CN112880699B - Vehicle cooperative positioning method based on brain selective attention mechanism - Google Patents
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
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Abstract
The invention discloses a vehicle cooperative localization method based on a brain selective attention mechanism, which specifically aims at the influence of a geometric structure, RB position precision and a relative motion state formed between a cooperative reference node and a vehicle to be localized on cooperative localization; adopting a selective attention mechanism of human brain to information processing, taking the three influencing factors as characteristic points of information selection, and carrying out selective filtering processing on information; and then, comprehensively screening out the optimal RB through feature integration so as to further improve the reliability and precision of the cooperative positioning of the vehicle.
Description
Technical Field
The invention relates to the technical field of vehicle cooperative positioning, in particular to a vehicle cooperative positioning method based on a brain selective attention mechanism.
Background
Real-time accurate positioning of a moving vehicle is one of essential key technologies for implementation of an Intelligent Transportation System (ITS) and an Automatic Driving Technology (ADT), such as: vehicle information management, traffic control and vehicle information service in the ITS, automatic obstacle avoidance, lane change and other technologies in the ADT all need the self-positioning capability of the vehicle with high reliability and high precision; therefore, the research on a reliable vehicle positioning method has important significance for the realization of an intelligent traffic system;
at present, the mainly applied ground carrier Positioning technology comprises inertial navigation Positioning, visual navigation Positioning and Global Positioning System (GPS) technology, and the inertial navigation Positioning technology is usually found in special ground carriers for military use and the like due to high cost, so that the application of the technology in civil vehicles is restricted by high cost; although the visual navigation positioning technology is widely researched and applied, it can only provide relative position information; the GPS is the most widely used vehicle positioning technology at present due to higher cost performance and maturity; the GPS has positioning accuracy which is different from meter level to centimeter level according to different implementation technologies, for example, the positioning accuracy of the carrier phase differential GPS can reach centimeter level; however, the experiment proves that: the accuracy and reliability of GPS positioning are severely affected by satellite signal blocking and multipath interference phenomena in a densely populated urban or tunnel environment, even with carrier phase differential GPS, and thus are difficult to adapt to the needs of urban ITS.
With the high-speed development of wireless communication technology, a vehicular ad hoc Network (VANET) is responsible for the interaction task of various traffic information and becomes an important component of ITS; meanwhile, a V2V, V2I and V2X communication mode in the VANET also brings a new solution to vehicle navigation positioning and positioning enhancement, namely a vehicle cooperative positioning technology, which is characterized in that a vehicle takes other vehicles or traffic infrastructure as a reference point and obtains information such as the position and relative position of the reference point through a wireless network to position the vehicle; in recent years, the strong demand of the application of artificial intelligence technology prompts experts in a plurality of fields such as neuroscience, cognitive science, computational science and the like to try to disclose the mechanism of brain information perception and processing from different levels and try to simulate the functions of the brain information perception and processing; the researchers found that: the reason why the brain can rapidly process and respond to massive information of the surrounding environment is that the brain can consciously or unconsciously screen the information in a targeted manner, namely, a selective attention mechanism is provided, but in the prior art, the brain selective attention mechanism is not applied to the aspect of vehicle cooperative positioning, and corresponding well-known reports and researches are not provided.
Disclosure of Invention
Aiming at the existing problems, the invention aims to provide a vehicle cooperative localization method based on a brain selective attention mechanism, and the method is characterized in that the influence of a geometric structure, RB position precision and relative motion state formed between a cooperative reference node and a vehicle to be localized on cooperative localization is researched, a selective attention mechanism of human brain on information processing is provided, and the three influencing factors are used as characteristic points of information selection to selectively filter information; and then, the optimal RB is comprehensively screened out through feature integration so as to further improve the reliability and precision of vehicle cooperative positioning, and the method has the characteristics of high positioning precision and no environmental limitation.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a vehicle cooperative positioning method based on brain selective attention mechanism comprises
The method comprises the following steps: in a VANET environment, selecting N RB combinations from VTBP neighbor vehicles or ITS infrastructure nodes, calculating geometric accuracy characteristic values of the selected N RB combinations, and sorting the advantages and the disadvantages according to an ascending order;
step two: introducing a CSM model of a variance adjustment factor as a state equation, taking the position coordinate of the RB as an observed quantity, correcting the position coordinate of the RB, and simultaneously evaluating the precision of the position coordinate of the RB;
step three: comprehensively evaluating the RB relative position characteristic, the position precision characteristic and the relative motion characteristic; establishing a cooperative positioning equation set by using the evaluated optimal RB position coordinates and the relative distance between the optimal RB position coordinates and the VTBP, and solving the VTBP position coordinates;
step four: and establishing a dead reckoning model by taking the solved VTBP position coordinate as an observed quantity, and correcting the VTBP position coordinate, so as to obtain a final VTBP position coordinate value and complete vehicle cooperative positioning.
