CN112180323A - Research on indoor joint positioning algorithm of TOA and AOA based on Wi-Fi - Google Patents

Abstract

The invention proposes an indoor ToA (Time of Arrival) and AoA (Angle of Arrival) joint positioning algorithm suitable for NLOS (Non Line of Sight, NLOS) environment, which can effectively utilize ToA and AoA to locate the target. First, the NLOS error in ToA is more accurately identified by Kalman filtering using the measured value dropout method, and then an improved threshold comparative weighted (TCW) algorithm is constructed according to the measured ToA and AoA. The initial position of the node is calculated, and the experimental results show that the positioning accuracy reaches 90% and the error is within 1.5m.

Description

Research on indoor joint positioning algorithm of TOA and AOA based on Wi-Fi

technical field

The invention belongs to the indoor positioning technology, and aims at the problem of low positioning accuracy caused by the non-line of sight (Non Line of Sight, NLOS) propagation of wireless signals in complex indoor environments, and proposes a ToA (Time of Arrival) suitable for Wi-Fi and a AoA (Angle of Arrival) indoor joint positioning algorithm model.

Background technique

With the development and popularization of wireless communication technology and network technology, people use a large amount of location information in life, for example, to provide precise synchronization clock source for telecommunication base stations, TV transmission stations, etc.; widely used in air transportation, electronic maps, It can realize positioning systems such as agricultural machinery navigation, automatic driving, and high-precision land leveling navigation; GPS equipment is widely used for engineering measurement in the construction of roads, bridges and tunnels; and applications in field exploration and urban planning. Outdoor positioning systems basically meet some of people's positioning needs, but people also have great needs for indoor location information. For example, in factories, construction sites and other places, people, equipment, and materials can be located in real time; in shopping malls, store location navigation ; In prisons, real-time positioning of prisoners; in security, indoor positioning technology is mainly used in the tracking of suspicious objects. However, due to the complex indoor environment and severe signal attenuation, it is difficult for the outdoor positioning system to solve some indoor positioning problems. Therefore, indoor positioning technology has become a research hotspot.

At present, indoor positioning technologies include ultra-wideband positioning technology, fingerprint positioning, radio frequency identification (Radio Frequency Identification, RFID) positioning, Bluetooth positioning, and Wi-Fi positioning. However, ultra-wideband positioning equipment is expensive. Based on fingerprint positioning, a lot of measurement work needs to be done in the early stage. Radio frequency identification has no communication ability, poor anti-interference ability, and the stability of Bluetooth system is slightly poor. relatively expensive. Wi-Fi is available in most indoor environments, and the hardware cost is very low, so Wi-Fi-based positioning technology can be widely used.

Common positioning parameters for Wi-Fi mainly include: Received Signal Strength (RSS), Angle of Arrival (AoA), and Time of Arrival (ToA). Positioning based on signal strength calculates the signal strength between the transmitter and receiver through the path propagation model, then converts the signal strength into distance, and finally calculates the target point position by trilateration. The interference of multipath effect makes the RSS fluctuate greatly. The time-of-arrival-based positioning estimates the flight time of the receiver and transmitter signals, and then uses the trilateration algorithm for positioning. The positioning accuracy is limited by the time estimation accuracy. The positioning based on the angle of arrival needs to estimate the angle of arrival of the signal to the receiver, and then use the trilateration method to locate the target. The classical method for solving the angle of arrival is the MUSIC algorithm, which requires that the number of physical antennas in the system must be greater than the number of multipath signals.

This paper improves a Threshold comparative weighted (TCW)-Taylor series expansion joint localization algorithm mentioned in "Research on Indoor Joint Localization Algorithm Based on ToA and AoA". First, the NLOS error in the ToA is more accurately identified by Kalman filtering using the measured value discarding method. Then, according to the measured ToA and AoA, the proposed improved TCW method is used to calculate the initial position of the target node. The simulation results show that the proposed improved threshold The positioning accuracy of the weighted method (improved TCW) algorithm is improved, and the initial positioning result reaches 90% error within 1.5m.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide an indoor ToA and AoA joint positioning algorithm suitable for NLOS environment, which can effectively use the ToA and AoA information to locate the target.

