CN106941718B - Mixed indoor positioning method based on signal subspace fingerprint database - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- H04B17/309—Measuring or estimating channel quality parameters
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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
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- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
Abstract
The invention discloses a mixed indoor positioning method based on a signal subspace fingerprint library, which comprises an off-line stage and a positioning stage; in the off-line stage, on one hand, the array antenna is used for receiving signals to obtain the received signal strength, and a received signal strength fingerprint database is established; and in addition, calculating the received array signals to obtain signal subspace data, and establishing a signal subspace fingerprint library. In the positioning stage, firstly, matching is carried out according to data measured in an online process and a fingerprint database, probability distribution of offline coordinates is considered, information fusion is carried out by utilizing a Bayesian theory, and accurate positioning coordinates are finally obtained. The array antenna technology used by the invention is one of the key technologies of future mobile terminals, and the fingerprint database established after the array signal processing contains the intensity and arrival angle information of incident signals, so that the influence of channel time variation caused by multipath propagation and environmental change can be more effectively resisted, the indoor positioning precision can be effectively improved, and the positioning error can be reduced.
Description
Technical Field
The invention belongs to the field of signal processing, information fusion and indoor positioning, and particularly relates to a mixed indoor positioning method based on a signal subspace fingerprint library.
Background
The indoor positioning technology is used for realizing accurate positioning of positions in an indoor environment, and mainly adopts a set of indoor position positioning system consisting of multiple technologies such as wireless communication, base station positioning and the like to realize position services of personnel, objects and the like in an indoor space. Common positioning technologies include Wi-Fi, Bluetooth, infrared, ultra wide band, RFID, ZigBee and ultrasonic based positioning technologies.
The fingerprint database positioning technology can effectively resist indoor multipath propagation and can improve indoor positioning accuracy to a great extent. Currently, typical indoor fingerprint database positioning methods are roughly classified into a Received Signal Strength (RSSI) fingerprint database positioning technology, a spatial spectrum fingerprint database positioning technology, an RFID tag positioning technology, and the like. The method for positioning the received signal strength fingerprint database can only provide the received signal strength information, and the signal subspace stores the main information of the signal, so that the method for positioning the signal subspace fingerprint database can more effectively resist the influence of channel time variation caused by multipath propagation and environmental change.
A fifth generation mobile communication system using a smart antenna technology as a core is in a vigorous research and development stage, and research shows that most of useful information in a communication process is related to time and place, and it is expected that various services based on wireless positioning will be launched with wide application of 5G communication, and high-quality positioning service will become an urgent need. By receiving signals by the array antenna and performing array signal processing, desired useful signals can be enhanced, unwanted interference and noise can be suppressed, and useful signal characteristics and information contained in the signals can be extracted. Therefore, the signal subspace fingerprint library established by the array signal processing technology can more effectively resist the influence of channel time variation caused by multipath propagation and environmental change compared with the received signal strength fingerprint library.
From the above theoretical analysis, if a mixed indoor positioning method of two fingerprint libraries is adopted, auxiliary information for indoor mobile terminal positioning can be added, and then positioning information of the two fingerprint libraries is fused by using the bayesian theory, so that indoor positioning accuracy can be improved, and positioning errors can be reduced. Through search, the related content is not disclosed in the prior art.
Disclosure of Invention
The invention aims to solve the technical problem of obtaining a signal subspace by using an array antenna of a mobile terminal and establishing a signal subspace fingerprint library to obtain a mixed indoor positioning method based on the signal subspace fingerprint library. The method aims to perform mixed indoor positioning of two fingerprint libraries, increase auxiliary information which can be used for positioning an indoor mobile terminal, and further improve indoor positioning precision through an information fusion technology based on a Bayesian theory so as to reduce positioning errors.