Preferably, the selection of N RB combinations and the calculation of the geometric precision characteristic value in step one include:
s1.1, setting the position coordinates of VTBP as unknown quantity (x, y), and selecting n neighbor vehicles or ITS infrastructure nodes from the surrounding environment as positioning RB i Wherein i =1,2, \8230;, n; RB (radio B) i Is a known quantity (x) i ,y i )；
S1.2 VTBP and RB can be obtained i Relative distance d therebetween i Comprises the following steps:
s1.3, calculating the geometric accuracy characteristic values of the selected N RB combinations, and sorting the advantages and the disadvantages according to ascending order.
Preferably, the calculation process of the geometric accuracy characteristic value in step S1.3 includes:
(1) Introducing measurement error e i Then there is an error of (e) in the position of the corresponding VTBP x ,e y ) Then, the formula (1) after adding the error is:
converting the formula (2) into a linear equation to obtain
(2) Order to
a i ＝(x-x i )/d i ，b i ＝(y-y i )/d i Equation (3) can be converted to a matrix form:
L＝HX+e (4)
in formula (4): l = [ L 1 ,l 2 ,…l n ] T ，X＝[e x ,e y ] T ，e＝[e 1 ,e 2 ,…e n ] T ，
(3) When H is full rank, H T H is reversible, then the error between the position estimate and the true value is:
the magnitude of the error in equation (5) is measured as covariance and yields:
in formula (6): sigma 2 The variance of the noise in e when the noise is uncorrelated two by two;
(4) As can be seen from formula (6), (H) T H) -1 Expressed as a magnification factor for the distance measurement error, and therefore defining the geometric position accuracy factor of the cooperative positioning of the vehicle as G, then
Wherein, in the formula (7), tr [ ] represents the inverse operation of the matrix;
(5) The operation of the formula (7) is simplified, the matrix inversion is equivalent to the sum of the eigenvalues of the matrix, and the corresponding formula (7) can be written as
In formula (8): lambda 1 、λ 2 Is a matrix H T A characteristic value of H;
and finally, obtaining a geometric position precision factor of vehicle cooperative positioning:
in the formula (9), det [ ] represents a determinant for matrix calculation.
Preferably, the process of correcting the position coordinates of the RB in real time in step two includes:
s2.1 adopts CSM as an RB carrier motion state equation, and is expressed as follows:
in the formula (10), X k+1 Is a state vector, phi k Being a state transition matrix, U k In order to input the control matrix, the control matrix is,
mean value of acceleration at time k, W k Mean value of zero and variance of Q k Gaussian distributed noise vector of (2); the above-mentioned
In formula (11), x k 、
Position, velocity and acceleration of RB, respectively; in equations (12) and (13), T is a sampling period; in the formula (14), τ is the maneuvering frequency,
the variance of the maneuvering acceleration of the carrier is shown, and q is a noise matrix;
s2.2 selection
For the mean acceleration at time k, the variance key is introducedSection factor eta k ＝μ(r k ) And then obtaining:
variance of acceleration
The update formula of (c) is:
in formula (25): a is max And a min Respectively representing the maximum value and the minimum value of the acceleration;
the equation for performing filter correction on the position coordinates of the RB carrier is:
preferably, the variance adjustment factor η is described in step S2.2 k The introduction process comprises the following steps:
(1) Let the innovation vector of the filter in KF be defined as:
innovation vector d k Is not relevant in the ideal case, and d k Is that the mean is zero and the variance is S k When the RB carrier motion is maneuvered, the maneuver changes the orthogonality of the information so that d k Is changed and is no longer zero, i.e.
