CN112558119B - Satellite selection method based on self-adaptive BFO-PSO - Google Patents
Satellite selection method based on self-adaptive BFO-PSO Download PDFInfo
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Abstract
The invention provides a satellite selection method based on self-adaptive BFO-PSO, which comprises the following steps: step 1: combining the satellite space geometric distribution and the signal-to-noise ratio to construct a signal-to-noise ratio weighted geometric precision factor; step 2: extracting visible satellites from navigation messages received by a receiver, and iteratively searching satellite combinations under a given number by utilizing self-adaptive BFO-PSO; step 3: and (3) selecting an optimized satellite combination which can meet the requirement of the signal-to-noise ratio weighted geometric precision factor by combining the satellite combination searched in the step (2) and the contribution degree of the rest satellites. The satellite contribution degree operator is introduced to replace the signal-to-noise ratio weighted geometric precision factor to calculate the rest satellites, and matrix inversion operation is only needed to be carried out once for each satellite, so that the inversion operation times of the signal-to-noise ratio weighted geometric precision factor are reduced, and the calculation speed is further increased.
Description
Technical Field
The invention belongs to the field of satellite positioning navigation, and relates to a satellite selection method based on self-adaptive BFO-PSO.
Background
With the continuous construction and development of the global satellite navigation system, the number of visible satellites available to the receiver is remarkably increased, objective conditions are provided for improving the navigation positioning performance, and the signal processing burden of the receiver is increased. Therefore, how to select a proper satellite combination from visible satellites, and reduce the calculation amount while meeting the positioning requirement becomes a research hot spot.
The existing star selection method mainly uses a geometric precision factor (GDOP) optimal star selection algorithm, and the combination which minimizes the GDOP value is found by traversing all visible satellite combinations, however, the star selection method has large calculation amount and affects real-time performance, so that partial scholars introduce a Particle Swarm Optimization (PSO) algorithm to reduce the calculation amount of the GDOP, and the PSO has poorer local searching capability and is easy to fall into the problem of 'early ripening' caused by local optimization; secondly, the GDOP only considers the spatial geometrical distribution of satellites and does not consider the signal quality problem which has a great influence on the positioning accuracy as well. And GDOP involves matrix inversion, computation is relatively time consuming.
Disclosure of Invention
The invention aims to provide a satellite selection method based on self-adaptive BFO-PSO, firstly, a bacterial foraging algorithm (BFO) with stronger local search capability is introduced to improve a particle swarm algorithm PSO, so that the optimizing capability of the particle swarm algorithm PSO is improved, and in addition, the searching efficiency of the PSO can be improved by introducing a self-adaptive weight factor; secondly, constructing a signal-to-noise ratio weighted geometric precision factor (SWGDOP) to replace the GDOP as a fitness function, and comprehensively considering satellite geometric distribution and signal quality; finally, a satellite contribution degree operator is provided, the number of times of SWGDOP inversion calculation is reduced, and the calculation speed is improved.
The technical scheme of the invention is as follows: step 1: combining the satellite space geometric distribution and the signal-to-noise ratio to construct a signal-to-noise ratio weighted geometric precision factor; step 2: extracting visible satellites from navigation messages received by a receiver, and iteratively searching satellite combinations under a given number by utilizing self-adaptive BFO-PSO; step 3: and (3) selecting an optimized satellite combination which can meet the requirement of the signal-to-noise ratio weighted geometric precision factor by combining the satellite combination searched in the step (2) and the contribution degree of the rest satellites.
