CN109061616B - Moving target positioning method - Google Patents
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- CN109061616B CN109061616B CN201811016737.7A CN201811016737A CN109061616B CN 109061616 B CN109061616 B CN 109061616B CN 201811016737 A CN201811016737 A CN 201811016737A CN 109061616 B CN109061616 B CN 109061616B
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- G—PHYSICS
- G01—MEASURING; TESTING
- 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
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
- G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
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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
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Abstract
The invention provides a method for positioning a moving target, which is characterized in that an active moving label is arranged on a moving node, a reader arranged at a fixed node is used for collecting wireless signals sent by the moving label, a server and the readers perform information interaction and perform data preprocessing operation to form a wireless signal gradient model, and the method specifically comprises the following steps: the reader acquires a corresponding signal intensity value from the mobile tag through a radio frequency identification technology; fitting the signal intensity value by a nonlinear least square method to obtain a curve with the abnormal end value removed and optimized, and performing cross-correlation matching on the curve and the wireless signal gradient model to obtain a distance-signal intensity value relation; the server calculates the coordinate position by a weighted centroid positioning method, and accurately positions the coordinate position according to unscented Kalman filtering to obtain a required coordinate point; the method of the invention can stably and accurately estimate the position of the moving target.
Description
Technical Field
The invention relates to a radio frequency identification positioning technology, in particular to a method for accurately positioning a moving target by utilizing unscented Kalman filtering.
Background
The radio frequency identification positioning technology has important application in scenes such as logistics management, library book management and the like. With the development of active radio frequency identification technology and the increase of identification distance, the method is currently applied to positioning of middle and low speed moving targets such as electric bicycles, pedestrians and the like. Considering the characteristics of weak capability of carrying equipment by a moving target, flexible moving distance, higher speed compared with the traditional application scene and the like, the positioning technology is required to have higher equipment convenience and low equipment cost.
At present, the traditional mobile target positioning method mainly comprises two main types of indoor WIFI positioning and outdoor GPS positioning. The indoor WIFI positioning access threshold is low, but the equipment precision is low, the indoor WIFI positioning access threshold is not suitable for occasions where the target moves relatively fast, and the target position cannot be tracked in real time. The outdoor GPS positioning technology is widely applied, but has the problems of being easily influenced by a shelter, short charging period of equipment and the like.
In summary, how to accurately locate the position of the moving target based on the low cost is a problem to be solved urgently.
Disclosure of Invention
The invention mainly aims to provide a method for accurately positioning a medium-low speed moving target by using unscented Kalman filtering, which can accurately position the position of the moving target at relatively low cost, and the method is realized by the following technical scheme:
the method for positioning the mobile target comprises the steps that an active mobile tag is arranged on a mobile node, a reader arranged at a fixed node is used for collecting wireless signals sent by the mobile tag, information interaction is carried out between a server and the readers, the server carries out data preprocessing operation and carries out matching through a wireless signal gradient model to obtain the position relation of the mobile node, and the method specifically comprises the following steps:
the reader acquires a corresponding signal intensity value from the mobile tag through a radio frequency identification technology; fitting the signal intensity value by a nonlinear least square method to obtain a curve with the abnormal end value removed and optimized, and performing cross-correlation matching on the curve and the wireless signal gradient model to obtain a distance-signal intensity value relation;
and the server calculates the coordinate position by a weighted centroid positioning method, and then accurately positions the coordinate position according to unscented Kalman filtering to obtain the coordinate point of the moving target.