(2) Normalizing the sequence of the innovation vectors to obtain statistics
(3) Establishing window detection statistic, and setting window size as m, window statistic wg of k moment k Is defined as
Coefficient of determination r k Is defined as
r k ＝wg k /wg k-1 (22)
When the RB carrier is not mobile, r k The value is usually close to 1, r when a maneuver occurs k The value increases rapidly and is proportional to the degree of mobility;
(4) In order to convert the mobility of the RB carrier into a variance adjustment factor, a raised-seminormal distribution function mu (u) is introduced,
(5) Will r is k As the input variable of the ascending seminormal distribution function mu (u), the variance adjustment factor eta can be obtained k
η k ＝μ(r k ) (24)。
Preferably, the specific process of step three includes:
s3.1, comprehensively evaluating the relative position characteristic, the position precision characteristic and the relative motion characteristic of the RB combination by adopting a fuzzy evaluation method, and selecting an optimal RB cooperative positioning combination;
and S3.2, establishing a cooperative positioning equation set by utilizing the position coordinate of the optimal RB and the relative distance between the optimal RB and the VTBP, solving the position coordinate of the VTBP by adopting a Taylor series analytic method, and establishing a cooperative positioning equation.
Preferably, the specific process of step four includes:
s4.1, establishing an observation equation by taking the solved VTBP position coordinate as an observed quantity, and establishing a dead reckoning model by the speed and the course angle measured by the VTBP;
s4.2, establishing a measurement model by taking the relative distance and the relative angle between the two vehicles as observed quantities and combining the position coordinates of the reference vehicle transmitted by the reference vehicle;
and S4.3, correcting the VTBP position coordinate by adopting an extended Kalman to obtain a final VTBP position coordinate value, and finishing the vehicle cooperative positioning.
The invention has the beneficial effects that: the invention discloses a vehicle cooperative positioning method based on brain selective attention mechanism, compared with the prior art, the improvement of the invention is as follows:
aiming at the problems in the prior art, the invention provides a Vehicle cooperative positioning method based on a brain selective attention mechanism, which particularly aims at the influence of a geometric structure, RB position precision and a relative motion state formed between a cooperative Reference node (RB) and a Vehicle To Be Positioned (VTBP) on cooperative positioning; adopting a selective attention mechanism of human brain to information processing, taking the three influencing factors as characteristic points of information selection, and carrying out selective filtering processing on the information; and then, the optimal RB is comprehensively screened out through feature integration so as to further improve the reliability and precision of vehicle cooperative positioning, and the method has the advantages of high positioning precision and no environmental limitation.
Drawings
Fig. 1 is a flowchart of a vehicle cooperative localization method based on brain selective attention mechanism according to the present invention.
Fig. 2 is a simplified diagram of a calculation process of the vehicle cooperative localization method based on the brain selective attention mechanism according to the present invention.
Fig. 3 is a schematic diagram of vehicle cooperative positioning in a VANET environment of the present invention.
FIG. 4 is a diagram of an attention model for optimal RB combination selectivity according to the present invention.
Fig. 5 is a schematic diagram of a motion trajectory of a vehicle in experimental verification of embodiment 1 of the present invention.
FIG. 6 is a graph comparing the results of the experiment of example 1 of the method of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the following description will be made with reference to the accompanying drawings and embodiments.