The invention has the following beneficial effects:
(1) The conventional GDOP satellite selection algorithm only considers the space geometric distribution of satellites and cannot screen out satellite signals seriously interfered. The signal-to-noise ratio can effectively reflect the quality of satellite observation signals, and the SWGDOP introduced by the invention comprehensively considers the geometric distribution and the signal-to-noise ratio of the satellite, can further reduce the influence of signal disturbance and improve the positioning precision;
(2) PSO is a fast optimizing algorithm, has the characteristics of simple calculation, easy realization, high convergence speed and the like, and can reduce the calculation times and time consumption of SWGDOP. However, PSO has weak local searching capability, and is prone to being trapped in local optimum, so that the problem of 'early ripening' occurs. BFO has stronger local searching capability, the local searching capability of PSO can be enhanced through the chemotactic process of introducing BFO, and the migration process of introducing BFO can lead the particle swarm to have the capability of jumping out of local optimum. In addition, the self-adaptive weight factors are introduced to dynamically adjust the inertia weights, so that the PSO searching efficiency can be improved;
(3) After the optimal satellite combination under the given number is obtained through the self-adaptive BFO-PSO algorithm, the satellite number can be gradually increased on the basis of the combination to improve the satellite selection performance. According to the method, the satellite combination is not required to be selected again for iterative search of BFO-PSO, so that the calculation efficiency is improved;
(4) The satellite contribution degree operator is introduced to replace SWGDOP to calculate the rest satellites, and matrix inversion operation is only needed to be carried out once for each satellite, so that the frequency of SWGDOP inversion operation is reduced, and the calculation speed is further increased.
Drawings
FIG. 1 is an overall flowchart of an algorithm;
FIG. 2 is a flow chart of an adaptive BFO-PSO star selection algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
As shown in fig. 1-2, the present invention proposes a satellite selection method based on adaptive BFO-PSO, the overall flowchart of which is shown in fig. 1, and the steps include:
step 1: combining the satellite space geometrical distribution and the signal-to-noise ratio to construct SWGDOP;
step 2: extracting visible satellites from navigation messages received by a receiver, and iteratively searching satellite combinations under a given number by utilizing self-adaptive BFO-PSO;
step 3: and (3) selecting an optimized satellite combination capable of meeting SWGDOP requirements by combining the satellite combination searched in the step (2) and the contribution degree of the rest satellites.
The specific implementation steps of the invention are as follows:
step 1: combining the satellite space geometrical distribution and the signal-to-noise ratio, the SWGDOP is constructed.
Step 1.1: and constructing a station heart coordinate system by taking the position of the receiver as the origin of the coordinate source and the east, north and zenith directions which are mutually perpendicular. In this coordinate system, the receiver-to-satellite observation vector is:
(1)
(2)
wherein,,、/>respectively representing the position coordinates of the satellite and the receiver in the geocentric coordinate system, < >>Is a coordinate transformation matrix, < >>Is the longitude and latitude of the receiver.
Step 1.2: observing vectors through satellitesThe elevation angle of the satellite can be calculated>And azimuth->：
(3)
(4)
Step 1.3: the pseudo-range observation equation corrected by the receiver is:
(5)
wherein,,is an error corrected pseudorange measurement, +.>Is receiver clock skew,/->Is a pseudorange measurement error.
Step 1.4: for N satellite combinations, a geometric matrix is generated in the positioning calculation process by using a weighted least square method
(6)
Step 1.5: the signal-to-noise ratio is the ratio of the received carrier signal strength to the noise strength, and can better represent the satellite signal quality. The higher the signal-to-noise ratio, the better the carrier signal quality effect. The present invention thus introduces a signal-to-noise ratio architecture SWGDOP:
(7)
wherein,,the weight matrix Q is +.>Is a diagonal array of corresponding satellite signal to noise ratios. />Representing a matrix trace operation. The SWGDOP trade-off considers the satellite space geometry distribution and signal quality, and the smaller the value, the better the performance of the satellite combination, so the SWGDOP is taken as the fitness function of the subsequent satellite selection algorithm.
Step 2: the visible satellites are extracted from the navigation messages received by the receiver, and the combination of satellites at a given number is searched iteratively using adaptive BFO-PSO, the flow chart of which is shown in FIG. 2.
Step 2.1: and (3) extracting satellite parameters from the received navigation messages, calculating the elevation angles of all satellites according to a formula (3), deleting satellites with the elevation angles lower than 5 degrees, and taking the rest satellites as visible satellites. The atmospheric delay correction errors and multipath effects of low elevation satellite signals can be significant, so it is generally believed that the large measurement and positioning errors that low elevation satellites bring about for the benefit of improving DOP values should be filtered out in advance.
