CN105611627B - The estimation method of WLAN access point AOA based on double antenna - Google Patents
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Abstract
A kind of estimation method of the WLAN access point AOA based on double antenna, using two common antenna WLAN access points, in receiving end using the CSI information received, Combined estimator is carried out to the flight time (Time of flight, TOF) of transmitting terminal to receiving end and angle of arrival.The present invention has fully considered multi-path environment influence caused by angle estimation of indoor complexity, estimates the mean error of angle within 5 degree, can satisfy the direct projection diameter angle estimation demand under indoor multipath signal.
Description
Technical Field
The invention relates to the technical field of signal processing and wireless positioning, in particular to a method for estimating an AOA (automatic optical access) of a WLAN (wireless local area network) access point based on double antennas, which is suitable for a positioning system in an indoor environment.
Background
In the field of mobile communications, the demand of Location Based Services (LBS) is increasing, and with the popularization of Wireless Local Area Networks (WLAN), an indoor Location system Based on WLAN is an emerging research hotspot meeting the demand.
Obtaining the AOA (angle of arrival) information of the incoming wave in the indoor environment can provide key positioning parameters for the indoor positioning system, thereby realizing high-precision positioning in the indoor environment. Meanwhile, compared with the current positioning technology based on RSSI, the positioning technology based on AOA does not need to construct a position fingerprint database, thereby saving a large amount of labor cost. Meanwhile, the positioning technology based on the AOA is a passive positioning technology, the network side positioning of the base station can be easily realized, and a user is not required to install any redundant software. Therefore, how to accurately estimate the arrival angle of the incoming wave direction is the key to solve the indoor positioning problem.
The traditional AOA estimation algorithm such as the MUSIC algorithm and the ESPRIT algorithm mainly adopt special multi-antenna array equipment, and the angle estimation is realized by utilizing the orthogonality of a signal subspace and a noise subspace. However, the traditional angle estimation algorithm also has obvious defects, and the AOA information can be accurately estimated only by adopting a special large-scale array antenna, which sets a barrier for indoor application and popularization of AOA estimation.
At present, the mobile communication system mostly adopts the smart antenna technology, so the serving base station can provide more accurate radio wave arrival angle information and provide location services based on network side positioning. For example, the current AOA positioning system based on LTE network utilizes the precoding mechanism of MIMO to achieve the acquisition of AOA. In indoor application, there is a conventional Ubicarse system, and in a WLAN environment, the design idea of sar (synthetic Aperture radar) is adopted, and the angle estimation is realized by rotating a receiving-end antenna to simulate a large antenna array. In addition, another existing system, the ArrayPhaser, realizes the design of a system with a multi-antenna array structure by cascading WLAN access points. In addition, the Direction Finding system utilizes WLAN dual antennas and adopts the principle of interferometer Direction Finding to realize the measurement of the arrival angle. Although the above systems can accurately perform arrival estimation, the above systems all have different degrees of defects. The implementation of the Ubicarse system requires improvement of the receiving device antenna; the ArrayPhaser system needs to make a large modification to the WLAN access point device, and meanwhile, synchronization errors existing in different devices must be fully considered in the cascade design, which also poses a large challenge to later-stage system maintenance. The directionpointing system performs angle estimation through two antennas, cannot effectively estimate multipath signals existing in the environment, and meanwhile, the estimated angle resolution is poor. These problems all bring limitations to the popularization of the AOA positioning system in the field of indoor positioning.
Disclosure of Invention
The invention aims to provide a method for estimating the WLAN access point AOA based on double antennas, which has the average error of the estimated angle of about 5 degrees and can greatly meet the requirement of angle estimation of indoor multipath signals.
The invention discloses a method for estimating a WLAN access point AOA based on double antennas, which comprises the following steps:
step 1, configuring a wireless local area network;
step 2, carrying out orthogonal modulation on the original data by adopting a plurality of subcarriers at a transmitting end;
step 3, estimating the channel state information of each subcarrier: receiving OFDM CSI channel information matrix CSI at a receiving endmatrix：
Wherein csii,jThe channel information value of the jth subcarrier on the ith antenna is obtained;
step 4, performing decorrelation processing on the received CSI channel matrix by adopting a spatial smoothing algorithm;
and step 5, carrying out joint estimation on the time of flight TOF (time of flight) of the signal reaching the array antenna and an arrival angle estimation method by using the decorrelated CSI (channel state information) information to obtain an estimation value of the direction of arrival.
