CN107801195A - A kind of roadside unit Optimization deployment method in car networking positioning - Google Patents
A kind of roadside unit Optimization deployment method in car networking positioning Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
Abstract
A kind of roadside unit Optimization deployment method in being positioned the invention provides car networking, using Measure Indexes of the geometric dilution of precision as evaluation car networking positioning performance, and K covering roadside unit deployment issues are decomposed into 1 simplified covering deployment issue first when the deployment issue of roadside unit under K coverage conditions is modeled as into majorized function modeling, the deployment basic deployment mode of efficiency highest is provided by the geometrical analysis to each roadside unit deployment mode, then K covering scenes are expanded to, the spatial group merging for seeking the basic deployment mode of K layers is converted into a majorized function, followed by asynchronous particle swarm optimization algorithm majorized function, to reduce computation complexity, heuristic search is carried out to roadside unit deployed position.The present invention can improve the deployment efficiency of roadside unit, system is obtained optimal positioning performance under K covering scenes by optimizing roadside unit deployed position.
Description
Technical field
The invention belongs to wireless location technology field, is related to a kind of car networking positioning field roadside unit (Roadside
Unit, RSU) deployment techniques.
Background technology
In car networking (Vehicle Ad-hoc Networks, VANETs) application, vehicle location, which is one, has weight
Want the application service of meaning and very big attraction.Because precision and reliability are inadequate, conventional global positioning system (GPS) and its
Improved technology is not particularly suited for the car networking application scenarios such as urban canyons and tunnel with stronger multipath effect.Therefore it is directed to this
A little scenes, the car networking location technology based on roadside unit (Roadside Unit, RSU) have caused extensive pass in recent years
Note.In car networking positioning, roadside unit can be counted as the known positioning anchor node in fixed and position, by itself and vehicle it
Between VANET communication links provide positioning metrical information, as received signal strength (Received Signal Strength,
RSS), arrival time (Time of Arrival, TOA) and angle of arrival (Direction of Arrival, DOA) etc., it is to improve
The reliable means of positioning performance.Above-mentioned metrical information can be converted into range information or the directly pattern of extraction information by vehicle
Feature carries out the positioning estimation to self-position.Simultaneously it was discovered by researchers that positioning system in the car networking that range error be present
In system, the geographic layout of roadside unit can significantly affect the positioning performance of any special algorithm.And existing car networking positioning
Research, which is concentrated mainly on, to improve location algorithm precision and reduces on computation complexity, usually assumes that and is deployed with enough roadside lists
Member.Therefore, with the sustainable development of car networking location technology, the deployment scheme of roadside unit is systematically studied to systematic function
Influence, while propose that a kind of reasonable, efficient roadside unit dispositions method seems particularly significant.
The content of the invention
To solve the above problems, the invention discloses a kind of roadside unit (RSU) dispositions method, using geometric accuracy because
The Measure Indexes of sub (GDOP) as evaluation car networking positioning performance, and the deployment issue of roadside unit under K coverage conditions is built
Mould is majorized function, finally carries out heuristic search using asynchronous particle swarm optimization algorithm, by disposing roadside as few as possible
Unit causes localization region when meeting that K re-covers lid, to obtain best positioning performance.
For the car networking alignment system based on roadside unit, the factor of system positioning performance is influenceed except roadside unit
Deployment mode, i.e., outside geometry distribution geographically, the coverage of roadside unit is also a key factor for needing to study.
Coverage represents the quantity for the roadside unit that any position of the vehicle in localization region may be coupled to, its portion with roadside unit
It is relevant to affix one's name to density.From the point of view of directly perceived, the roadside unit of dense deployment can bring the redundant measurements for being advantageous to positioning, so as to effectively
The positioning performance of raising system.But lower deployment cost is considered, the coverage of roadside unit is often limited, and some specific
Location algorithm can require to coverage again, and such as the coverage in three side positioning requirements regions is not less than 3, and triangle polyester fibre then requires to cover
Cover degree is not less than 2.Therefore, for the ease of analyzing and improving universality, the present invention gives the coverage K of a roadside unit first
(K >=3), i.e., the Optimization deployment pattern of roadside unit is studied under the scene of K coverings, utilization roadside as few as possible is single
Member is come the positioning performance that makes system reach optimal.