The method for constructing an indoor positioning model according to the present invention includes the following steps:

Step 1. Build an exponential model of NLOS error;

Step 2: Eliminate the NLOS error in the ToA by using the Kalman Filter (KF) of the measured value discarding method. The basic equation of KF is a recursive form, and the previous state is used to continuously predict and correct;

Step 3: Build an improved threshold comparison weighting algorithm model.

beneficial effect

The object of the present invention is to provide a kind of indoor ToA and AoA joint positioning algorithm suitable for NLOS environment, it can effectively use ToA and AoA information to locate the target, and has the following advantages:

1. Kalman filter is used to more accurately estimate the NLOS error in ToA;

2. The measurement error of AoA is allowed to be relatively large;

3. The improved threshold weighting method (improved TCW) algorithm is proposed to improve the positioning accuracy, and the initial positioning result achieves 90% error within 1.5m.

Description of drawings

FIG. 1 is a flow chart of the present invention.

Figure 2 shows the difference between the estimated NLOS error and the true NLOS error.

Figure 3 Schematic diagram of the improved TCW method.

Figure 4. Probability cumulative distribution diagram of the error of the improved TCW and the error of the TCW method.

specific implementation

Below in conjunction with accompanying drawing, the present invention is described in further detail:

Fig. 1 is the flow chart of the present invention, and the steps are as follows:

Step 1. Build the NLOS error model. The NLOS error comes from the complex indoor environment and obstacles. When the signal encounters obstacles, it will reflect, so that the signal reaching the receiver is synthesized by multiple error signals, which makes the ToA and AoA measurements. There is a big error. In order to better study the NLOS error, a positioning technology research group of Ericsson proposed the T1PI model, which is a channel model commonly used to simulate and evaluate time-based positioning technology:

Among them, D(τ) is the non-line-of-sight transmission delay, and τ _rms is the delay speed, which is a variable obeying a log-normal distribution and can be defined as:

τ _rms = T ₁ d ^ε ξ (2)

where T ₁ is the median value of τ _rms when d=1km, d is the distance from the receiver to the transmitter, in kilometers, ε is an exponential factor, ranging from 0.5 to 1.0, ξ is a logarithmic random variable, and lgξ is The mean value is 0, and the standard deviation is 2-6dB. Table 1 below shows the specific model parameters in different environments.

Table 1 Model parameters for different channel environments

surroundings T₁/us ε ξ/dB downtown 1.0 0.5 4 general urban area 0.4 0.5 4 suburbs 0.3 0.5 4 outskirts 0.1 0.5 4

Step 2: Eliminate the NLOS error in the ToA by Kalman filtering using the measurement value discarding method. Kalman filtering is to perform an optimal estimation of the system state, so that the optimal estimated value of the system has the smallest mean square error. The main steps are divided into two steps. Construct the prediction equation and construct the correction equation, and use two equations to represent the transition process of the unknown state and the relationship between the input and output of the measurement system, so as to connect the state value at a certain moment with the current and previous measurement values. The state equation and The measurement equations are shown in equations (3) and (4):

X(k+1)=AX(k)+w(k) (3)

Z(k)=HX(k)+v(k) (4)

H＝[10α]，v(k)＝n_m(k)，式子中τ为ToA值，b(k)和n_m(k)分别为 NLOS误差和测量误差，Δ为采样间距，Z(k)是测量值，α是试验值。卡尔曼滤波的过程如式(5)-(9)：In order to alleviate and eliminate the NLOS error, ToA and its first derivative and NLOS error are taken as the state vector to be estimated, and the relationship between the state vector and the measurement vector is expressed, where