In the step 3, estimating the channel state information of each subcarrier, and performing channel estimation by adopting a Least Square (LS) technology;
the least squares technique assumes that the K subcarriers are orthogonal, i.e., no ISI (Inter symbol interference), and the received training signal is Y K']K' 0,1,2, …, K-1 estimates the channel H asApplying a cost functionAnd (3) minimizing:
let the cost functionAboutIs equal to 0, the solution to obtain the LS channel estimate is:
order toTo representThe element in (b), K' is 0,1,2, …, K-1, and X is known as a diagonal matrix under the assumption of ISI-free, so the LS signal estimation on each subcarrier can be expressed as:
in step 5, when the decorrelated CSI information jointly estimates the time of flight TOF and the arrival angle estimation method of the signal arriving at the array antenna, a two-dimensional direction matrix containing AOA and TOF information is constructed:
whereinIs a vector of M x 1 directions, where M represents the number of virtual array antennas after decorrelation using a spatial smoothing algorithm, θkIs the angle of arrival, τ, of the kth pathkThe flight time of the kth path is adopted, and finally a system for carrying out joint estimation on TOF and AOA by utilizing OFDM multi-carrier information is established;
the AOA estimation method is to use MUSIC algorithm to complete the estimation of the arrival angle; the MUSIC algorithm is to obtain an estimated value of a covariance matrix according to N received signal vectors;
wherein, R is a covariance matrix, and then the characteristic value decomposition of R ═ U ∑ U is carried out on the covariance matrixHAccording to the magnitude sequence of the eigenvalues U, regarding the eigenvector corresponding to the maximum eigenvalue equal to the number K of the signals as a signal subspace, and regarding the remaining eigenvectors corresponding to the (M-K) eigenvalues as a noise subspace, thenThen traverse the angle by 360 degrees according toTo calculate a spectral function PMUSICWhereinTo obtain an estimate of the direction of arrival theta and time estimate tau by seeking a peak,representing a direction vectorThe conjugate of (2) transposes the vector.
The invention has the following advantages: based on the existing WiFi equipment to estimate the AOA, the invention utilizes the received Channel State Information (CSI) matrix of the OFDM orthogonal subcarrier to jointly estimate the Time of Flight (TOF) of the multipath signal to the array antenna and the AOA, compared with the traditional WLAN angle measurement technology, the invention does not need to change any transceiver equipment, can achieve the same angle measurement precision, has the average error of the estimated angle of about 5 degrees, can greatly meet the requirement of the angle estimation of the indoor multipath signal, and has higher popularization value.
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FIG. 1 is a schematic block diagram of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
As shown in fig. 1, the overall schematic block diagram of the system.
(1) Original data is generated on a WLAN transmitting network card and is transmitted through radio frequency after being modulated by an OFDM baseband.
(2) And receiving the radio frequency signal passing through the multipath channel on the WLAN receiving network card, and then carrying out down-conversion to obtain a baseband signal.
(3) After the baseband signal is acquired, the baseband signal is demodulated first to acquire CSI information of the received signal.
(4) And constructing an observation equation by using the obtained CSI information, and estimating the AOA information by using a TOF and AOA joint estimation algorithm.
The invention discloses a method for estimating a WLAN access point AOA based on double antennas, which comprises the following steps:
step 1, configuring a wireless local area network:
the antenna array comprises a central frequency point, a bandwidth, the number (2) of array antennas, an antenna spacing, the number of orthogonal modulation subcarriers, a subcarrier frequency spacing, a signal-to-noise ratio and the like.
And 2, carrying out orthogonal modulation on the original data by adopting a plurality of subcarriers.
Step 3, estimating the channel state information of each subcarrier:
assuming that all subcarriers are orthogonal, i.e. without ISI, the training symbols for K subcarriers can be represented in matrix form:
wherein, X [ k']Represents the pilot signal on the k' th sub-carrier, satisfiesVar{X[k']}＝σ2And K is 0,1,2, … K-1. Since all subcarriers are assumed to be orthogonal, X is a diagonal matrix. Given the channel gain of k carriers [ H [0 ]] H[1] … H[k'-1]]TThe received k' training signals [ Y [0 ]] Y[1] … Y[k'-1]]TCan be expressed as:
where H is a channel vector, H ═ H [0 [ ]],H[1],…,H[k'-1]]T(ii) a Z is a noise vector Z ═ Z [0 ═ Z],Z[1],…,Z[k'-1]]TY is the received training signal vector, Y ═ Y [0 [ ]] Y[1] … Y[k'-1]]TAnd satisfies E { Z [ k']}＝0，
In order to obtain channel estimatesThe LS channel estimation method requires minimizing the following cost function
Let the cost functionAboutIs equal to 0, i.e.:
can then obtainThereby obtaining a solution to the LS channel estimateComprises the following steps:
order toTo representThe element in (1), K ═ 0,1, … K-1. Since X is known as a diagonal matrix under the assumption of ISI-free, the LS signal estimate on the k' th subcarrier can be expressed as:
namely, receiving CSI (channel State information) channel information matrix CSI of OFDM at a receiving endmatrix：
Wherein csii,jIs the channel information value of the jth subcarrier on the ith antenna.