Referring to fig. 1-4, a method for cooperative localization of a vehicle based on brain selective attention mechanism includes
The method comprises the following steps: setting the position coordinates of a VTBP (vehicle to be positioned) as unknown quantities (x, y) in a VANET environment, selecting N RB (reference vehicle) combinations from neighbor vehicles or ITS infrastructure nodes according to a geometric barycenter method, wherein the selection rule is that the position of the VTBP at the last moment is positioned near the geometric barycenter of a polygon formed by the RB combinations, calculating geometric precision characteristic values of the selected N RB combinations, and performing quality sequencing according to an ascending order, wherein the smaller the geometric precision characteristic value is, the better the RB combination state is; the specific process comprises the following steps:
s1.1 real-time information interaction between vehicles and ITS infrastructure in VANET environment (as shown in FIG. 3), setting the position coordinates of VTBP in VANET environment as unknown quantity (x, y), and selecting n neighbor vehicles or ITS infrastructure nodes from the surrounding environment as positioning RB according to geometric gravity center method i Wherein i =1,2, \8230, N; RB (radio B) i Is a known quantity (x) i ,y i )；
S1.2 VTBP and RB can be obtained i Relative distance d between i Comprises the following steps:
s1.3, calculating the geometric accuracy characteristic values of the selected N RB combinations, wherein the calculation process is as follows:
(1) The actual measured VTBP and the reference point RB due to the measurement error of the ranging device i There will be an error e in the relative distance between i The position estimate of the corresponding VTBP has an error of (e) x ,e y ) Then, the formula (1) after adding the error is:
converting the formula (2) into a linear equation to obtain
(2) Order to
a i ＝(x-x i )/d i ，b i ＝(y-y i )/d i ，
Equation (3) can be converted to a matrix form:
L＝HX+e (4)
in formula (4): l = [ L 1 ,l 2 ,…l n ] T ，X＝[e x ,e y ] T ，e＝[e 1 ,e 2 ,…e n ] T ，
(3) When H is full rank, H T H is reversible, then the error between the position estimate and the true value is:
the magnitude of the error in equation (5) can be measured as covariance, resulting in:
in formula (6): sigma 2 The variance of the noise in e when the noise is uncorrelated two by two;
(4) From the formula (6), (H) can be seen T H) -1 Expressed as a magnification of the distance measurement error, thus, the vehicle is drivenThe geometric position precision factor of cooperative positioning is defined as G, then
In the formula (7), tr [ ] represents the inverse operation of the matrix;
when the vehicle to be positioned is cooperatively positioned, the combination of the minimum G value is selected as the optimal RB combination, the amplification effect on errors is minimum, but matrix inversion operation is required to be carried out when the formula (7) is adopted for carrying out calculation on G, the real-time performance is reduced due to the large calculation amount, and therefore the calculation of the formula (7) is required to be simplified;
(5) The operation of the formula (7) is simplified, the matrix inversion is equivalent to the sum of the eigenvalues of the matrix, and the corresponding formula (7) can be written as
In formula (8): lambda [ alpha ] 1 、λ 2 Is a matrix H T H characteristic value;
can finally obtain
In formula (9), det [ ] represents a determinant for solving a matrix;
obtaining a geometric accuracy characteristic value G, sequencing the N RB combinations according to the geometric accuracy characteristic value G, and when the geometric accuracy characteristic value G is smaller, indicating that the RB combination state is better;
step two: as can be seen from equation (1), when the vehicle is co-located, the position coordinate of the RB must be obtained, and usually, the position coordinate of the RB has a certain error, which inevitably affects the accuracy of the co-location; therefore, when the RB is selected, the position coordinate precision of the RB must be evaluated and corrected, and the RB with higher position precision is selected as the optimal RB combination;
through analyzing the motion characteristics of the vehicle, the vehicle motion has a certain motion rule in a relative time period, and the motion rule can be described by adopting a current statistical model of a motor carrier;
therefore, the improved current statistical model is used as a state equation of the vehicle, and the RB position coordinate sent by the RB is used as an observed quantity to carry out error correction and precision evaluation on the RB position coordinate, so that error interference on a cooperative positioning result is eliminated to a certain extent;
the method comprises the steps that a CSM model introducing variance adjustment factors is a state equation, position coordinates of RB are used as observed quantity, the position coordinates of RB are corrected in real time by adopting a formula (17), meanwhile, the precision of the position coordinates of RB is evaluated according to the difference value between the position coordinates of RB and the corrected value, and in addition, the stable stability of the relative motion state of RB is evaluated according to the position coordinates of RB;
the derivation process of utilizing the CSM model introduced with the variance adjustment factor as a state equation and utilizing the position coordinate of the RB as the observed quantity to modify the position coordinate of the RB in real time comprises the following steps:
s2.1 Current statistical motion model
The vehicle has a variable acceleration characteristic, and the current statistical model adopts a time-varying acceleration probability density function and an acceleration non-zero mean value time correlation model, so that the motion characteristic of the moving vehicle can be better described;
and CSM is adopted as an RB carrier motion state equation, and the equation is expressed as follows:
in formula (10), X k+1 Is a state vector, phi k Being a state transition matrix, U k In order to input the control matrix, the control matrix is input,
mean value of acceleration at time k, W k Mean value of zero and variance of Q k A gaussian distributed noise vector of (a); the described
In formula (11), x k 、
Position, velocity and acceleration of RB, respectively; in the formulas (12) and (13), T is a sampling period; in the formula (14), τ is the maneuvering frequency;
is the variance of the vehicle maneuvering acceleration; q is a noise matrix;
s2.2 selecting the mean value of the acceleration
Is taken as the current acceleration prediction value, i.e.