A kind of roadside unit Optimization deployment method in being positioned the invention provides car networking, comprises the following steps：
1) using the performance of geometric dilution of precision measurement roadside unit deployment scheme, and fischer information matrix and system are utilized
Model is counted, the geometric dilution of precision expression formula under RSS and mixing two kinds of positioning methods of TOA/DOA is described respectively；
2) it is 1 covering scene first by problem reduction, i.e., by the analysis to K covering scene roadside unit deployment issues
Every bit is covered by a roadside unit in localization region, and the roadside unit optimized under this scene is obtained by geometrical analysis
Basic deployment mode；
3) it is an optimization problem by the roadside unit Deployment modeling of K covering scenes, by right on the basis of step 2)
The Spatial Coupling optimization of the basic deployment mode of K layers, it is established that to minimize optimization of the zone leveling geometric dilution of precision as target
Function；
4) majorized function described in step 3) is solved using asynchronous particle swarm optimization algorithm, obtained by iterative search
Obtain the optimization deployed position of roadside unit.
Further, fischer information matrix is calculated by the joint probability density function of measurement vector in the step 1)
Draw, the geometric dilution of precision of location estimation is derived by by fischer information matrix, the fischer information square in statistical model
Battle array is relevant with the Jacobian matrix of observation.
Further, the GDOP expression formulas of the RSS positioning methods are
Wherein,WithThe variance of x-axis y-axis error of coordinate is represented respectively,The variance of range error is represented,Representation vector μ Jacobian matrix；
Mixing TOA/DOA positioning methods GDOP expression formulas be
In above formula, FuRepresent p (ri| the fischer information matrix of log-likelihood function u)：
Wherein, γiTo represent the actual position coordinate of vehicle to the related channel gain of distance, u=(x, y).
Further, optimal roadside unit base is obtained by the geometrical analysis to different deployment modes in the step 2)
This deployment mode.
Further, the different deployment modes include linear deployment pattern and staggeredly deployment mode.
Further, using the average geometric dilution of precision of localization region as mesh when establishing majorized function in the step 3)
Mark, using the space displacement of the basic deployment mode of K layers as optimized variable, single object optimization is established as by the deployment issue of roadside unit
Model.
Further, object function is defined as：
F (η, K)=| | η | |-1∫ηGDOP(uRSU, uη)duη
Wherein, | | η | | represent region η area, uRSUAnd uηThe RSU positions that K coverings are realized to region η are represented respectively
Any point in set and region η, K is coverage.
Further, asynchronous particle swarm optimization algorithm is used in the step 4), by set the study of asynchronous time-varying because
Son and inertia weight, iterative search obtain the roadside unit deployed position optimized.
Further, the search rate equation of the asynchronous particle swarm optimization algorithm and state renewal equation are respectively：
Wherein, k is iterations,Change for current state,For new state,For present rate,For
Particle i optimum state value,For the optimum state value of all particles, ω represents inertia weight, c1And c2Represent i points of particle
Not toWithThe asynchronous training factor of motion.
Further, by states of the motion vector d of (K-1) dimension as population in step 4)
Compared with prior art, the invention has the advantages that and beneficial effect：
The present invention using geometric dilution of precision as the Measure Indexes for evaluating car networking positioning performance, and by K coverage conditions
The deployment issue of lower roadside unit is modeled as majorized function.K covering roadside unit deployment issues are decomposed into letter first during modeling
The 1 covering deployment issue changed, deployment efficiency highest basic portion is provided by the geometrical analysis to each roadside unit deployment mode
Administration's pattern, K covering scenes are then expanded to, the spatial group merging for seeking the basic deployment mode of K layers is converted into an optimization
Function, this method utilizes asynchronous particle swarm optimization algorithm majorized function, and to reduce computation complexity, roadside unit is disposed
Position carries out heuristic search, so as to obtain the K of optimization covering roadside unit position coordinateses.The present invention can cover field in K
By optimizing roadside unit deployed position under scape, the deployment efficiency of roadside unit is improved, system is obtained optimal positioning performance.
Brief description of the drawings
Fig. 1 is the roadside unit deployment scenario in car networking alignment system.
Fig. 2 is 1 covering scene lower linear deployment mode and the staggeredly geometrical analysis of deployment mode, wherein (a) is linear portion
Administration's pattern, (b) are staggeredly deployment mode.
Fig. 3 is the Spatial Coupling schematic diagram of the basic deployment mode of multilayer under K covering scenes.
Embodiment
Technical scheme provided by the invention is described in detail below with reference to specific embodiment, it should be understood that following specific
Embodiment is only illustrative of the invention and is not intended to limit the scope of the invention.
The present invention measures the performance of deployment mode using geometric dilution of precision, and provides a two stage deployment plan
Slightly：First by 1 covering scene that problem reduction is roadside unit, a kind of optimal basic deployment mode is found；Secondly covered for K
Lid scene, the deployment of roadside unit can be modeled as an optimization problem, under RSS and mixing two kinds of positioning methods of TOA/DOA,
Zone leveling GDOP is minimized by seeking the optimum organization of the basic deployment mode of K layers；Finally use asynchronous particle group optimizing
Algorithm carries out heuristic search, to obtain the roadside unit deployed position of global optimum.