And 4, performing decorrelation processing on the received CSI channel matrix by adopting a spatial smoothing algorithm:
and dividing the equidistant linear array into a plurality of sub-arrays which are overlapped. If the array manifolds of the sub-arrays are the same (this assumption applies to equidistant linear arrays), the covariance matrices of the sub-arrays can be added and then averagely substituted for the covariance matrix R of the original arrays. And dividing the M-element equidistant linear array into L sub-arrays by sliding, wherein each sub-array has N units, and N is M-L + 1. The output of the ith forward sub-array is:
where s (t) is the complex amplitude of the received plane wave, nl(t) is a noise vector, AMIs a direction matrix of NxM dimensions, which is a guide vector a of N dimensionsM(θi)(i＝1,2,…,K)，
d is the spacing between the array antennas, λ is the wavelength of the received signal, θiIs the direction of the incoming wave of the ith source, so the covariance matrix of the ith forward sub-array is:
the forward spatial smoothing covariance matrix RfComprises the following steps:
on this basis, consider the reverse of the linear array (in the order of M, M-1, …,2, 1). The covariance matrix R of backward space smoothing can be obtained by the same methodbComprises the following steps:
wherein, the covariance matrix of the l-th backward sub-matrixIn fact RbIs RfThe relationship between the conjugate inverted sequence arrays is the common conjugate inverted sequence invariance. Thus, the forward-backward smoothed covariance matrix is:
the advantage of conjugate inversion invariance is that the number of sub-arrays can be increased, but the effective aperture of the array is reduced relative to the original array because the sub-arrays are smaller than the original array. Despite this aperture loss, it changes the limitations of the eigen decomposition type AOA algorithms based on antenna array covariance matrices.
And 5, jointly estimating a flight time and arrival angle estimation method of the signals arriving at the array antenna by using the decorrelated CSI information:
constructing a two-dimensional direction matrix containing AOA and TOF information when the decorrelated CSI information carries out joint estimation positioning on the time of flight TOF (time of flight) and the arrival angle estimation calculation method of the signals arriving at the array antenna:
whereinIs a vector of M x 1 directions, where M represents the number of virtual array antennas after decorrelation using a spatial smoothing algorithm, θkIs the angle of arrival, τ, of the kth pathkThe flight time of the kth path is obtained, and finally a system for performing joint estimation on TOF and AOA by utilizing OFDM multi-carrier information is established.
The AOA estimation method is to use MUSIC algorithm to complete the estimation of the arrival angle. Assuming that the distance between two adjacent antennas is d, after the signal passes through different propagation paths, a certain angle theta of a certain multipath signal is setkWhen incident to the receiving antenna array, the angle of incidence thetakThe sum theta is introduced to two adjacent antennaskCorrelated path difference dsin θkThe phase shift caused by the path difference is 2 π fdsin θkAnd c, the ratio of the total weight to the total weight of the product. If the first antenna is taken as a reference, the wave front signal reaching the antenna 1 from the v-th path is set as sk(t), the wave front signal of the kth path received by the ith antenna is:
si,k(t)＝aksk(t)exp(-jw0(i-1)dsinθk/c)；
wherein s isi,k(t) represents the wave front signal of the k path received by the i antenna, akRepresenting the amplitude attenuation of the k-th path, w0Representing the angular velocity of the carrier, d the antenna spacing, and c the speed of light. Considering the influence of noise of the ith antenna, assuming that there are N multipath signals in the environment, the wavefront signal received by the ith antenna is the sum of the multipath signals, that is:
assuming that the number of the array antennas is M, the noise of each antenna is the average valueIs 0 and variance is σ2The noise is not correlated, and simultaneously, the noise and the signal are not correlated, and the wave front signal received by each antenna is written into a vector form as follows:
X(t)＝AS(t)+N(t)；
wherein,for received data vectors of dimension M, S (t) [ S ]1(t),S2(t),...,SN(t)]TFor the N-dimensional signal vector, the signal vector is,is an M x N dimensional directional matrix.Is an M-dimensional direction vector, τk＝dsinθk/c，N(t)＝[n1(t),n2(t),...,nM(t)]TIs M-dimensional noise, where N is the number of multiple paths.
