CN115914997A - Mobile individual UWB positioning non-visual field correction system for large data service of farm - Google Patents

Mobile individual UWB positioning non-visual field correction system for large data service of farm Download PDF

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CN115914997A
CN115914997A CN202211392989.6A CN202211392989A CN115914997A CN 115914997 A CN115914997 A CN 115914997A CN 202211392989 A CN202211392989 A CN 202211392989A CN 115914997 A CN115914997 A CN 115914997A
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individual
mobile individual
time
uwb
base station
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赵亦欣
殷乐
黄伟
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Southwest University
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Southwest University
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The invention discloses a mobile individual UWB positioning non-vision field correction system for large data service of a farm, which comprises a positioning unit and a monitoring unit, wherein the positioning unit is used for positioning a mobile individual UWB positioning non-vision field; the positioning unit is used for ranging the mobile individual and determining the position of the mobile individual; the positioning unit comprises a base station for ranging the mobile individual and a moving element for measuring the motion data of the mobile individual; the monitoring unit is used for displaying the position information of the mobile individual and drawing the movement track of the mobile individual. The invention can provide accurate position estimation and greatly reduce the positioning error caused by NLOS.

Description

Mobile individual UWB positioning non-visual field correction system for large data service of farm
Technical Field
The invention relates to the field of indoor positioning, in particular to a mobile individual UWB (ultra-wideband) positioning non-visual field correction system for large data service of a farm.
Background
The positioning of mobile individuals in a farm is affected by various possible emergencies such as unfixed article placement in a pig farm, random movement of personnel and the like, and random shielding exists between a base station and a mobile node, so that communication is in a non-Line-of-sight (NLOS) state, and a positioning system generates serious positioning deviation. The existing positioning fusion method has the problems that the non-line-of-sight error correction effect which possibly occurs in a complex environment is limited, and the positioning accuracy is continuously reduced along with the increase of the environmental complexity.
Therefore, there is a need for a mobile individual UWB location non-visual field correction system for farm big data services that can solve the above problems.
Disclosure of Invention
In view of this, an object of the present invention is to overcome the defects in the prior art, and provide a mobile individual UWB positioning non-visual field correction system for a large data service in a farm, which can provide accurate position estimation and greatly reduce positioning errors caused by NLOS.
The invention discloses a mobile individual UWB (ultra-wideband) positioning non-visual field correction system for large data service of a farm, which comprises a positioning unit and a monitoring unit;
the positioning unit is used for ranging the mobile individual and determining the position of the mobile individual; the positioning unit comprises a base station for ranging the mobile individual and a moving element for measuring the motion data of the mobile individual;
the monitoring unit is used for displaying the position information of the mobile individual and drawing the movement track of the mobile individual.
Further, the positioning unit determines the position of the mobile individual according to the following method:
s1, initialization
Figure BDA0003932069920000021
And epsilon' 0 And setting time k =1; wherein it is present>
Figure BDA0003932069920000022
Is position and velocity at time 0' 0 Distance difference at time 0;
s2, measuring the inertial acceleration u of the mobile individual at the moment k-1 k-1 ；
S3, utilizing the inertial acceleration u k-1 And moveThe position of the individual at the time of k-1 is obtained, and the position and speed precalculated value of the moving individual at the time of k is obtained
Figure BDA0003932069920000023
S4, according to the precalculated value
Figure BDA0003932069920000024
Predicting the distance between the moving individual and the ith base station at the time k
Figure BDA0003932069920000025
S5, calculating the distance
Figure BDA0003932069920000026
And distance>
Figure BDA0003932069920000027
Difference value epsilon between k (ii) a Wherein the distance pick>
Figure BDA0003932069920000028
Measuring a distance for the UWB between the mobile individual and the ith base station at time k;
s6, according to the difference epsilon k And epsilon' k-1 Determining a correction factor N k ；
S7, according to the correction factor N k Calculating a correction value d for correcting the measured distance of UWB i ；
S8, according to the correction value d i Calculating the target coordinate z k ；
S9, according to the target coordinate z k Updating the estimated value of the position and the speed of the mobile individual at the time k
Figure BDA0003932069920000029
S10, according to the estimated value
Figure BDA00039320699200000210
Calculating a distance difference value epsilon 'at the moment k' k ；
S11, performing iterative operation according to the following steps: so that k is added by 1 and k after 1 addition is substituted into step S2, S2-S10 are executed.