Specifically, the present invention comprises the following steps：
1) performance of roadside unit deployment scheme is measured using geometric dilution of precision, and utilizes fischer information matrix
(Fisher Information Matrix, FIM) and statistical model, respectively to RSS and mixing two kinds of positioning methods of TOA/DOA
Under geometric dilution of precision expression formula be described；
For RSS present in car networking positioning and mixing two kinds of positioning methods of TOA/DOA, the present invention passes through measurement vector
Joint probability density function calculate its fischer information matrix (FIM), in statistical model, FIM and the refined of observation can
It is more relevant than matrix, so as to be derived by the geometric dilution of precision of location estimation by FIM.The derivation of geometric dilution of precision expression formula
Process is as follows：
Geometric dilution of precision is applied in GPS system first, for assessing the position of satellite to receiver positioning precision
Influence, it is defined asWhereinWithThe variance of x-axis y-axis error of coordinate is represented respectively,Represent
The variance of range error.GDOP is introduced car networking positioning by the anchor node dispositions method of the present invention, for analyzing roadside unit
Influence of the deployed position to system positioning performance, GDOP values are smaller, it is meant that roadside unit has better geometric layout, system
Position error is influenceed smaller by range error.
As a rule, car networking location algorithm can be divided mainly into the algorithm based on RSS and the algorithm based on mixing TOA/DOA
Two species, the present invention are derived respectively to the GDOP expression formulas of two types algorithm.To the location algorithm of RSS modes,
It derives similar with the GDOP derivations that TOA pseudoranges are directed in GPS system.Car networking positioning scene is as shown in figure 1, make vehicle
Observation vector r={ the P received at K RSUi, i=1 ..., K, wherein PiRepresent the signal received at i-th of RSU
Intensity, it is known that PiGaussian distributedWherein, generally can be from the environmental information of priori for reference to mean receiving power
Middle acquisition.Distance of the vehicle at i-th of RSU can be correspondingly expressed as di=| | ui- u | |, wherein ui=(xi, yi) represent
I RSU position coordinates, u=(x, y) represent the actual position coordinate of vehicle.Make uRSURepresent RSU position coordinateses in region
Set, then the joint probability density function of vehicle receiver observation vector can be expressed as：
In above formulaIts fischer information matrix is：
Wherein E { * } represents that computational mathematics it is expected, orderRepresentation vector μ Jacobian matrix, RSS positioning can be obtained
The GDOP expression formulas of mode are
Location algorithm for mixing TOA/DOA modes, vehicle are generally equipped with receiving antenna array, pass through ARRAY PROCESSING skill
TOA and DOA parameters needed for art Combined estimator, to reach higher location estimation precision.Assuming that vehicle has the equal of M root antennas
Even linear array receiver, for known unit energy transmission signal, the Baseband Receiver letter that receiver obtains at i-th of RSU
Number it can be expressed as ri(t)=[rI, 0(t) ..., rI, (M-1)(t)], i=1 ..., K, wherein
ri(t)=a (θi)γis(t-τi)+ni(t)
In above formula, γiFor to distance related channel gain, τiAnd θiRespectively correspond to i-th of RSU propagation delay time
TOA and angle of arrival DOA, ni(t) represent that average is zero, power spectral density isAdditive white Gaussian noise, N0To be unilateral
Power spectral density.a(θi) representative antennas response vector, have
Wherein parameter ρ, f and c represent antenna spacing, the carrier frequency of signal and transmission speed respectively, for the ease of deriving GDOP,
Reception signal is converted into discrete form by us, is sampled by the N points discrete time that the sampling interval is T, reception signal ri(t)
Joint probability density function is represented by
Wherein μi(tk)=a (θi)γis(tk-τi), tkFor k-th of sampling instant, geometrical relationship x=x is utilizedi+cτicos
θiAnd y=yi+cτisinθi, we can be by p (ri|τi, θi) it is expressed as p (ri| u), TOA/DOA can must be mixed by further deriving
The GDOP expression formulas of positioning method are
In above formula, FuRepresent p (ri| the fischer information matrix of log-likelihood function u)：
2) can be that 1 covering is (complete first by problem reduction by the analysis to K covering scene roadside unit deployment issues
Covering) scene, i.e., every bit is covered by a roadside unit in localization region, can pass through the geometry point to different deployment modes
Analysis obtains the basic deployment mode of roadside unit optimized under this scene, to maximize deployment efficiency.