The MUSIC algorithm is to divide a special space into a mutually orthogonal signal subspace and a noise subspace by using the singular value decomposition of an autocorrelation matrix of a received signal X (t), and obtain a spectrum estimation result by using the orthogonal characteristic of the subspace. Let X (t) be an autocorrelation matrix Rxx(t)，
Rxx(t)＝E[X(t)XH(t)]＝Rs+Rnoise；
Wherein R isxx(t) M × M symmetric matrix with rank M, Rs＝E[S(t)SH(t)]Is an N x N matrix with rank N, RnoiseIs a matrix with rank M-N. By the pair Rxx(t) solving the eigenvalue and the eigenvector, sorting the eigenvalue according to the magnitude, and taking the eigenvector corresponding to the M-N minimum eigenvalues to form a noise subspace and marking as En＝{vN+1,vN+2,...,vMSince the noise subspace and the signal subspace are orthogonal, in the signal direction θkAbove, it is clear that:
in order to realize the incoming wave direction estimation of N multipath signals, the spectral peak search is carried out by using the formula through continuously changing the value of theta, and the obtained spectral peaks corresponding to N theta are the incoming wave directions of the multipath signals:
further, the step 3 estimates the channel state information of each subcarrier, and performs channel estimation by using a Least Square (LS) technique.
The least squares technique assumes that N-1 subcarriers are orthogonal, i.e., no ISI, and that the received training signal is Y k']K' 0,1,2, …, K-1 estimates the channel H asThe following cost function is minimized:
make the above cost function aboutIs equal to 0, the solution to obtain the LS channel estimate is:
order toTo representThe element in (1), K ═ 0,1,2, …, K-1. X is known as a diagonal matrix under the assumption of ISI-free, so the LS signal estimate on each subcarrier can be expressed as:
further, when the CSI information after decorrelation in step 5 performs joint estimation and positioning on the time of flight TOF and angle of arrival estimation method of signals arriving at the array antenna, a two-dimensional direction matrix containing AOA and TOF information is constructed:
whereinIs the direction vector of M1, θkIs the angle of arrival, τ, of the kth pathkThe flight time of the kth path is obtained, and finally a system for performing joint estimation on TOF and AOA by utilizing OFDM multi-carrier information is established.
The MUSIC algorithm obtains an estimated value of a covariance matrix according to signals received by the N receiving antennas,
wherein, R is a covariance matrix, and then the characteristic value decomposition of R ═ U ∑ U is carried out on the covariance matrixHAccording to the magnitude sequence of the eigenvalue U, regarding the eigenvector corresponding to the maximum eigenvalue equal to the number K of the signals as a signal subspace, and regarding the remaining (M-K) eigenvalue pairsShould the feature vector be viewed as a noise subspace, thenThen traverse the angle by 360 degrees according toTo calculate a spectral function PMUSICWhereinTo obtain an estimate of the direction of arrival theta and time estimate tau by seeking a peak,representing a direction vectorThe conjugate of (2) transposes the vector.
The invention fully considers the influence of indoor complex multipath environment on angle estimation, and by adopting the system and the method, the average error of the estimated angle is about 5 degrees, thus the requirement of angle estimation of indoor multipath signals can be greatly met.