Variance of acceleration
Is taken as
In formula (16): a is a max And a min Respectively representing the maximum value and the minimum value of the acceleration;
the equation for filter correction of the position coordinates is
Correcting the position coordinates of the RB in real time by using the formula (17);
from the analysis of equations (14) and (16) it follows: system variance Q of motion model k Variance with acceleration
Is in direct proportion; a is max And a min After the value is determined, when the carrier maneuvers at a small acceleration, the system variance is large, and the filtering precision is low; when the carrier moves at a larger acceleration, the system variance is smaller, and the filtering precision is higher;
therefore, the conventional CSM has high filtering accuracy for a highly mobile carrier and low filtering accuracy for a weakly mobile carrier; CSM cannot describe the acceleration value as the interval [ (4-pi) a) -max /4,(4-π)a max /4]And weak maneuvering, resulting in lower filtering accuracy.
From the analysis of equations (14) and (16) it follows: system variance Q of motion model k Variance with acceleration
Proportional ratio of a max And a min After the value is determined, when the carrier maneuvers with a small acceleration, the system variance is large, and the filtering precision is low; when the carrier moves at a larger acceleration, the system variance is smaller, and the filtering precision is higher;
wherein the analysis finds the acceleration variance
The incoordination with the carrier mobility is the main reason of poor filtering effect, therefore, the invention designs the variance adjustment factor eta aiming at the problem k Adjusting the variance by a factor eta k Step S2.2 is introduced to the acceleration variance
Performing self-adaptive adjustment to improve CSM filtering accuracy, wherein the variance adjustment factor eta k The design process of (2) comprises:
(1) Let the innovation vector of the filter in KF be defined as:
innovation vector d k Is not relevant in the ideal case, and d k Is that the mean is zero and the variance is S k When the RB carrier moves, the movement changes the orthogonality of the new information, so that d k The mean value of (a) changes and is no longer zero;
(2) Innovation vector d k Obeying a multidimensional Gaussian distribution with a variance S k Subject to a degree of freedom of m (m being the vector dimension) 2 Distribution, it is generally possible to detect the mobility of the support and the divergence of the filters according to this multidimensional distribution property of the innovation, but this straightforward way is complex and therefore it is possible to normalize the sequence of innovation to obtain the statistic g k
(3) Establishing window detection statistic, and setting window size as m, window statistic wg of k moment k Is defined as
Coefficient of determination r k Is defined as
r k ＝wg k /wg k-1 (22)
When the RB carrier is not mobile, r k The value is usually close to 1, r when a maneuver occurs k The value increases rapidly and is proportional to the degree of manoeuvre;
(4) In order to convert the mobility of the RB carrier into a variance adjustment factor, a raised-seminormal distribution function mu (u) is introduced,
(5) Will r is k The variance adjustment factor eta can be obtained as an input variable of the ascending seminormal distribution function mu (u) k
η k ＝μ(r k ) (24)；
Obtaining variance adjustment factor eta k Adjusting the factor eta according to the variance k Adjusting the maneuvering acceleration variance in real time to obtain an acceleration variance updating formula:
in the formula:
is the average acceleration at time k; (ii) a
Acceleration variance according to equation (25)
And system variance Q k Can be self-adaptiveAdjusting, when the mobility of the carrier is weak, the variance adjusting factor eta k Is smaller, so that
And Q k The variance adjustment factor is introduced to ensure that the filtering keeps better robustness to model uncertainty.