Further, the position and speed precalculated value of the moving individual at the time k is determined according to the following formula
Figure BDA00039320699200000211
Figure BDA00039320699200000212
Wherein A is k-1 And B k-1 All are Kalman filtering recursion equation coefficients at the moment of k-1;
Figure BDA00039320699200000213
is an estimate of the position and velocity at time k-1.
Further, the distance between the moving individual and the ith base station at the time k is predicted according to the following formula
Figure BDA0003932069920000031
Figure BDA0003932069920000032
Wherein the content of the first and second substances,
Figure BDA0003932069920000033
is->
Figure BDA0003932069920000034
A position in the x-direction; />
Figure BDA0003932069920000035
Is->
Figure BDA0003932069920000036
Position in the y-direction; x is the number of i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction.
Further, the correction factor N is determined according to the following formula k ：
Figure BDA0003932069920000037
Wherein is epsilon' k-1 Is the distance difference at time k-1.
Further, a correction value d for correcting the UWB measurement distance is calculated according to the following formula i ：
Figure BDA0003932069920000038
Further, the target coordinate z is calculated according to the following formula k ：
Figure BDA0003932069920000039
Wherein the content of the first and second substances,
Figure BDA00039320699200000310
/>
Figure BDA00039320699200000311
n is the number of base stations.
Further, the position and velocity estimates of the mobile individual at time k are updated according to the following formula
Figure BDA00039320699200000312
Figure BDA00039320699200000313
Wherein, K k A Kalman filtering recursion parameter at the moment k; c k Is the state at time kSpatial motion coefficients.
Further, the distance difference value epsilon 'of the k moment is determined according to the following formula' k ：
Figure BDA0003932069920000041
Wherein the content of the first and second substances,
Figure BDA0003932069920000042
Figure BDA0003932069920000043
is->
Figure BDA0003932069920000044
A position in the x-direction; />
Figure BDA0003932069920000045
Is->
Figure BDA0003932069920000046
Position in the y direction; x is the number of i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction.
Further, the base station includes a UWB communication module; the moving member comprises a UWB communication module and an IMU inertia measurement module.
The invention has the beneficial effects that: the invention discloses a mobile individual UWB (ultra wide band) positioning non-visual field correction system for large data service of a farm, which selects a UWB positioning technology with obvious positioning advantages and an IMU (inertial measurement Unit) positioning technology for research on the basis of the existing positioning technology from practical application, combines the respective advantages of the two positioning technologies, corrects UWB ranging errors by using the strong external interference resistance of the IMU in a non-visual field state, corrects the positioning accumulated errors of the IMU by using the high-precision position settlement result of the UWB in the non-visual field state, continuously improves the positioning precision of the combined positioning technology facing to the complex environment in the farm, and greatly reduces the positioning errors caused by NLOS (non line of sight).
Drawings
The invention is further described below with reference to the following figures and examples:
FIG. 1 is a schematic diagram of the overall design of the calibration system of the present invention.
Detailed Description
The invention is further described with reference to the accompanying drawings, in which:
the invention discloses a mobile individual UWB positioning non-vision field correction system for large data service of a farm, which comprises a positioning unit and a monitoring unit;
the positioning unit is used for ranging the mobile individual and determining the position of the mobile individual; the positioning unit comprises a base station for measuring the distance of the moving individual and a moving element for measuring the movement data of the moving individual;
the monitoring unit is used for displaying the position information of the mobile individual and drawing the movement track of the mobile individual.
Wherein the base station comprises a UWB communication module; the base station is divided into a main base station and a plurality of secondary base stations; the moving member comprises a UWB communication module and an IMU inertia measurement module. The moving part is a moving label; the mobile tag is a tag object arranged on a mobile individual. The UWB communication module and the IMU inertial measurement module both use the prior art, and are not described herein again.
The positioning unit mainly has the functions that each base station and the mobile tag can measure distance in real time through the UWB communication module, the mobile tag can obtain real-time motion data of a detected mobile individual through the IMU inertia measurement unit, the collected data are collected to the main base station through the UWB communication module, the main base station carries out position calculation through the collected data, and position information is uploaded to the monitoring unit. The monitoring unit is an upper computer; the upper computer adopts the existing monitoring host; the main function of the upper computer is to send configuration information (position information of each fixed base station) to the main base station, receive the position information sent by the main base station, display the position information in real time, keep historical position information and draw the moving track of the moving individual.
The invention adopts a novel and simple non-line-of-sight error compensation mode, and considers the correction factor ultra-wideband distance measurement of the influence of the non-line-of-sight condition on the system error. By detecting the difference between the ultra-wideband distance measurement and the predicted distance, the intensity of the NLOS condition can be quantitatively determined and its impact reduced using short term accurate IMU measurements.