Claims (2)
1. A method for estimating WLAN access point AOA based on dual antennas is characterized by comprising the following steps:
step 1, configuring a wireless local area network;
step 2, carrying out orthogonal modulation on the original data by adopting a plurality of subcarriers at a transmitting end;
step 3, estimating the channel state information of each subcarrier:
receiving OFDM CSI channel information matrix CSI at a receiving endmatrix：
Wherein csii,jThe channel information value of the jth subcarrier on the ith antenna is obtained;
step 4, performing decorrelation processing on the received CSI channel matrix by adopting a spatial smoothing algorithm;
step 5, performing joint estimation on the time of flight TOF (time of flight) of a signal to reach the array antenna and an arrival angle estimation method by using the decorrelated CSI (channel state information) to obtain an estimation value of an arrival direction;
in step 5, when the decorrelated CSI information jointly estimates the time of flight TOF and the arrival angle estimation method of the signal arriving at the array antenna, a two-dimensional direction matrix containing AOA and TOF information is constructed:
whereinIs a vector of M x 1 directions, where M represents the number of virtual array antennas after decorrelation using a spatial smoothing algorithm, θkIs the angle of arrival, τ, of the kth pathkThe flight time of the kth path is adopted, and finally a system for carrying out joint estimation on TOF and AOA by utilizing OFDM multi-carrier information is established;
the AOA estimation method is to use MUSIC algorithm to complete the estimation of the arrival angle; the MUSIC algorithm obtains an estimated value of a covariance matrix according to N received signal vectors x (N);
wherein, R is a covariance matrix, and then the characteristic value decomposition of R ═ U ∑ U is carried out on the covariance matrixHAccording to the magnitude sequence of the characteristic value U, the characteristic vector corresponding to the maximum characteristic value equal to the number K of the signals is seenMaking a signal subspace, and regarding the feature vectors corresponding to the remaining (M-K) feature values as a noise subspaceWherein U isSAnd UNRespectively representing the feature vectors of the signal and the noise,andconjugate transposes of eigenvectors, sigma, representing signal and noise, respectivelySSum-sigmaNRespectively representing the characteristic values of the signal and the noise, and traversing the angle for 360 degrees according toTo calculate a spectral function PMUSICWhereinTo obtain an estimate of the direction of arrival theta and time estimate tau by seeking a peak,representing a direction vectorThe conjugate of (2) transposes the vector.
2. The method for estimating AOA of a WLAN access point based on dual antennas according to claim 1, characterized in that: in the step 3, estimating the channel state information of each subcarrier, and performing channel estimation by adopting a least square technology;
the least squares technique assumes that the K sub-carriers are orthogonal, i.e., no ISI, and that the received training signal is Y K']K' 0,1,2, …, K-1 estimates the channel H asApplying a cost functionAnd (3) minimizing:
let the cost functionAboutIs equal to 0, the solution to obtain the LS channel estimate is:
order toTo representThe element in (b), K' is 0,1,2, …, K-1, and X is known as a diagonal matrix under the assumption of ISI-free, so the LS signal estimation on each subcarrier can be expressed as:
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| CN111405657B (en) * | 2020-04-02 | 2021-01-05 | 哈尔滨工程大学 | CSI-based single access point positioning method based on arrival angle and arrival time difference |
| TWI739479B (en) * | 2020-06-17 | 2021-09-11 | 廣達電腦股份有限公司 | Electronic device and positioning method for improving smoothness and precesion of positioning |
| CN111901745B (en) * | 2020-07-13 | 2021-10-08 | 华中科技大学 | WiFi indoor positioning method based on direction angle measurement |
| CN112235028B (en) * | 2020-09-14 | 2022-04-19 | 德清阿尔法创新研究院 | CSI automatic accurate calibration system for commercial WiFi signals |
| US11855810B2 (en) * | 2020-10-19 | 2023-12-26 | Texas Instruments Incorporated | Simultaneous CSI at single RX chain device |
| CN112881976B (en) * | 2021-01-06 | 2024-09-06 | 普联国际有限公司 | CSI-based single antenna positioning method, device, equipment and storage medium |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103391602A (en) * | 2012-05-12 | 2013-11-13 | 美国博通公司 | Wi-Fi proximity considerations within multiple-user, multiple-access, and/or MIMO wireless communication |
| CN104396151A (en) * | 2012-04-16 | 2015-03-04 | 三星电子株式会社 | Hierarchical channel sounding and channel state information feedback in massive MIMO systems |
| CN105188082A (en) * | 2015-08-05 | 2015-12-23 | 重庆邮电大学 | Evaluation method for RSS (Received Signal Strength)/AOA (Angle of Arrival)/TDOA (Time Difference of Arrival) positioning performance under indoor WLAN (Wireless Local Area Network) environment |
-
2016
- 2016-01-08 CN CN201610012316.1A patent/CN105611627B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104396151A (en) * | 2012-04-16 | 2015-03-04 | 三星电子株式会社 | Hierarchical channel sounding and channel state information feedback in massive MIMO systems |
| CN103391602A (en) * | 2012-05-12 | 2013-11-13 | 美国博通公司 | Wi-Fi proximity considerations within multiple-user, multiple-access, and/or MIMO wireless communication |
| CN105188082A (en) * | 2015-08-05 | 2015-12-23 | 重庆邮电大学 | Evaluation method for RSS (Received Signal Strength)/AOA (Angle of Arrival)/TDOA (Time Difference of Arrival) positioning performance under indoor WLAN (Wireless Local Area Network) environment |
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