In order to achieve the above object, the technical solution provided by the present invention is a mixed indoor positioning method based on a signal subspace fingerprint library, which specifically includes the following steps:
step (1): in the off-line stage, the received signal strength is measured according to the area coordinates (xi，yj) I 1, …, m, y 1, …, n, and the corresponding received signal strength R is measuredk(xi，yj) I is 1, …, m, j is 1, …, n, k is 1, …, L, and establishes a fingerprint library of received signal strength;
step (2): the array signal x (t) is measured off-line by using an array antenna of the mobile terminal, and an antenna array output vector x (t) can be represented as x (t) as (t) + n (t), where x (t) x ═ x (t)1(t)，x2(t)，…，xb(t)，]A is array direction matrix, s (t) is signal source, n (t) is noise;
and (3): obtaining a signal subspace U from x (t) by an array signal processing methods＝[u1，u2，…，uL]Wherein u is1，u2，…，uLThe eigenvectors corresponding to the L larger eigenvalues of the covariance matrix of the array signal are used to measure the signal subspace set { U ] of all the area coordinatess(xi，yj) 1, …, m, j 1, …, n }, and establishing a signal subspace fingerprint library;
and (4): on-line measurement of received signal strength vector R' (x) at a mobile terminali，yj) And signal subspace U's(xi，yj) Then, calculating in a fingerprint database according to a correlation algorithm to obtain matching coordinates;
and (5): performing fingerprint library matching training on each region coordinate to respectively obtain the error statistical probability P (d) of two fingerprint librariesR|(xi，yj) And P (d)U|(xi，yj))；
And (6): according to the error statistical probability of the two fingerprint libraries, information fusion is carried out by using Bayesian theory to obtain posterior probability P ((x) on each area coordinatei，yj)|dR，dU)；
Step (a)7): obtaining accurate positioning coordinates of the mobile terminal by utilizing the maximum posterior probability criterion
Further, the step 2 specifically includes: defining uniform linear array composed of b antennas, array element spacing d, and array direction vector expressed as
Wherein the first array element is selected as a reference point, c is the signal wavelength, and thetakFor the incoming wave direction, the antenna array outputs x (t) as x (t): as (t) + n (t), where x (t) ═ x1(t)，x2(t)，…，xb(t)]，s(t)＝[s1(t)，s2(t)，…，sL(t)]And A is an array direction matrix.
The step 3 specifically includes: the covariance matrix of the array output signal x (t) is R { (x (t) -m { (x (t)) { (t) } { (m {)x(t))(x(t)-mx(t))HIn which m isx(t) is the mean value of the output signal, and since the covariance matrix R is a square matrix, the eigenvalue decomposition can be directly carried out on the covariance matrix R
And then obtain the signal subspace Us＝[u1，u2，…，uL]Wherein u is1，u2，…，uLEigenvectors corresponding to L larger eigenvalues of the covariance matrix of the array signal, U being a unitary matrix, UHIs a transposed form thereof; lambda [ alpha ]iIs a characteristic value, uiIs a feature vector, ui HIs its transposed form and sets up a signal subspace fingerprint library.
The step 4 specifically includes: on-line measurement of received signal strength vector R' (x) at a mobile terminali，yj) And signal subspace U's(xi，yj) In the fingerprint library of the received signal strength and the fingerprint library of the signal subspace, respectively, use
And
the two formulas obtain corresponding fingerprint library matching coordinates, wherein D is the minimum Euclidean distance, and T is the minimum matrix two-norm.
The step 5 specifically includes: step (4) is executed on each area coordinate for a plurality of times, matching errors are calculated by using the matching coordinates, corresponding probability statistics is carried out, and the matching error statistical probabilities P (d) of the two fingerprint libraries are obtained respectivelyR|(xi，yj) And P (d)U|(xi，yj))。
The posterior probability in the step (6) is P ((x)i，yj)|dR，dU)∝P(dR|(xi，yj))·P(dU|(xi，yj))·P((xi，yj))。
The invention has the beneficial effects that:
1. the invention adopts a mixed positioning method of the received signal strength fingerprint database and the signal subspace fingerprint database, fully utilizes the effective positioning information of the mobile terminal, and eliminates various errors generated by a single fingerprint database.