In this embodiment, the positioning unit determines the position of the mobile individual according to the following method:
s1, initialization
Figure BDA0003932069920000051
And epsilon' 0 And setting time k =1; wherein it is present>
Figure BDA0003932069920000052
Is the position and speed of time 0 or initial time epsilon' 0 The distance difference is 0 or the initial time; is/are>
Figure BDA0003932069920000053
Is a state vector;
s2, measuring the inertial acceleration u of the mobile individual at the moment k-1 k-1 ；
S3, utilizing the inertial acceleration u k-1 And the position of the moving individual at the time k-1 to obtain the position and speed pre-calculated value of the moving individual at the time k
Figure BDA0003932069920000061
That is, the position and speed at the time k are estimated through the data information at the time k-1;
s4, according to the precalculated value
Figure BDA0003932069920000062
Predicting the distance between the moving individual and the ith base station at the time k
Figure BDA0003932069920000063
S5, calculating the distance
Figure BDA0003932069920000064
And the distance->
Figure BDA0003932069920000065
The difference between k (ii) a Wherein distance +>
Figure BDA0003932069920000066
Measuring a distance for the UWB between the mobile individual and the ith base station at time k; />
Figure BDA0003932069920000067
S6, according to the difference epsilon k And epsilon' k-1 Determining a correction factor N k ；
S7, according to the correction factor N k Calculating a correction value d for correcting the measured distance of UWB i ；
S8, according to the correction value d i Calculating the target coordinate z k ；
S9, according to the target coordinate z k Updating the estimated position and speed value of the moving individual at the time k
Figure BDA0003932069920000068
S10, according to the estimated value
Figure BDA0003932069920000069
Calculating a distance difference value epsilon 'at the moment k' k ；
S11, performing iterative operation according to the following steps: so that k is added by 1 and k after 1 addition is substituted into step S2, S2-S10 are executed.
Through the operation, the position and the speed of the mobile individual at one moment can be obtained once in each iteration or loop; starting from the starting time 0, sequentially having the positions corresponding to the time 1, the time 2, the time 8230, and the time n, and finally obtaining the moving track of the moving individual in a certain time period by sequentially connecting the subsequent times from the starting time 0 to the position corresponding to the last time n.
In the present embodiment, by introducing a state space in the stochastic estimation theory, a state model is used to describe the relationship between the state and the measured value, and the state is estimated based on the measured value by prediction and update. The kalman filter does not need to store all historical data. According to the state estimation of the previous moment and the current measurement information, a new state estimation is calculated by adopting a recursion method, the storage and calculation capacity requirements of a computer are reduced, the real-time processing capacity is improved, and further, a discrete-time Kalman filtering method is provided, wherein the essence of Kalman filtering is the minimum mean square estimation of equation output.
Determining the position and speed precalculated value of the moving individual at the time k according to the following formula
Figure BDA0003932069920000071
Figure BDA0003932069920000072
Wherein, A k-1 And B k-1 All are Kalman filtering recursion equation coefficients at the time of k-1;
Figure BDA0003932069920000073
is an estimate of the position and velocity at time k-1. Determining coefficient A by constructing a second-order kinematics model to simulate the motion of a moving individual k-1 And B k-1 (ii) a The second-order kinematic model can be a uniform acceleration linear motion model; further, a Kalman filter is adopted to fuse the distance information measured by UWB and the inertia information measured by IMU, so as to obtain the optimal precalculated value of the position information of the mobile individual. Kalman filtering is considered the best linear estimator commonly used in data fusion, and can also yield the following recursive equation:
Figure BDA0003932069920000074
Figure BDA0003932069920000075
P k|k ＝(I-K k C k )P k | k-1 。
in this embodiment, the distance between the mobile unit and the ith base station at time k is predicted according to the following formula
Figure BDA0003932069920000076
Figure BDA0003932069920000077
Wherein the content of the first and second substances,
Figure BDA0003932069920000078
is->
Figure BDA0003932069920000079
A position in the x-direction; />
Figure BDA00039320699200000710
Is->
Figure BDA00039320699200000711
Position in the y-direction; x is the number of i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction. That is, the distance between the mobile unit and the i-th base station is obtained by calculating the coordinate position formed by x and y. Likewise, is selected>
Figure BDA00039320699200000712
Is a state vector that includes position information and velocity information.