H=[10α], v(k)=nm ( _k ), where τ is the ToA value, b(k) and nm ( _k ) are the NLOS error and measurement error, respectively, Δ is the sampling interval, Z ( k) is the measured value and α is the experimental value. The process of Kalman filtering is shown in equations (5)-(9):

P _k ^- =AP _k-1 A ^T +Q (6)

P _k = (IK _k C)P _K ^- (9)

和

分别代表着第k个时间状态变量的预测值和估计值，P_k ^-和P_k分别代表着第k个时刻预测和估计均方误差阵，Q和R分别是过程噪声协方差和测量噪声向量协方差，K_k为第k时刻卡尔曼滤波的增益。in,

and

represent the predicted value and estimated value of the state variable at the kth time, respectively, P _k ^- and P _k represent the predicted and estimated mean square error matrix of the k time time, respectively, Q and R are the process noise covariance and measurement noise vector, respectively Covariance, K _k is the gain of the Kalman filter at time k.

当

大于10ns或者小于0，或者当

大于1.2，

的值用

来代替。如图2所示，当采用测量值丢弃法卡尔曼滤波估计TOA 中的NLOS误差比普通卡尔曼滤波估计NLOS误差更好。In practice, the solution of the Kalman filter is calculated recursively, and each update estimate of its state is calculated from the previous estimate and the new input data. When the previous ToA is seriously affected by the NLOS error, the subsequent The measurement value of , produces a very large error. Another problem is that the delay caused by NLOS can only be greater than zero. In response to this problem, improvements are made in (8) to (9), and the judgment

when

greater than 10ns or less than 0, or when

greater than 1.2,

the value of

to replace. As shown in Fig. 2, the NLOS error in the TOA estimated by the Kalman filter is better than the NLOS error estimated by the ordinary Kalman filter when the measured value discarding method is adopted.

Step 3: Adopt the proposed improved threshold comparison weighting method, as shown in Figure 3, and the specific method is as follows:

a) First find out the point D _i (x _i , y _i ) where the three circles intersect, i=1, 2, 3, 4, 5, 6, and find the common chord segment L1 _i = k1 _i where every two circles intersect x+b1 _i , i=1,2,3;

b) Make three straight lines of AoA, take the center of the circle as a point, measure the value of AoA as the slope, the error of the slope obeys the mean value of 0, the variance is 1°, L2 _i = k2 _i x+b2 _i , i=1,2,3;

c) Find the intersection point of L1 _i and L2 _i , and filter out P _i (x _i , y _i ), i=1,...,N in the common area where the three circles intersect;

d) Calculate the distance _dmn between any two intersection points _Pm and _Pn ;

e) take the average value of all distances _dmn as the threshold _Dthr ;

f) Then the initial weight I _k of all possible intersection points P _i is 0, that is, I _k =0; compare the sizes of d _mn and D _thr , if d _mn <D _thr , then I _m =I _m +1,I _n =In +1,1≤m, _n≤N ;

g) The formula for calculating the position of the initial point (x _t , y _t ),

Claims (4)

1. the purpose of this invention is to provide a kind of indoor ToA and AoA joint positioning algorithm that is applicable to NLOS environment, it can effectively utilize ToA and AoA to locate the target, it is characterized in that: a) Build an exponential model of NLOS error; b) Eliminate the NLOS error in the ToA by Kalman filtering of the measured value discarding method; c) Use the method of improved threshold comparison and weighting to perform initial positioning. 2. The NLOS error model is constructed according to claim 1. The NLOS error originates from the complex indoor environment and obstacles. When the signal encounters obstacles, it will reflect, resulting in the signal reaching the receiver being synthesized by multiple error signals, so that ToA and The AoA measurement value has a large error. In order to better study the NLOS error, a positioning technology research group of Ericsson proposed the T1PI model, which is a channel model commonly used to simulate and evaluate time-based positioning technology:

