CN106793087B - Array antenna indoor positioning method based on AOA and PDOA - Google Patents

Abstract

The invention relates to an array antenna indoor positioning algorithm based on AOA and PDOA, which comprises the following steps: distinguishing multipath signals by using an array antenna; selecting two signal paths with the strongest energy according to the energy of the received signals, and acquiring phase information of the two signal paths from an antenna; calculating the arrival angles of the two signals by using an AOA estimation method; calculating the propagation distance of the two signals by using a PDOA estimation method; according to the position of the total obstacle of the positioning scene, a virtual base station can be established, and a non-line-of-sight (NLOS) path is converted into a line-of-sight (LOS) path; calculating the position coordinates of the object to be positioned by using a weighted least square WLS algorithm; and obtaining more accurate position coordinates by using a residual weighted LS algorithm pair.

Description

An indoor positioning method of array antenna based on AOA and PDOA

technical field

The invention belongs to the technical field of indoor positioning algorithms, and is particularly aimed at multipath and non-line-of-sight indoor positioning environments.

Background technique

The research and application of positioning technology is very extensive, permeating all aspects of military, business, life, etc. Global Positioning System (GPS) provides a good global positioning service, but it cannot be used in indoor environment. Passive ultra-high frequency radio frequency identification (UHF RFID) indoor positioning technology has attracted more and more people's attention, because passive tags have the advantages of small size, simple deployment, no power supply, low price, long reading distance, and fast speed. Due to the advantages of reading and writing speed, passive UHF RFID positioning technology has been gradually applied to the field of situational awareness, such as logistics management, intelligent transportation, robots, automated office and so on.

The methods of indoor wireless positioning include time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), signal strength of arrival (RSS) and phase difference of arrival (PDOA). However, in the UHF FRID system with very narrow bandwidth, the TOA and TDOA methods are difficult to implement; the RSS-based method is very sensitive to multipath signal fading, so the positioning accuracy of the positioning system based on the pure RSS method is not very high. ; PDOA-based methods are also affected by multipath, making the ranging results inaccurate; meanwhile, AOA-based methods are affected by non-line-of-sight (NLOS) and multipath.

Common methods to reduce the influence of multipath and non-line-of-sight on indoor positioning accuracy are divided into statistical methods and geometric methods. These two types of methods are widely used in both front-end modeling and back-end positioning algorithms. For the method of suppressing errors in the modeling process, statistical modeling is generally to model non-line-of-sight errors, describe non-line-of-sight errors with certain statistical features, or model scatterers that cause multipath problems; Geometric modeling, such as ray-tracing, mainly uses the propagation characteristics of wireless signals to be similar to those of light to analyze the path of signal propagation in specific environments. For the method of suppressing errors in the positioning algorithm, the statistical method is to determine whether the signal path is line-of-sight or non-line-of-sight by measuring the error distribution of the received signal; and the geometric method is to determine whether the signal path is line-of-sight or non-line-of-sight through the geometric relationship. sight distance.

The biggest disadvantage of the statistical method is that it needs to know the statistical characteristics of the signal in a certain environment, which requires a large number of actual measurements to obtain such statistical characteristics. When the environment changes, the statistical characteristics will change, and a large number of measurements will be required to correct the parameters. The geometric method does not require a large number of measurements, but only needs to know the indoor layout. However, the existing methods all use the direct path or the primary scattering path for positioning. For the multiple scattering problem, it is assumed that it does not exist, or it is filtered out by algorithmic judgment.

SUMMARY OF THE INVENTION

The invention provides an indoor positioning algorithm that can well improve the influence of multipath and non-line-of-sight on indoor positioning, and effectively improve positioning accuracy. The technical scheme is as follows:

An indoor positioning algorithm for array antennas based on AOA and PDOA, comprising the following steps:

1) Using an array antenna to distinguish multipath signals;

2) Select two signal paths with the strongest energy according to the energy of the received signal, and obtain the phase information of the two signal paths from the antenna;

3) Use the AOA estimation method to calculate the arrival angles of the two signals;

4) Use the PDOA estimation method to calculate the propagation distance of the two signals;

5) According to the position of the total obstacle in the positioning scene, a virtual base station can be established, and the non-line-of-sight NLOS path can be converted into a line-of-sight LOS path;

6) According to the angle information obtained in step 3) and the distance information obtained in step 4), in conjunction with the virtual base station established in step 5), the weighted least squares WLS algorithm is used to calculate the position coordinates (x', y of the object to be located) '):

Z ₁ =[x',y',x' ² +y' ² ] ^T =(G ^T WG) ^-1 G ^T WH

diag{·}表示对角矩阵，

in

diag{·} represents a diagonal matrix,

Z=[x,y,x ² +y ² ] ^T , X _k =[x _k ,y _k ] ^T ,k=1,2,...,N, is the virtual corresponding to the kth signal path Base station, d _k is the path length of the k-th signal path estimated by PDOA, and d _k ≤d _k+1 , θ _k is the arrival angle of the k-th signal path;

7) Use the residual weighted LS algorithm to optimize the position obtained in step 6) to obtain more accurate position coordinates (x, y).