In this embodiment, most algorithms determine the distance difference between the measured value and the predicted value by establishing an empirical threshold, and when the distance difference is greater than the empirical threshold, it is determined that the UWB base station has a non-line-of-sight effect and the measured value needs to be error-corrected, and when the distance difference is smaller than the empirical value, it is determined that the base station is in a line-of-sight condition. However, the establishment of empirical thresholds is a complex problem. When the UWB is under the influence of the non-line-of-sight, the error caused by the non-line-of-sight is related to factors such as the material of a shielding object, the distance between the shielding object and a base station when shielding occurs and the like, the shielding error is continuously increased or reduced along with the change of the shielding condition, and the shielding error is not a sudden change value. And the error is divided into two conditions of shielding and non-shielding by using an empirical value, which is not in accordance with the objective rule of the object.
Further introducing a correction factor N k The current state of the UWB is quantitatively determined in more detail. For analyzing all errors generated by the UWB, the error correction is needed for the whole positioning process. By adopting the mode of the correction factor, the shielded state of the UWB base station can be further quantized, the UWB state is not simply divided into two states by adopting a threshold value mode, and the quantization factor can be better applied to error compensation and is more effective in improving the precision.
The correction factor N is determined according to the following formula k ：
Figure BDA0003932069920000081
Wherein is epsilon' k-1 Is the distance difference at the time of k-1;
where k =1, k-1=0, then ε' k-1 Is an initialization value ε' 0 (ii) a Will initialize value ε' 0 And the difference epsilon at the moment k =1 1 Substituting the correction factor expression to obtain the correction factor N at the moment k =1 1 (ii) a And in the subsequent step S10, the distance difference epsilon 'at the time k is determined by the following formula' k ：
Figure BDA0003932069920000082
Wherein the content of the first and second substances,
Figure BDA0003932069920000083
Figure BDA0003932069920000084
is->
Figure BDA0003932069920000085
A position in the x-direction; />
Figure BDA0003932069920000086
Is->
Figure BDA0003932069920000087
Position in the y-direction; x is a radical of a fluorine atom i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction. Likewise, the->
Figure BDA0003932069920000088
Is a state vector and is not described herein.
Namely:
at k =2, k-1=1, then ε' 1 Can be represented by formula
Figure BDA0003932069920000089
Is determined by mixing epsilon' 1 And the difference epsilon at the time k =2 2 Substituting the correction factor expression to obtain the correction factor N at the moment k =2 2 (ii) a Similarly, the principle of calculating the correction factor after k =2 is the same, and is not described herein again.
In this embodiment, a correction factor N is utilized k A correction value d for correcting the measured distance of UWB is calculated according to the following formula i ：
Figure BDA0003932069920000091
And calculates the target coordinate z according to the following formula k ：
Figure BDA0003932069920000092
Wherein the content of the first and second substances,
Figure BDA0003932069920000093
/>
Figure BDA0003932069920000094
n is the number of base stations; (x) 1 ,y 1 ) Is the position coordinate of the first base station, (x) 2 ,y 2 ) Is the position coordinate of the second base station, (x) i ,y i ) Position coordinates of the ith base station, and so on, (x) n ,y n ) Position coordinates of the nth base station; in the same way, according to the formula
Figure BDA0003932069920000095
Can obtain d in turn 1 、d 2 、…、d i 、…、d n ；
In this embodiment, the estimated values of the position and velocity of the mobile individual at the time k are updated according to the following formula
Figure BDA0003932069920000096
Figure BDA0003932069920000097
Wherein, K k A Kalman filtering recursion parameter at the moment k; c k Is the state space motion coefficient at time k. As described above, the parameter K is determined by simulating the motion of a moving individual using a second-order kinematics model and performing data fusion using Kalman filtering k And coefficient C k (ii) a The second-order kinematics model and the kalman filter both adopt the prior art, and are not described herein again.
Finally, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (10)

1. A mobile individual UWB positioning non-visual field correction system for farm big data service is characterized in that: comprises a positioning unit and a monitoring unit;
the positioning unit is used for ranging the mobile individual and determining the position of the mobile individual; the positioning unit comprises a base station for ranging the mobile individual and a moving element for measuring the motion data of the mobile individual;
the monitoring unit is used for displaying the position information of the mobile individual and drawing the movement track of the mobile individual.