Among them, D(τ) is the non-line-of-sight transmission delay, and τ _rms is the delay speed, which is a variable obeying a log-normal distribution and can be defined as: τ _rms = T ₁ d ^ε ξ (2) where T ₁ is the median value of τ _rms when d=1km, d is the distance from the receiver to the transmitter, in kilometers; ε is the exponential factor, ranging from 0.5 to 1.0, ξ is a logarithmic random variable, lgξ The mean value is 0, and the standard deviation is 2-6dB. Table 1 below shows the specific model parameters in different environments. Table 1 Model parameters for different channel environments surroundings T₁/us ε ξ downtown 1.0 0.5 4 general urban area 0.4 0.5 4 suburbs 0.3 0.5 4 outskirts 0.1 0.5 4 3. according to claim 1, utilize the measurement value discarding method Kalman filter to eliminate the NLOS error in the ToA, the Kalman filter is to carry out the optimal estimation to the system state, make the optimal estimation value of the system have the minimum mean square error, and the main steps are divided into: In two steps, the prediction equation and the correction equation are constructed, and the two equations are used to represent the transition process of the unknown state and the relationship between the input and output of the measurement system, so as to connect the state value at a certain moment with the current and previous measurement values. The state equation and measurement equation are shown in equations (3) and (4): X(k+1)=AX(k)+w(k) (3) Z(k)=HX(k)+v(k) (4) In order to alleviate and eliminate the NLOS error, ToA and its first derivative and NLOS error are taken as the state vector to be estimated, and the relationship between the state vector and the measurement vector is expressed, where

H=[1 0 α], v(k)=nm ( _k ). In the formula, τ is the ToA value, b(k) and nm( _k ) are the NLOS error and measurement error, respectively, Δ is the sampling interval, Z(k) is the measurement value, and α is the experimental value. The process of Kalman filtering is as follows: Formulas (5)-(9)

P _k ^- =AP _k-1 A ^T +Q (6)

P _k = (IK _k C)P _K ^- (9) in,

and

represent the predicted value and estimated value of the state variable at the kth time, respectively, P _k ^- and P _k represent the predicted and estimated mean square error matrix of the k time time, respectively, Q and R are the process noise covariance and measurement noise vector, respectively Covariance, K _k is the gain of the Kalman filter at the kth time; In practice, the solution of the Kalman filter is calculated recursively, and each update estimate of its state is calculated from the previous estimate and the new input data. When the previous ToA is seriously affected by the NLOS error, the subsequent The measurement value of , produces a very large error. Another problem is that the delay generated by NLOS can only be greater than zero. For this problem, improve it in (8)~(9), and judge

when

greater than 10ns or less than 0, or when

greater than 1.2,

the value of

Instead, as shown in Fig. 2, when the measured value discarding method is adopted, the Kalman filter estimates the NLOS error in ToA better than the ordinary Kalman filter for estimating the NLOS error. 4. according to claim 1, adopt the proposed improved threshold comparison weighting method, as shown in Figure 3, concrete method is as follows: a) First find out the point D _i (x _i , y _i ) where the three circles intersect, i=1, 2, 3, 4, 5, 6, and find the common chord segment L1 _i = k1 _i where every two circles intersect x+b1 _i , i=1,2,3; b) Make three straight lines of AoA, take the center of the circle as a point, measure the value of AoA as the slope, the error of the slope obeys the mean value of 0, the variance is 1°, L2 _i = k2 _i x+b2 _i , i=1,2,3; c) Find the intersection point of L1 _i and L2 _i , and filter out P _i (x _i , y _i ), i=1,...,N in the common area where the three circles intersect; d) Calculate the distance _dmn between any two intersection points _Pm and _Pn . Take the average value of all distances _dmn as the threshold _Dthr ; e) Set the initial weight I _k of all possible intersection points P _i to 0, that is, I _k =0; compare the sizes of d _mn and D _thr , if d _mn <D _thr , then I _m =I _m +1,I _n =In +1,1≤m, _n≤N ; f) The position calculation formula of the initial point (x _t , y _t ),

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