The method for locating the moving target is further designed in such a way that the data preprocessing comprises the following steps: the method comprises the following steps that a server collects wireless signal data, corrects a propagation model of a wireless signal, obtains a standard relation curve of signal intensity values and distances to form a wireless signal gradient model, and stores the wireless signal gradient model into the server;
the method for locating a moving object is further designed in that the data preprocessing comprises the following steps:
step 1) constructing a wireless signal gradient model according to the formula (1):
in the formula (1), p (d) represents a signal strength value received by the terminal when the linear distance from the base station is d, namely an RSSI value; p (d) 0 ) Denotes distance from base station as d 0 The signal power received by the terminal; d is a radical of 0 N is a path attenuation factor for the reference distance;
step 2) optimizing the model value by linear regression according to the formula (2):
ρ i ＝-10lgd i ,i＝1,2,...,m
in the formula (2), the reaction mixture is,
m represents the number of measurement points; d i Represents the distance, RSSI, at the ith measurement point i Representing the signal strength value at the ith measurement point; ρ is a unit of a gradient i Representing the ideal signal power value at the ith measurement point;
and 3) averaging the optimized model values and storing the averaged model values into a corresponding database.
The method for positioning the moving target is further designed in that the method for positioning the moving target fits the signal intensity value through a nonlinear least square method, and the curve obtained after removing the abnormal end value and optimizing is specifically as follows:
enabling the acquired signal intensity value to approach a fitting curve to a wireless signal propagation model through a nonlinear system, wherein the model of the nonlinear system is y = f (x, theta), and satisfies the following conditions: minS (x) = f T (x)f(x)＝||f(x)|| 2 Wherein f (x) = (f) 1 (x),f 2 (x),...,f m (x)) T And x = (x) 1 ,x 2 ,...,x n ) T X denotes the fitted data, S (x) denotes the minimum mean square error function, and θ denotes the deviation value.
The method for positioning the moving target is further designed in that the step of calculating the coordinate position according to the weighted centroid positioning method specifically comprises the following steps:
the reader positions are set as follows: b1 (X1, Y1), B2 (X2, Y2), \8230;, bn (Xn, yn), the mobile node is: m (Xi, yi), the distances from the mobile node to the fixed points are d1, d2, \ 8230, dn, then the coordinates of the mobile node are calculated according to the formula (3)
The method for positioning the moving target is further designed in such a way that unscented Kalman filtering obtains a nonlinear function value point set through selecting Sigma points and nonlinear function mapping, performs state prediction and state correction on the point set to obtain a state variable of the next stage, and realizes the correction of a true value by using a predicted value, wherein the method specifically comprises the following steps of:
setting a system state variable X = [ X, v = x ,y,v y ] T Wherein X represents the X axial position; v. of x Represents the x axial velocity; y represents the Y axial position; v. of y Indicating the y axial velocity.
Setting state transition matrix
T is the signal scan time and the angle of the mobile node from the origin is taken as the measurement.
The method for positioning the moving target is further designed in that a reader at the fixed point utilizes an antenna to emit electromagnetic waves outwards, a radio frequency circuit is arranged in an active tag at the mobile node, the radio frequency circuit activates the electromagnetic waves to be emitted to the reader, and the reader obtains a signal strength value.
The method for positioning the moving target is further designed in that the step of cross-correlation matching the curve and the wireless signal gradient model specifically comprises the following steps:
step A) the mean square error under the best approximation is calculated according to equation (4):
wherein x (f) is a value of a database standard curve at a moment; y (f) is the actual value obtained;
step B) normalizing the relative error according to the formula (5)
Step C) setting
Binding formula (5) yields:
where ρ is xy Is a correlation coefficient of x and y, and is known from the Schwarz inequality of the summation form, 0<ρ xy <1, if ρ xy =1, mean square error is 0, indicating that the two values are completely identical; if number ρ xy If the value is not less than 0, the two data are completely inconsistent;if the correlation coefficient p xy Shu is close to 1, which means that the smaller the error of the approximation, the more consistent the two data values are; when ρ xy When the threshold value is approached, the standard value is used to replace the actual value.