It is as follows that basic deployment mode under 1 covering scene obtains process：
When studying the deployment issue of K covering roadside units, we are reduced to one 1 covering (all standing) scene first
Under deployment issue, i.e., when every bit is covered by least one roadside unit in localization region, find road on this condition
The optimal deployment mode of side unit (Optimal Placement Pattern, OPP), alternatively referred to as basic deployment mode, and K covers
Optimization deployment under lid scene then only needs conveniently to obtain the stack combinations that above-mentioned basic deployment mode is carried out spatially.
In view of minimizing lower deployment cost, it is assumed that the maximum communication distance of roadside unit is R, and 1 covering roadside unit is disposed
Problem can be converted into the problem of how carrying out all standing to localization region using the disk model that minimum radius is R.By nothing
The inspiration of the triangle gridding pattern of line sensor network interior joint covering, it is easy to which it is to ensure all standing most to prove to be uniformly distributed
Good layout.Because roadside unit is generally disposed at both sides of the road, and its communication radius is far longer than the width of road, present invention weight
Two kinds of uniform deployment modes of typical RSU of point analysis：Linear deployment pattern and staggeredly deployment mode, respectively such as Fig. 2 (a) and
(b) shown in, to obtain the deployment mode of deployment cost minimization (efficiency highest).It is L to make road width, from geometrical analysis,
Spacing in Fig. 2 (a) in linear deployment mode between adjacent R SU is (along road direction)And handed in Fig. 2 (b)
The adjacent R SU spacing of wrong deployment mode isThere is DS＜ DL, therefore staggeredly deployment mode may be considered completely
Efficiency highest optimal deployment mode is disposed during sufficient all standing condition.
3) on the Research foundation of step 2), the roadside unit deployment of K covering scenes can be modeled as an optimization problem,
By optimizing to the Spatial Coupling of the basic deployment mode of K layers, it is established that to minimize zone leveling geometric dilution of precision as target
Majorized function.
After the optimal deployment mode (OPP) of 1 covering is searched out, road that we can be expanded under K covering scenes
Side unit deployment issue.To meet the needs of coverage, a kind of available mode is to fix one layer of OPP roadside unit conduct first
Basis deployment, then there is different skies between the additional OPP roadside units of (K-1) layer of superposition deployment thereon, each layer of OPP
Between displacement, resulting K layers roadside unit deployment combination can make localization region reach K covering, and dispose efficiency highest.Together
When, the deployment combination of this roadside unit should also meet the needs of maximization system positioning performance, i.e., reduce whole localization region as far as possible
Average GDOP values.In view of the symmetry in space, we take arbitrary trapezoidal net region η as survey region (Fig. 2
(b) dotted line frame in), then the deployment of roadside unit can be converted into the optimization problem on a region η, and its object function can be with
It is defined as
F (η, K)=| | η | |-1∫ηGDOP(uRSU, uη)duη
Parameter in above formula | | η | | represent region η area, uRSUAnd uηRepresent respectively and K coverings are realized to region η
Any point in RSU location sets and region η, K covering scenes roadside can be obtained by minimizing optimization object function f (η, K)
The optimization deployment mode of unit.
4) majorized function described in step 3) is solved using asynchronous particle swarm optimization algorithm, obtained by iterative search
Obtain the optimization deployed position of roadside unit.
During using asynchronous particle swarm optimization algorithm, by setting the Studying factors and inertia weight of asynchronous time-varying, iteration is searched
Rope obtains the roadside unit deployed position optimized, while reduces computation complexity.Its specific solution procedure is as follows：
For the ease of solving above-mentioned optimization problem, we take any RSU positions in first layer OPP former as coordinate first
Point o, the trapezoid area using centered on o is taken to be used as survey region η.Noticing every layer of OPP of superposition can regard as first layer OPP
Gained after doing corresponding displacement along road edge, therefore make displacement set d=[d1..., di..., dK-1], di∈(-DS, DS), then such as
Shown in Fig. 3, i-th layer of OPP corresponds to displacement di, jth layer OPP correspond to displacement dj, above-mentioned optimization problem, which can be converted into, finds one most
The displacement set d of optimization, the roadside unit of generation is disposed combination and minimize object function f (η, K).This optimization problem is
NP-hard problems, the present invention are solved using asynchronous particle swarm optimization algorithm (APSO) to be iterated.APSO algorithms are in population
The Studying factors of asynchronous time-varying and the inertia weight of linear decrease are employed in search procedure, can greatly reduce iterative calculation
Complexity, its search rate equation and state renewal equation are respectively：
In the kth time iterative process of APSO algorithms, each particle i is via speedBy current stateChange turns to new
StateAnd new speedBy present rateParticle i optimum state valueWith the optimum state of all particles
ValueTogether decide on, ω represents inertia weight, c1And c2Represent particle i respectively toWithThe asynchronous training factor of motion.