2. The invention adopts the information fusion technology based on the Bayesian theory, and effectively improves the accuracy of the judgment of the final positioning result.
3. The invention establishes a signal subspace fingerprint library, and compared with a received signal strength fingerprint library, the signal subspace stores the main information of the signal, and can more effectively resist the influence of channel time variation caused by multipath propagation and environmental change.
4. The array antenna is adopted to receive the signals and carry out array signal processing, so that the required useful signals can be enhanced, useless interference and noise can be inhibited, and useful signal characteristics and information contained in the signals can be conveniently extracted.
Drawings
Fig. 1 is a scene schematic of the hybrid indoor positioning of the present invention.
FIG. 2 is a flow chart of the method of the present invention.
Fig. 3 is a schematic diagram of a uniform linear array.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
As shown in FIG. 1, the present invention provides an indoor floor plan and signal information sampling points when a user walks, including received signal strength and signal subspace information, wherein AP1，AP2，……，APLFor a router access point, i.e. a transmitting base station, (x)i，yj) I is 1, …, m is 1, y is 1, …, n is the reference point coordinate when the information is collected, namely the position of the information collected by the mobile terminal, and the fingerprint database of the received signal strength and the fingerprint database of the signal subspace are established according to the position fingerprint information collected at the reference point;
as shown in fig. 2, the method of the present invention comprises the following main steps:
step (1): in the off-line phase, the received signal strength is measured according to the respective area coordinates (x)i，yj) I 1, …, m, y 1, …, n, and the corresponding received signal strength R is measuredk(xi，yj) I is 1, …, m, j is 1, …, n, k is 1, …, L, and establishes a fingerprint library of received signal strength;
step (2): the array signal x (t) is measured off-line by using an array antenna of the mobile terminal, and an antenna array output vector x (t) can be represented as x (t) as (t) + n (t), where x (t) x ═ x (t)1(t)，x2(t)，…，xb(t)，]A is array direction matrix, s (t) is signal source, n (t) is noise;
and (3): obtaining a signal subspace U from x (t) by an array signal processing methods＝[u1，u2，…，uL]Wherein u is1，u2，…，uLThe eigenvectors corresponding to the L larger eigenvalues of the covariance matrix of the array signal are used to measure the signal subspace set { U ] of all the area coordinatess(xi，yj) 1, …, m, j 1, …, n, and establishes a signal nullA fingerprint library;
and (4): on-line measurement of received signal strength vector R' (x) at a mobile terminali，yj) And signal subspace U's(xi，yj) Then, calculating in a fingerprint database according to a correlation algorithm to obtain matching coordinates;
and (5): performing fingerprint library matching training on each region coordinate to respectively obtain the error statistical probability P (d) of two fingerprint librariesR|(xi，yj) And P (d)U|(xi，yj))；
And (6): according to the error statistical probability of the two fingerprint libraries, information fusion is carried out by using Bayesian theory to obtain posterior probability P ((x) on each area coordinatei，yj)|dR，dU)；
And (7): obtaining accurate positioning coordinates of the mobile terminal by utilizing the maximum posterior probability criterion
The step (1) further comprises: measuring the received signal strength off-line according to the region coordinate { (x)i，yj) I 1, …, m, y 1, …, n, and measuring the corresponding received signal strength { R { (R) }k(xi，yj) I 1, …, m, j 1, …, n, k 1, …, L, and creates a fingerprint library of the received signal strengths as shown in the following table:
the step (2) comprises the following steps: an even linear array that constitutes by b antennas, array element interval are d, use incoming wave direction theta to incide b antennas, then the array direction vector of even linear array is:
wherein, the first array element is selected as a datum point, and c is the signal wavelength.