Step three: comprehensively evaluating RB relative position characteristics, position precision characteristics and relative motion characteristics by adopting a fuzzy evaluation method, and selecting an optimal RB cooperation positioning combination; establishing a cooperative positioning equation set by using the position coordinate of the optimal RB and the relative distance between the optimal RB and the VTBP, and solving the position coordinate of the VTBP by adopting a Taylor series analytic method, wherein the specific process comprises the following steps:
s3.1, comprehensively evaluating the relative position characteristic, the position precision characteristic and the relative motion characteristic of the RB combination by adopting a fuzzy evaluation method, and selecting an optimal RB cooperative positioning combination;
firstly, carrying out normalization processing on relative position characteristics, position precision characteristics and relative motion characteristics of RB combinations; wherein the relative position features are geometric position precision values in the first step; the position precision characteristic is the average value of the difference value between the original value and the corrected value of each RB vehicle position in the step two; the relative motion characteristic is the average value of the difference value between the RB vehicle motion speed and the VTBP vehicle motion speed;
secondly, carrying out weighted summation on the normalized relative position characteristic, position precision characteristic and relative motion characteristic, wherein the summed value is a comprehensive evaluation value, and the smaller the value is, the better the corresponding RB vehicle combination is;
s3.2, establishing a cooperative positioning equation set by utilizing the position coordinates of the optimal RB and the relative distance between the optimal RB and the VTBP, solving the position coordinates of the VTBP by adopting a Taylor series analytic method, and establishing a cooperative positioning equation
The positioning coordinate is calculated by adopting a Taylor series method, and the calculation process is as follows:
step1: setting an initial coordinate estimation position (x) 0 ,y 0 ) And an error threshold, the error of the estimated position from the actual value of the coordinates being (e) x ,e y )；
Step2: set the localization equations in (x) 0 ,y 0 ) Expanding by Taylor series, neglecting terms of two or more
Transforming the above equation into matrix form: a. The b d＝b
When A is b When full rank, A b Reversible;
it can be derived that:
step3: the position of the updated coordinates is x = x 0 +e x ，y＝y 0 +e y (ii) a Judgment (e) x ,e y ) If the threshold value is smaller than the set threshold value, the iteration is stopped; if the error is larger than the set threshold value, the Step is switched to Step2 to continue iteration until the error is smaller than the set error threshold value.
Step four: establishing an observation equation by taking the solved VTBP position coordinate as an observed quantity, establishing a dead reckoning model by the speed and course angle measured by the VTBP, and correcting the VTBP position coordinate by adopting an extended Kalman to obtain a final VTBP position coordinate value so as to complete vehicle cooperative positioning, wherein the specific process comprises the following steps:
s4.1, establishing an observation equation by taking the solved VTBP position coordinate as an observed quantity, and establishing a dead reckoning model by the speed and the course angle measured by the VTBP:
in formula (30): x is the number of k Is the east position coordinate of the vehicle; y is k Is the north position coordinate of the vehicle; v. of k Is the running speed of the vehicle; theta k Is the driving steering angle.
S4.2 establishing a measurement model
The relative distance and the relative angle between two vehicles are used as observed quantities, and a system measurement equation generated by combining the position coordinates of the reference vehicle transmitted by the reference vehicle is
In formula (31):
position coordinates of the reference vehicle at the moment k; r is k The relative distance between vehicles at the moment k;
is the relative angle between the two workshops;
the angle between the reference vehicle and the X-axis direction;
s4.3 correcting the VTBP position coordinate by adopting an extended Kalman
In the filtering iterative process R k In order to measure the variance matrix of the error,in general R k Is determined according to the error of the measured vector, wherein
Through the process, the final VTBP position coordinate value is obtained, and vehicle cooperative positioning is completed; by utilizing the Vehicle cooperative positioning method based on the brain selective attention mechanism, the influence of a geometric structure, RB position precision and a relative motion state formed between a cooperative Reference node (RB) and a Vehicle To Be Positioned (VTBP) on cooperative positioning is specifically aimed at, a selective attention mechanism of human brain on information processing is adopted, and the influence factors of the three aspects are used as characteristic points of information selection to selectively filter information; and then, the optimal RB is comprehensively screened out through feature integration, and the reliability and the precision of vehicle cooperative positioning are further improved.