The invention has the advantages that:
the method for positioning the moving target comprises the steps of firstly preprocessing data, correcting a wireless signal propagation model by a large amount of data in advance, and obtaining a standard relation curve of signal strength values and distances; the reader at the fixed node obtains a corresponding signal strength value from the active tag at the mobile node by using radio frequency identification technology. Fitting the signal intensity value by utilizing a nonlinear least square method to obtain a curve with the abnormal terminal values removed and optimized, and performing cross-correlation matching on the curve and a server database standard curve to obtain a distance-signal intensity value relation; and calculating the coordinate position by using a weighted centroid positioning method, and accurately positioning the coordinate position according to unscented Kalman filtering to obtain the required coordinate point. The method of the invention can stably and accurately estimate the position of the moving target.
The method of the invention can be applied to: 1. a vehicle intelligent positioning system; the data acquisition equipment is installed at the crossroad, and the electronic active tag card is installed on the license plate, so that the motor vehicle/electric vehicle can be prevented from being stolen and robbed, and the police can quickly break a case and lock a criminal suspect; .2. An enterprise attendance system; the RFID chest card is worn by the staff, so that the situation that some staff only punch the card but do not work can be avoided, and the enterprise can conveniently manage the flow of staff in key production areas.
Drawings
FIG. 1 is a flow chart of a method for locating a moving object according to the present invention.
FIG. 2 is a diagram of a specific algorithm for positioning a moving object according to the present invention
Fig. 3 is a comparison graph of the differences between the distances of the mobile nodes of three-channel data before and after filtering.
Fig. 4 is a graph comparing the difference between the speed of a mobile node for three channel data before and after filtering.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The method for positioning the moving target in the invention, as shown in fig. 1, comprises the following steps:
step 101: data preprocessing, namely correcting a wireless signal propagation model by a large amount of data in advance to obtain a standard relation curve of signal intensity values and distances and storing the standard relation curve into a database;
specifically, the signal strength model conforms to a simplified wireless signal gradient model for the distance between the base station and the terminal, as follows:
wherein, p (d) represents the RSSI value which is the signal strength value received by the terminal when the distance from the base station is d; p (d) 0 ) Indicates a distance d from the base station 0 The signal power received by the terminal; d 0 N is a path attenuation factor for the reference distance;
step 2) optimizing the model value by linear regression according to the formula (2):
ρ i ＝-10lgd i ,i＝1,2,...,m
wherein the content of the first and second substances,
m represents the number of measurement points; d is a radical of i Represents the distance, RSSI, at the ith measurement point i Representing the signal strength value at the ith measurement point; ρ is a unit of a gradient i Representing the ideal signal power value at the ith measurement point; .
And averaging the optimized model values and storing the averaged model values into a corresponding database.
Step 102: the reader at the fixed node uses radio frequency identification technology to obtain a corresponding signal strength value from the active tag at the mobile node. Fitting the signal intensity value by using a nonlinear least square method to obtain a curve with the abnormal end value removed and optimized, and performing cross-correlation matching on the curve and a server database standard curve to obtain a distance-signal intensity value relation;
specifically, the reader at the fixed point utilizes the antenna to emit electromagnetic waves outwards, the radio frequency circuit in the active tag at the mobile node activates to emit electromagnetic waves to the reader, and the reader obtains the power value of the signal, namely the signal strength value. Enabling the acquired signal intensity value to approach a fitting curve to a wireless signal propagation model through a nonlinear system, enabling the acquired signal intensity value to approach the fitting curve to the wireless signal propagation model through the nonlinear system, wherein the model of the nonlinear system is y = f (x, theta), and the following requirements are met: minS (x) = f T (x)f(x)＝||f(x)|| 2 Wherein f (x) = (f) 1 (x),f 2 (x),...,f m (x)) T And x = (x) 1 ,x 2 ,...,x n ) T X denotes the fitted data, S (x) denotes the minimum mean square error function, and θ denotes the deviation value.
The fitted value and the actual value are calculated as follows:
the mean square error under the best approximation is:
wherein x is a value of the standard curve t of the database; and y is the actually obtained value.