Fig. 3 is a schematic diagram of a uniform linear array, and an array direction matrix is defined as:
then the large line array output metrics x (t) can be expressed by the following equation
x(t)＝As(t)+n(t)
Wherein x (t) ═ x1(t)，x2(t)，…，xb(t)]，s(t)＝[s1(t)，s2(t)，…，sL(t)]N (t) is noise;
the step (3) comprises the following steps: the covariance matrix of the array output signals x (t) is
R＝E{(x(t)-mx(t))(x(t)-mx(t))H}
Wherein m isx(t)＝E[x(t)]Is the average value of the output signals, and mx(t) is 0. Then there is
R＝E{x(t)x(t)H}＝E{(Ax(t)+n(t))(Ax(t)+n(t))H}
The dry covariance matrix R is a square matrix, and then the eigenvalue of the dry covariance matrix R can be directly decomposed to obtain:
the obtained characteristic values lambda are sequenced to obtain lambda1≥…≥λL＞λL+1＝…＝λb＝σ2Where σ is2Representing noise power, i.e. the first K values being related to the signal and having a value greater than sigma2Finding the signal subspace Us＝[u1，…，uK]Usually, L ═ K is taken, where u1，u2，…，uLEigenvectors corresponding to L larger eigenvalues of the covariance matrix of the array signal, U being a unitary matrix, UHIs a transposed form thereof; lambda [ alpha ]iIs a characteristic value, uiIs a feature vector, ui HIs its transposed form and establishes a signal subspace fingerprint library, U is a unitary matrix, UHIs a transposed form thereof; lambda [ alpha ]iIs a characteristic value, uiIs a feature vector, ui HIs its transposed form and establishes a signal subspace fingerprint library, as shown in the following table:
the step (4) comprises the following steps: measuring received signal strength vector R ' and signal subspace U ' at mobile terminal on-line 'sCalculating R' and each R (x) in the fingerprint database of the received signal strength according to the KNN algorithmi，yj) Euclidean distance of (a):
D(xi，yj)＝||R′-R(xi，yj)||2
and selecting K coordinates corresponding to the received signal strength vector with the minimum Euclidean distance, and calculating an average value to obtain the matching coordinates of the received signal strength fingerprint database. And calculating U 'according to a minimum matrix two-norm criterion'sWith each U in the signal subspace fingerprint librarys(xi，yj) The poor matrix two norm:
the calculation makes the matrix two-norm T (x)i，yj) And taking the coordinate at the minimum value, namely the matching coordinate of the signal subspace fingerprint library.
The step (5) comprises the following steps: step (4) is executed for a plurality of times on each area coordinate, matching errors are calculated by using the matching coordinates, corresponding probability statistics is carried out, and the statistical probability of the matching errors of the two fingerprint libraries is P (d)R|(xi，yj) And P (d)U|(xi，yj) I.e. the probability distribution of the matching error under the condition of known coordinates.