Example 1: data emulation
The coverage area of the Internet of vehicles is assumed to be a road area with the length of 500 meters and the width of 500 meters; the road area has 5 RB vehicles in total, each vehicle moves along different elliptical tracks, and the initial coordinates are (60.0, 15.4) m, (230.0, 400.1) m, (425, 127.9) m, (320.4, 480.0) m, and (480.0, 332.5) m; the movement speeds are respectively 20m/s, 40m/s, 30m/s, 45m/s and 25m/s; the motion track is shown in fig. 5;
the starting position of the VTBP vehicle is (250.0, 90.0), and the movement speed is 50m/s; in the experiment, the method and the traditional Taylor series positioning method are adopted, the number of RB vehicles selected by the two methods is 3, and the traditional Taylor series positioning method randomly selects 3 vehicles from 5 RB vehicles; fig. 6 is a comparison graph of experimental effects, in 150s experimental verification, the average error of the method of the present invention is 1.9m, while the average error of the conventional taylor series positioning method is 4.9m, and the effectiveness of the method of the present invention can be seen from comparison data.
The foregoing shows and describes the general principles, principal features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A vehicle cooperative localization method based on brain selective attention mechanism is characterized in that: comprises the steps of
The method comprises the following steps: in a VANET environment, selecting N RB combinations from VTBP neighbor vehicles or ITS infrastructure nodes, calculating geometric precision characteristic values of the selected N RB combinations, and sorting the advantages and the disadvantages according to an ascending order;
step one, the selection of the N RB combinations and the calculation process of the geometric accuracy characteristic values comprise the following steps:
s1.1, setting the position coordinates of VTBP as unknown quantity (x, y), and selecting n neighbor vehicles or ITS infrastructure nodes from the surrounding environment as positioning RB i Wherein i =1,2, \8230;, n; RB (radio B) i Is a known quantity (x) i ,y i )；
S1.2 obtaining VTBP and RB i Relative distance d between i Comprises the following steps:
s1.3, calculating geometric accuracy characteristic values of the selected N RB combinations, and performing quality sorting according to an ascending order;
step two: introducing a CSM model of a variance adjustment factor as a state equation, taking the position coordinate of the RB as an observed quantity, correcting the position coordinate of the RB, and simultaneously evaluating the position coordinate precision of the RB;
the process of correcting the position coordinates of the RB in real time comprises the following steps:
s2.1 adopts CSM as an RB carrier motion state equation, and is expressed as follows:
in the formula (10), X k+1 Is a state vector, phi k Being a state transition matrix, U k In order to input the control matrix, the control matrix is input,
mean value of acceleration at time k, W k Is a mean of zero and a variance of Q k Gaussian distributed noise vector of (2); the above-mentioned
In formula (11), x k 、
Position, velocity and acceleration of RB, respectively; in equations (12) and (13), T is a sampling period; in the formula (14), τ is the maneuvering frequency,
is the variance of the maneuvering acceleration of the carrier, and q is a noise matrix;
s2.2 selection
For the mean acceleration at time k, a variance adjustment factor eta is introduced k ＝μ(r k ) Obtaining:
variance of acceleration
The update formula of (2) is:
in formula (25): a is max And a min Respectively representing the maximum value and the minimum value of the acceleration;
the equation for performing filtering correction on the position coordinates of the RB carrier is as follows:
step S2.2 the variance adjustment factor eta k The introduction process of (2) comprises:
(1) Let the innovation vector of the filter in KF be defined as:
innovation vector D k Is not relevant in the ideal case, and D k Is that the mean is zero and the variance is S k When the RB carrier moves, the movement changes the orthogonality of the new information, so that D is k Has changed mean value and is no longer zero, i.e.