Normalized to a relative error, then there is
Such as a ream
The above formula is rewritten as:
ρ xy the correlation coefficient between x and y is known from the Schwarz inequality of the summation form, 0<ρ xy <1. If the correlation coefficient p xy =1, mean square error is 0, which means that the two values are completely consistent; if the correlation coefficient p xy If the value is not less than 0, the two data are completely inconsistent; if the correlation coefficient p xy Shu is close to 1, indicating that the smaller the error of the approximation, the more the two data values agree. When rho xy When the RSSI value tends to be a threshold value, the actual value can be replaced by the standard value, and a new RSSI value-distance relationship is obtained.
Step 103: and calculating the coordinate position by using a weighted centroid positioning method, and accurately positioning the coordinate position according to unscented Kalman filtering to obtain the required coordinate point.
Specifically, the unscented kalman filter obtains a nonlinear function value point set by selecting a Sigma point and performing nonlinear function mapping, performs state prediction and state correction on the point set to obtain a state variable of the next stage, and realizes correction of a true value by using a predicted value, specifically:
setting a system state variable X = [ X, v = x ,y,v y ] T Wherein X represents the X axial position; v. of x Represents the x axial velocity; y represents the Y axial position; v. of y Representing the y-axis velocity.
Setting a state transition matrix
T is the signal scan time and the angle of the mobile node from the origin is taken as the measurement. After this step is completed, it indicates that the positioning of the moving object is completed.
Referring to fig. 2, the process of locating a moving target in the present embodiment is described in detail.
First, data preprocessing is performed. A large amount of environmental data (namely signal intensity values-distances) near the fixed antenna are collected firstly, then a plurality of groups of data are optimized by using a dynamic environment model, and the obtained values are averaged and stored in a database as an RSSI value-distance standard curve under the environment.
And secondly, fitting the signal intensity value by using a nonlinear least square method to obtain a curve with the abnormal end values removed and optimized, and performing cross-correlation matching on the curve and a server database standard curve to obtain a distance-signal intensity value relation. In this embodiment, the tag a is used as a mobile node, and the point a moves in the communication ranges of the reader 1, the reader 2, and the reader 3 to obtain corresponding R1, R2, and R3. And obtaining r1, r2 and r3 values, namely z values, after nonlinear least square fitting, and matching the z values with standard data of a database to obtain corresponding distances d1, d2 and d3.
And finally, calculating the coordinate position by using a weighted centroid positioning method, and accurately positioning the coordinate position according to unscented Kalman filtering after noise filtering to obtain the required coordinate point. In this embodiment, the weighted centroid is located at
Obtaining small noise after filtering noise by Kalman filtering
Unscented Kalman Filter ukf usage
Correction of
Then get
To position (X, Y). Fig. 3 shows the difference after position filtering and fig. 4 shows the difference of velocity, and it can be seen from fig. 4 that the effect of X-axis velocity after filtering is significantly improved compared with that of no filtering. The Y axial velocity value is obviously improved after being processed by the UKF, which shows that the UKF can better inhibit the interference noise in the external environment and approaches to a true value as much as possible. In conclusion, the method provided by the invention obviously improves the positioning precision by carrying out nonlinear two-times fitting to remove outliers and unscented Kalman filtering to suppress noise, and the effectiveness of the method is verified.