The invention uses the antenna array to distinguish multipath signals, and uses the virtual base station to convert the non-line-of-sight path into the line-of-sight path. The two signal paths with the strongest energy are selected, and the AOA and PDOA estimation algorithms are used to calculate the position of the object to be located. The influence of multipath and non-line-of-sight on indoor positioning can be well improved, and the positioning accuracy can be effectively improved.

Description of drawings

FIG. 1 is a block diagram of the overall flow of the present invention.

FIG. 2 is a schematic diagram of a positioning scene of the present invention.

FIG. 3 is a schematic diagram of converting a non-line-of-sight path into a line-of-sight path using a virtual base station.

Fig. 4 is a simulation diagram of the positioning accuracy using the algorithm of the present invention. The simulated environment is a 10m*10m room. Under the Gaussian white noise environment, using the algorithm of the present invention, the positioning accuracy is less than 0.5 with a probability of 80%. m.

Detailed ways

The algorithm of the present invention is described in detail below using a uniform linear array.

1) Using an array antenna to distinguish multipath signals.

2) According to the energy of the received signal, two signal paths with the strongest energy are selected, and the phase information of the two signal paths is obtained from the antenna.

3) Using the AOA estimation method to calculate the angle of arrival of the two signals, and according to the phase information obtained in step 2), the classical MUSIC algorithm based on spatial smoothing in the AOA estimation method can be used to calculate the angle of arrival of the signals.

4) Use the PDOA estimation method to calculate the propagation distance of the two signals. The formula of the PDOA method is as follows:

Among them, c is the propagation speed of light in the air, Δφ represents the phase difference generated by the signal propagating from the array antenna to the object to be positioned, π=3.14, Δf is the frequency difference between two signals of different frequencies, d is the calculated the propagation distance of the signal.

5) According to the position of the total obstacle in the positioning scene, a virtual base station can be established to convert a non-line-of-sight (NLOS) path into a line-of-sight (LOS) path. As shown in FIG. 3 , X _b is the position of the array antenna, X _t is the position of the object to be positioned, and X _v is the virtual base station established according to the position established by the obstacle l. Looking from the virtual base station to the object to be located, we get a line-of-sight path.

6) According to the angle information obtained in step 3), the distance information obtained in step 4), in conjunction with the virtual base station established in step 5), the weighted least squares (WLS) algorithm is used to calculate the position of the object to be located:

The distance d and angle θ of N signal paths are estimated by the entity base station, X _k =[x _k ,y _k ] ^T ,k=1,2,...,N is corresponding to the kth signal path , d _k is the path length of the k-th signal path estimated by PDOA, and d _k ≤d _k+1 , θ _k is the arrival angle of the k-th signal path. Use X=[x,y] ^T to represent the position of the object to be positioned, then:

x sin(θ _i )-y cos(θ _i )=x _i sin(θ _i )+y _i cos(θ _i )

Write the above two formulas in matrix form, you can get GZ=H, where:

Z=[x, y, x ² +y ² ] ^T .

Use the weighted least squares algorithm to calculate the position of the object to be positioned, and obtain the initial position coordinates (x', y') of the object to be positioned:

Z ₁ = [x', y', x ^'2 +y' ² ] ^T = (G ^T WG) ^-1 G ^T WH,

diag{·}表示对角矩阵，

in

diag{·} represents a diagonal matrix,

k＝1,2,...,N，利用残差加权(LS)算法对步骤6)得到的位置进行优化，得到更为准确的待定位物体的位置X＝[x,y]^T,其中：7) set

k=1,2,...,N, use the residual weighting (LS) algorithm to optimize the position obtained in step 6) to obtain a more accurate position of the object to be positioned X=[x,y] ^T , where :

X′ _i =[x′ _i ,y′ _i ] ^T i =1,2,...,N is the position of the object to be located derived from the i-th signal path.

Claims (1)

1. A method for indoor positioning of array antennas based on AOA and PDOA, comprising the steps: 1) Using an array antenna to distinguish multipath signals; 2) Select two signal paths with the strongest energy according to the energy of the received signal, and obtain the phase information of the two signal paths from the antenna; 3) Use the AOA estimation method to calculate the arrival angles of the two signals; 4) Use the PDOA estimation method to calculate the propagation distance of the two signals; 5) According to the position of the total obstacle in the positioning scene, a virtual base station can be established, and the non-line-of-sight NLOS path can be converted into a line-of-sight LOS path; 6) According to the angle information obtained in step 3) and the distance information obtained in step 4), in conjunction with the virtual base station established in step 5), the weighted least squares WLS algorithm is used to calculate the position coordinates (x', y of the object to be located) '): Z ₁ =[x',y',x' ² +y ^'2 ] ^T =(G ^T WG) ^-1 G ^T WH in

diag{·} represents a diagonal matrix,

Z=[x,y,x ² +y ² ] ^T , X _k =[x _k ,y _k ] ^T ,k=1,2,...,N, is the virtual corresponding to the kth signal path Base station, d _k is the path length of the k-th signal path estimated by PDOA, and d _k ≤d _k+1 , θ _k is the arrival angle of the k-th signal path; 7) set

Use the residual weighting (LS) algorithm to optimize the position obtained in step 6) to obtain a more accurate position of the object to be positioned X=[x,y] ^T , where:

X′ _i =[x′ _i ,y′ _i ] ^T i =1,2,...,N is the position of the object to be located derived from the i-th signal path.

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