In the solution procedure of the present invention, by states of the motion vector d of (K-1) dimension as populationAnd the state of current particle
Value can be then calculated by object function f (η, K), and the final output of iterative process is the optimization deployment position of roadside unit
Put.
Technological means disclosed in the present invention program is not limited only to the technological means disclosed in above-mentioned embodiment, in addition to
Formed technical scheme is combined by above technical characteristic.It should be pointed out that for those skilled in the art
For, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also considered as
Protection scope of the present invention.
Claims (10)
1. a kind of roadside unit Optimization deployment method in car networking positioning, it is characterised in that comprise the following steps：
1) using the performance of geometric dilution of precision measurement roadside unit deployment scheme, and fischer information matrix and statistics mould are utilized
Type, the geometric dilution of precision expression formula under RSS and mixing two kinds of positioning methods of TOA/DOA is described respectively；
2) by the analysis to K covering scene roadside unit deployment issues, it is 1 covering scene first by problem reduction, that is, positions
Every bit is covered by a roadside unit in region, and the roadside unit for obtaining optimizing under this scene by geometrical analysis is basic
Deployment mode；
3) it is an optimization problem by the roadside unit Deployment modeling of K covering scenes, by K layers on the basis of step 2)
The Spatial Coupling optimization of basic deployment mode, it is established that to minimize optimization letter of the zone leveling geometric dilution of precision as target
Number；
4) majorized function described in step 3) is solved using asynchronous particle swarm optimization algorithm, road is obtained by iterative search
The optimization deployed position of side unit.
2. the roadside unit Optimization deployment method in car networking positioning according to claim 1, it is characterised in that the step
It is rapid 1) in fischer information matrix be calculated by the joint probability density function of measurement vector, pushed away by fischer information matrix
Lead to obtain the geometric dilution of precision of location estimation, fischer information matrix and the Jacobian matrix of observation have in statistical model
Close.
3. the roadside unit Optimization deployment method in car networking positioning according to claim 2, it is characterised in that described
The GDOP expression formulas of RSS positioning methods are：
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Mixing TOA/DOA positioning methods GDOP expression formulas be：
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In above formula, FvRepresent p (ri| the fischer information matrix of log-likelihood function u)：
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Wherein, riTo represent the actual position coordinate of vehicle to the related channel gain of distance, u=(x, y).
4. the roadside unit Optimization deployment method in car networking positioning according to claim 1, it is characterised in that the step
It is rapid 2) in by the geometrical analysis to different deployment modes, obtain the basic deployment mode of optimal roadside unit.
5. the roadside unit Optimization deployment method in car networking according to claim 4 positioning, it is characterised in that it is described not
Include linear deployment pattern and staggeredly deployment mode with deployment mode.
6. the roadside unit Optimization deployment method in car networking positioning according to claim 1, it is characterised in that the step
It is rapid 3) in when establishing majorized function using the average geometric dilution of precision of localization region as target, with the sky of the basic deployment mode of K layers
Between displacement be optimized variable, the deployment issue of roadside unit is established as single object optimization model.
7. the roadside unit Optimization deployment method in car networking positioning according to claim 6, it is characterised in that target letter
Number is defined as：
F (η, K)=| | η | |-1∫ηGDOP(uRSU, uη)duη
Wherein, | | η | | represent region η area, uRSUAnd uηRepresent respectively region η is realized K covering RSU location sets and
Any point in the η of region, K are coverage.
8. the roadside unit Optimization deployment method in car networking positioning according to claim 1, it is characterised in that the step
Rapid 4) middle using asynchronous particle swarm optimization algorithm, by setting the Studying factors and inertia weight of asynchronous time-varying, iterative search obtains
The roadside unit deployed position that must be optimized.
9. the roadside unit Optimization deployment method in car networking positioning according to claim 8, it is characterised in that described different
Walk particle swarm optimization algorithm search rate equation and state renewal equation be respectively：
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Wherein, k is iterations,Change for current state,For new state,For present rate,For particle i
Optimum state value,For the optimum state value of all particles, ω represents inertia weight, c1And c2Represent particle i respectively toWithThe asynchronous training factor of motion.
10. the roadside unit Optimization deployment method in car networking positioning according to claim 9, it is characterised in that in step
Rapid 4) the middle state by the motion vector d of (K-1) dimension as population
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