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 changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. A mobile target positioning method is characterized in that an active mobile tag is arranged on a mobile node, a wireless signal sent by the mobile tag is collected through a reader arranged at a fixed node, information interaction is carried out between a server and each reader, the server carries out data preprocessing operation and carries out matching through a wireless signal gradient model to obtain the position relation of the mobile node, and the method specifically comprises the following steps:
the reader acquires a corresponding signal intensity value from the mobile tag through a radio frequency identification technology; fitting the signal intensity value by a nonlinear least square method to obtain a curve with the abnormal terminal values removed and optimized, and performing cross-correlation matching on the curve and the wireless signal gradient model to obtain a distance-signal intensity value relation;
the server calculates the coordinate position by a weighted centroid positioning method, and then accurately positions the coordinate position according to unscented Kalman filtering to obtain a coordinate point of the moving target;
the reader at the fixed point utilizes the antenna to emit electromagnetic waves outwards, the active tag at the mobile node is provided with a radio frequency circuit, the radio frequency circuit activates the electromagnetic waves to be emitted to the reader, and the reader obtains a signal strength value;
the cross-correlation matching of the curve and the wireless signal gradient model specifically comprises the following steps:
step A) calculating the mean square error under the optimal approximation according to equation (4):
wherein x (f) is a value of a database standard curve at a moment; y (f) is the actual value obtained;
step B) normalizing the relative error according to the formula (5)
Step C) setting
Binding formula (5) yields:
where ρ is xy Is a correlation coefficient of x and y, and is known from the Schwarz inequality of the summation form, 0<ρ xy <1, if ρ xy =1, mean square error is 0, indicating that the two values are completely identical; if number ρ xy =0, it indicates that the two data are completely inconsistent at this time; if the correlation coefficient p xy The closer to 1, the smaller the error representing the approximation, the more consistent the two data values; when rho xy When the threshold value is approached, the standard value is used to replace the actual value.
2. The method of claim 1, wherein the data preprocessing comprises: the server collects wireless signal data, modifies a propagation model of a wireless signal, obtains a standard relation curve of signal intensity values and distances to form a wireless signal gradient model, and stores the wireless signal gradient model in the server.
3. The method of claim 2, wherein the data preprocessing comprises the steps of:
step 1) constructing a wireless signal gradient model according to the formula (1):
in the formula (1), p (d) represents a signal strength value received by the terminal when the linear distance from the base station is d, namely an RSSI value; p (d) 0 ) Indicates a distance d from the base station 0 The signal power received by the terminal; d 0 Is a reference distance; n is a path attenuation factor;
step 2) optimizing the model value by linear regression according to the formula (2):
ρ i ＝-10lgd i ,i＝1,2,...,m
in the formula (2), the reaction mixture is,
m represents the number of measurement points; d i Represents the distance, RSSI, at the ith measurement point i Representing the signal strength value at the ith measurement point; rho i Representing the ideal signal power value at the ith measurement point;
and 3) averaging the optimized model values and storing the averaged model values into a corresponding database.
4. The method for positioning a moving target according to claim 3, wherein the fitting of the signal intensity values by a nonlinear least square method to obtain the optimized curve without outliers is specifically:
enabling the acquired signal intensity value to approach a fitting curve to a signal wireless propagation model through a nonlinear system, wherein the model of the nonlinear systemType y = f (x, θ), satisfying: minS (x) = f T (x)f(x)＝||f(x)|| 2 Wherein f (x) = (f) 1 (x),f 2 (x),...,f m (x)) T And x = (x) 1 ,x 2 ,...,x n ) T X denotes the fitted data, S (x) denotes the minimum mean square error function, and θ denotes the deviation value.
5. The method according to claim 4, wherein the calculating the coordinate position according to the weighted centroid localization method specifically comprises:
the reader positions are set as follows: b1 (x) 1 ,y 1 ),B2(x 2 ,y 2 ),…,Bn(x n ,y n ) The mobile node is: m (X) i ,Y i ) The distances from the mobile node to the fixed points are respectively D 1 ,D 2 ,…,D n Then the mobile node coordinates are calculated according to equation (3),
6. the method according to claim 4, wherein unscented kalman filtering obtains a nonlinear function value point set by selecting a Sigma point and nonlinear function mapping, performs state prediction and state correction on the point set to obtain a state variable at the next stage, and corrects a true value by using a predicted value, specifically:
setting a system state variable X = [ X, v = x ,y,v y ] T Wherein X represents the X axial position; v. of x Represents the x axial velocity; y represents the Y axial position; v. of y Represents the y-axis velocity;
setting state transition matrix
T is the signal scan time and the angle of the mobile node from the origin is taken as the measurement.
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