight are removed, and a new particle set G and a new weight W are obtained;
in this embodiment, in step S5, the method for calculating the corrected predicted position includes:
the corrected predicted position (x)2，γ2) And calculating the terminal position (x)1，y1) The error calculation method is delta ═ PN+1-Pk|。
Fig. 3 shows the bit error rate simulation result of an indoor positioning method based on radio frequency signals. As can be seen from the figure, the uncorrected positioning result has larger error due to shielding, the influence of the error can be smaller by adopting the fitting method, and the positioning precision loss caused by shielding can be better reduced by adopting the method provided by the invention.
The invention discloses an indoor positioning method based on radio frequency signals, which aims at the problem that when shielding exists between a signal source and a terminal in the positioning process, part of terminals cannot receive signals of a reference star, so that a positioning result has large errors. Aiming at the problem of signal shielding in positioning, the offset track is corrected by a particle filter feedback method based on the motion track of the terminal. Firstly, on-line positioning time is shortened by a mode of constructing a fingerprint library in advance based on a position fingerprint positioning method, received signal strength is collected to construct the fingerprint library, and the position of a terminal is determined based on a fingerprint library matching method in an on-line stage. And aiming at the shielding phenomenon, a position prediction method is provided, and the position of the terminal is predicted by using an autoregressive moving average model. Because only the terminal motion track is used as basic information in the position prediction, the prediction effect is poor, in order to improve the precision of position correction, a filtering algorithm is proposed for optimization, the position of the terminal is predicted based on the terminal motion track through a filtering method, the position of the terminal is corrected based on the radio frequency signal intensity, the real-time position of the terminal is obtained through iteration in sequence, the positioning error caused by signal shielding is reduced, and the whole terminal positioning precision is effectively improved.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. An indoor positioning method based on radio frequency signals is characterized by comprising the following steps:
s1: RSS (received Signal Strength) of offline acquisition terminaliAnd location information (x)i，yi) Storing the data in a location fingerprint database as reference information to construct a location fingerprint database RSS and location information (x)i，yi) Mapping the model;
s2: acquiring terminal received signal strength information RSS ' on line, calculating terminal position information (x ', y ') by using a weighted KNN algorithm, entering step S7 if the position fingerprint database does not contain terminal track information, and entering step S3 if the position fingerprint database contains terminal track information;
s3: predicting the terminal position (x) using an autoregressive moving average model based on terminal trajectory information1，y1)；
S4: based on terminal location (x)1，y1) Generating a particle swarm (x, gamma), calculating a particle swarm signal strength RSS based on a location fingerprint database RSS construction modeliComparing the RSS' with the RSSi', generating a particle weight W;
s5: correcting predicted position information (x) using particle weight w and particle group (x, y)2，y2)；
S6: comparing the corrected predicted position information (x)2，y2) And calculating the terminal position information (x ', gamma'), comparing the error with a threshold, and if the error is higher than the threshold, (x) is calculated2，y2) As final terminal position information (x ', y'), if the error is not higher than the threshold, (x ', y') is taken as final terminal position information (x ', γ'), and stored in the position fingerprint database as terminal trajectory information, and step S2 is entered to locate the terminal position at the next moment;
s7: the terminal position information (x ', γ') is stored as terminal trajectory information in the position fingerprint database, and the process proceeds to step S2 to locate the terminal position at the next time.
2. The method as claimed in claim 1, wherein in step S1, the location fingerprint database RSS and location information (x)i，yi) The mapping model is as follows:
wherein, N is the number of the collected information, and K is the vector number of RSS.
3. The method as claimed in claim 1, wherein in step S1, the received signal strength information RSS isiThe following were used:
4. the method according to claim 3, wherein in step S2, the weighted KNN algorithm has the following formula:
where D is the distance between two RSSs, c is the c-th value of the RSS vector, i refers to the i-th proximity, and RSScReal-time signal, RSS, acquisition for a useri，cAre signals in the database.
5. The method as claimed in claim 4, wherein in step S3, the calculation method for predicting the terminal position by the autoregressive moving average model is as follows:
Pt＝β1Pt-1+β2Pt-2+…+βPPt-P+Zt
wherein, the track Pt＝(xi，yi) β is the weight of the member star position at each moment, and there is a correlation between the error terms, expressed as
Zt＝εt+α1εt-1+α2εt-2+…+αpεt-p
Wherein Z istIs the total error of the current time, epsiloniError of the previous p moments, αiIs the weight of each error.
6. The method as claimed in claim 5, wherein the step S4 is based on terminal location (x)1，y1) Generating a particle population (x, y), the particle population generation method comprising:
predicting a particle swarm G at the moment by utilizing the particle swarm at the previous moment and a terminal motion model;
Gi+1＝h(Gi)+NG
where h () is the motion model, NGIs Gaussian distribution, N (0, δ d)2) D is the terminal movement distance at the last moment;
and updating the weight of each sample in the particle set by using the received signal strength data information of the previous moment and an observation equation, namely a received signal strength calculation model:
Wj＝g(RSSj-f(Gj))
wherein: and f (x, y, z) is an observation equation between the position and the signal intensity, P is power, all weights are normalized to obtain a sample set for predicting satellite state distribution, a resampling method is adopted, sequencing is carried out according to the weight, 1/2 samples with small weights are removed, and a new particle set G and a new weight W are obtained.
7. The method as claimed in claim 6, wherein in step S5, the method for calculating the corrected predicted position comprises:
the corrected predicted position (x)2，y2) And calculating the terminal position (x)1，y1) The error calculation method comprises
δ＝|PN+1-Pk|。
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