(2) Normalizing the sequence of the innovation vectors to obtain statistics
(3) Establishing window detection statistic, and setting window size as m, window statistic wg of k moment k Is defined as
Coefficient of determination r k Is defined as
r k ＝wg k /wg k-1 (22)
When the RB carrier is not mobile, r k Value close to 1, r when a maneuver occurs k The value increases rapidly and is proportional to the degree of mobility;
(4) In order to convert the mobility of the RB carrier into a variance adjustment factor, a raised-seminormal distribution function mu (u) is introduced,
(5) The variance adjusting factor eta can be obtained by taking rk as the input variable of the raised seminormal distribution function mu (u) k
h k ＝μ(r k ) (24)；
Step three: comprehensively evaluating the RB relative position characteristic, the position precision characteristic and the relative motion characteristic; establishing a cooperative positioning equation set by using the evaluated optimal RB position coordinate and the relative distance between the optimal RB position coordinate and the VTBP, and solving the VTBP position coordinate;
step four: and establishing a dead reckoning model by taking the solved VTBP position coordinate as an observed quantity, and correcting the VTBP position coordinate, so as to obtain a final VTBP position coordinate value and complete vehicle cooperative positioning.
2. The brain-selective attention mechanism-based vehicle cooperative localization method according to claim 1, wherein: the calculation process of the geometric accuracy characteristic value in the step S1.3 includes:
(1) Introducing measurement error e i Then there is an error of (e) in the position of the corresponding VTBP x ,e y ) Then, the formula (1) after adding the error is:
converting the formula (2) into a linear equation to obtain
(2) Order to
a i ＝(x-x i )/d i ，b i ＝(y-y i )/d i ，
Equation (3) can be converted to a matrix form:
L＝HX+E (4)
in formula (4): l = [ L = 1 ,l 2 ,…l n ] T ，X＝[E x ,E y ] T ，E＝[E 1 ,E 2 ,…E n ] T ，
(3) When H is full rank, H T H is reversible, then the error between the position estimate and the true value is:
the magnitude of the error in equation (5) is measured by covariance and yields:
in formula (6): sigma 2 The variance of the noise in e when the noise is uncorrelated two by two;
(4) As can be seen from formula (6), (H) T H) -1 Expressed as a magnification factor for the distance measurement error, and therefore defining the geometric position accuracy factor of the cooperative positioning of the vehicle as G, then
Wherein, in the formula (7), tr [ ] represents the inverse operation of the matrix;
(5) The operation of the formula (7) is simplified, matrix inversion is equivalent to the sum of matrix eigenvalues, and the corresponding formula (7) can be written as
In formula (8): lambda 1 、λ 2 Is a matrix H T A characteristic value of H;
and finally, obtaining a geometric position precision factor of vehicle cooperative positioning:
in the formula (9), det [ ] represents a determinant for matrix calculation.
3. The brain-selective attention mechanism-based vehicle cooperative localization method according to claim 1, wherein: the concrete process of the third step comprises:
s3.1, comprehensively evaluating the relative position characteristic, the position precision characteristic and the relative motion characteristic of the RB combination by adopting a fuzzy evaluation method, and selecting an optimal RB cooperation positioning combination;
and S3.2, establishing a cooperative positioning equation set by utilizing the position coordinate of the optimal RB and the relative distance between the optimal RB and the VTBP, solving the position coordinate of the VTBP by adopting a Taylor series analytic method, and establishing a cooperative positioning equation.
4. The brain-selective attention mechanism-based vehicle cooperative localization method according to claim 1, wherein: the concrete process of the step four comprises:
s4.1, establishing an observation equation by taking the solved VTBP position coordinate as an observed quantity, and establishing a dead reckoning model by the speed and the course angle measured by the VTBP per se;
s4.2, establishing a measurement model by taking the relative distance and the relative angle between the two vehicles as observed quantities and combining the position coordinates of the reference vehicle transmitted by the reference vehicle;
and S4.3, correcting the VTBP position coordinate by adopting an extended Kalman to obtain a final VTBP position coordinate value, and finishing the vehicle cooperative positioning.
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