The step (6) comprises the following steps: calculating the posterior probability on each area coordinate by using a Bayesian information fusion technology according to the reported statistical probability of the matching errors from the two fingerprint libraries:
the step (7) comprises the following steps: according to the criterion of the maximum a posteriori probability,
obtaining final positioning coordinates of mobile terminal
Claims (5)
1. A mixed indoor positioning method based on a signal subspace fingerprint library is characterized by comprising the following steps:
step (1): in the off-line phase, the received signal strength is measured according to the respective area coordinates (x)i,yj) I 1, m, j 1, n, the corresponding received signal strength R is measuredk(xi,yj) I is 1, …, m, j is 1, …, n, k is 1, …, L, and establishes a fingerprint library of received signal strength;
step (2): the array signal x (t) is measured off-line by using an array antenna of the mobile terminal, and an antenna array output vector x (t) can be represented as x (t) as (t) + n (t), where x (t) x ═ x (t)1(t),x2(t),…,xb(t)]Representing signals on b receiving antennas, wherein A is an array direction matrix, s (t) is a signal source, and n (t) is noise;
and (3): obtaining a signal subspace U by solving a covariance matrix and an eigenvalue decomposition method from x (t)s＝[u1,u2,…,uL]Wherein u is1,u2,…,uLThe eigenvectors corresponding to the L larger eigenvalues of the covariance matrix of the array signal are used to measure the signal subspace set { U ] of all the area coordinatess(xi,yj) 1, …, m, j 1, …, n }, and establishing a signal subspace fingerprint library;
and (4): on-line measurement of reception at a mobile terminalSignal strength vector R' (x)i,yj) And signal subspace U's(xi,yj)，
Calculating R' and each R (x) in the fingerprint database of the received signal strength according to the KNN algorithmi,yj) Euclidean distance of (a):
D(xi,yj)＝||R'-R(xi,yj)||2
selecting K coordinates corresponding to the received signal strength vector with the minimum Euclidean distance, calculating an average value to obtain matching coordinates of a received signal strength fingerprint database, and calculating U 'according to a minimum matrix two-norm criterion'sWith each U in the signal subspace fingerprint librarys(xi,yj) The poor matrix two norm:
the calculation makes the matrix two-norm T (x)i,yj) Taking the coordinate at the minimum value, namely the matching coordinate of the signal subspace fingerprint library;
and (5): performing fingerprint library matching training on each region coordinate to respectively obtain the error statistical probability P (d) of two fingerprint librariesR|(xi,yj) And P (d)U|(xi,yj))；
And (6): according to the error statistical probability of the two fingerprint libraries, information fusion is carried out by using Bayesian theory to obtain posterior probability P ((x) on each area coordinatei,yj)|dR,dU)；
And (7): obtaining accurate positioning coordinates of the mobile terminal by utilizing the maximum posterior probability criterion
2. The method according to claim 1, wherein the method comprises the following steps: the method comprises the following steps of (2):defining uniform linear array composed of b antennas, array element spacing d, and array direction vector expressed as
Wherein the first array element is selected as a reference point, c is the signal wavelength, and thetakFor the incoming wave direction, the antenna array output x (t) is x (t) as (t) + n (t), where x (t) x1(t)，x2(t)，…，xb(t)]，s(t)＝[s1(t),s2(t),…,sL(t)]And A is an array direction matrix.
3. The method according to claim 1, wherein the method comprises the following steps: the method comprises the following steps of (3): the covariance matrix of the array output signal x (t) is R { (x (t) -m { (x (t)) { (t) } { (m {)x(t))(x(t)-mx(t))HIn which m isx(t) is the mean value of the output signal, and since the covariance matrix R is a square matrix, the eigenvalue decomposition can be directly carried out on the covariance matrix R
And then obtain the signal subspace Us＝[u1,u2，…，uL]Wherein u is1，u2，…，uLEigenvectors corresponding to L eigenvalues of covariance matrix of array signal, U being unitary matrixHIs a transposed form thereof; lambda [ alpha ]iIs a characteristic value, uiIs a feature vector, ui HIs its transposed form and sets up a signal subspace fingerprint library.
4. The method according to claim 1, wherein the method comprises the following steps: the method comprises the following steps of (5): step (4) is executed on each area coordinate for a plurality of times, matching errors are calculated by using the matching coordinates, corresponding probability statistics is carried out, and the matching error statistical probabilities P (d) of the two fingerprint libraries are obtained respectivelyR|(xi,yj) And P (d)U|(xi,yj))。
5. The method according to claim 1, wherein the method comprises the following steps: in the step (6), the posterior probability is P ((x)i,yj)|dR,dU)∝P(dR|(xi,yj))·P(dU|(xi,yj))·P((xi,yj))。
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| CN110703189A (en) * | 2019-09-30 | 2020-01-17 | 天津大学 | Passive positioning method based on wireless signal RSS subspace feature vector matching |
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