2. The farm big data served mobile individual UWB localization non-vision field correction system of claim 1, characterized in that: the positioning unit determines the position of the mobile individual according to the following method:
s1, initialization
Figure FDA0003932069910000011
And epsilon' 0 And setting time k =1; wherein it is present>
Figure FDA0003932069910000012
Is position and velocity at time 0' 0 Distance difference at time 0;
s2, measuring the inertial acceleration u of the mobile individual at the moment k-1 k-1 ；
S3, utilizing inertial acceleration u k-1 And the position of the moving individual at the time k-1 to obtain the position and speed pre-calculated value of the moving individual at the time k
Figure FDA0003932069910000013
S4, according to the precalculated value
Figure FDA0003932069910000014
Predicting the distance ^ between the moving individual and the i-th base station at time k>
Figure FDA0003932069910000015
S5, calculating the distance
Figure FDA0003932069910000016
And the distance->
Figure FDA0003932069910000017
Difference value epsilon between k (ii) a Wherein the distance pick>
Figure FDA0003932069910000018
Measuring a distance for the UWB between the mobile individual and the ith base station at time k;
s6, according to the difference epsilon k And epsilon' k-1 Determining a correction factor N k ；
S7, according to the correction factor N k Calculating a correction value d for correcting the measured distance of UWB i ；
S8, according to the correction value d i Calculating the target coordinate z k ；
S9, according to the target coordinate z k Updating the estimated value of the position and the speed of the mobile individual at the time k
Figure FDA0003932069910000019
S10, according to the estimated value
Figure FDA0003932069910000021
Calculating the distance difference epsilon at the moment k k ′；
S11, performing iterative operation according to the following steps: so that k is added by 1 and k after 1 addition is substituted into step S2, S2-S10 are executed.
3. The farm big data served mobile individual UWB location non-vision correction system of claim 2, characterized in that: determining the position and speed precalculated value of the moving individual at the time k according to the following formula
Figure FDA0003932069910000022
Figure FDA0003932069910000023
Wherein, A k-1 And B k-1 All are Kalman filtering recursion equation coefficients at the moment of k-1;
Figure FDA0003932069910000024
is an estimate of the position and velocity at time k-1.
4. The farm big data served mobile individual UWB localization non-vision correction system of claim 2, characterized in that: predicting the distance between the moving individual and the ith base station at the time k according to the following formula
Figure FDA0003932069910000025
Figure FDA0003932069910000026
Wherein the content of the first and second substances,
Figure FDA0003932069910000027
is->
Figure FDA0003932069910000028
A position in the x-direction; />
Figure FDA0003932069910000029
Is->
Figure FDA00039320699100000210
Position in the y-direction; x is the number of i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction.
5. The farm big data served mobile individual UWB location non-vision correction system of claim 2, characterized in that: the correction factor N is determined according to the following formula k ：
Figure FDA00039320699100000211
Wherein is epsilon' k-1 Is the distance difference at time k-1.
6. The farm big data served mobile individual UWB localization non-vision correction system of claim 2, characterized in that: calculating a correction value d for correcting a measured distance of UWB according to the following formula i ：
Figure FDA0003932069910000031
7. The farm big data served mobile individual UWB location non-vision correction system of claim 2, characterized in that: calculating the target coordinate z according to the following formula k ：
Figure FDA0003932069910000032
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003932069910000033
Figure FDA0003932069910000034
n is the number of base stations.
8. The farm big data served mobile individual UWB location non-vision correction system of claim 2, characterized in that: updating the position and velocity estimates of the moving individual at time k according to the following formula
Figure FDA0003932069910000035
Figure FDA0003932069910000036
Wherein, K k A Kalman filtering recursion parameter at the moment k; c k Is the state space motion coefficient at time k.
9. The farm big data served mobile individual UWB location non-vision correction system of claim 2, characterized in that: determining a distance difference value epsilon 'of k moment according to the following formula' k ：
Figure FDA0003932069910000037
Wherein the content of the first and second substances,
Figure FDA0003932069910000038
/>
Figure FDA0003932069910000041
is->
Figure FDA0003932069910000042
A position in the x-direction; />
Figure FDA0003932069910000043
Is->
Figure FDA0003932069910000044
Position in the y-direction; x is the number of i The position of the ith base station in the x direction; y is i Is the position of the ith base station in the y direction.
10. The farm big data served mobile individual UWB localization non-vision field correction system of claim 1, characterized in that: the base station comprises a UWB communication module; the moving member comprises a UWB communication module and an IMU inertia measurement module.
CN202211392989.6A 2022-11-08 2022-11-08 Mobile individual UWB positioning non-visual field correction system for large data service of farm Pending CN115914997A (en)

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