Step 2.2: assume that a total of M satellites are extracted, numbered sequentially from 1 to M. Presetting N satellite combinations selected from the N satellite combinations for positioning calculation, wherein the N satellite combinations existCombinations were made, each as one particle, and L particles were randomly selected as the initial population.
Step 2.3: initializing particle swarm parameters, setting BFO particle trend operation timesTrend to step size C, migration probability +.>PSO inertial weight boundary +.>、/>Speed boundary->、/>Learning factor->、/>Maximum iteration number Maxgen, initial iteration number +.>。
Step 2.4: order the=[/>]Representing the position of the ith particle in the population after j iterations, wherein the element is a satellite number; let->=[/>]Representing the speed of the ith particle after j iterations, wherein the element represents the variation of satellite numbers; let->Representing individual extrema of the particle,/->Is the corresponding position; />Representing population global extremum @, @>Is the corresponding position; SWGDOP is selected as fitness function of star selection>。
Step 2.5: calculating the fitness of all particles of the initial population, and setting the current fitness and the current position of the particles as the individual extremum of the particlesCorresponding position->Searching for global extremum of initial population>Position->。
Step 2.6: for all particles in the population, the positions and the speeds of the particles are updated according to the following PSO algorithm formula:
(8)
(9)
wherein,,、/>a non-negative learning factor; />、/>Is subject to [0,1 ]]Uniformly distributed random numbers; />Is an inertial weight which has a significant influence on the search ability of PSO, greater +.>Can improve global search capability of PSO, and is smaller +.>The local search capability may be improved. The present invention thus adopts the form of nonlinear dynamic inertial weight +.>The algorithm can adaptively update the weight value:
(10)
wherein,,respectively, the minimum value and the average value of the fitness of the population particles.
Step 2.7: calculating the particle fitness, comparing the individual fitness with an individual extremum, and if the individual fitness is smaller, updating the individual extremum and the position of the particle; comparing the minimum fitness of the population with the global extremum, and updating the global extremum and the position if the minimum fitness of the population is reduced.
Step 2.8: if the global extremum in the step 2.7 is updated, entering the step 2.9 to execute trend operation; otherwise, step 2.13 is entered to execute the migration operation.
Step 2.9: initialization of。
Step 2.10: random inversion formula:
(11)
wherein the method comprises the steps ofIs a swimming step size->Is a unit vector of random direction.
Step 2.11: calculating the adaptability after overturning, if the global extremum is better, the particles move according to the overturning direction of the formula (11), otherwise, the particles do not move; let k=k+1 then.
Step 2.12: if it isAnd returning to the step 2.10, otherwise, entering the step 2.14.
Step 2.13: ordering all particle fitness, selectingThe migration operation is carried out by particles with larger personal adaptability, namely, the selected particles are added with probability +.>The random initialization is performed again. In addition->Representing a rounding down.
Step 2.14: increasing the number of iterationsIf (if)/>And returning to the step 2.6, otherwise, outputting the optimal solution, and ending the algorithm.
Step 3: and (3) selecting an optimized satellite combination capable of meeting SWGDOP requirements by combining the satellite combination searched in the step (2) and the contribution degree of the rest satellites.
Step 3.1: and constructing a satellite contribution degree operator.
Order theWherein->Indicating the addition of the row vector corresponding to the d-th satellite. Let->Then
(12)
Wherein the method comprises the steps ofIs a scalar, let->(13)，
Then(14)
As can be seen from the above, in case a given satellite combination is obtained in step 2, it is optional to letThe largest satellite increases the number of combined satellites, so that +.>As a contribution operator for the remaining satellites. Let's assume that one satellite is added from the remaining m satellites by +.>The calculation has the following advantages: 1. under the condition of selected satellite combination, satellites are added through a contribution degree operator, BFO-PSO iterative operation is not required to be carried out again, and calculation amount can be reduced; 2. if the medicine is directly selected according to the prior mode>To calculate satellite contribution, the user needs to do the corresponding +.>And performing matrix inversion operation m times. However, it can be seen from formula (13) that if +.>Only need to be->And the matrix inversion operation is carried out once, so that the calculation time can be further saved.
Step 3.2: gradually increasing the number of combined satellites by calculating the remaining satellite contribution degree operators until the SWGDOP of the selected satellite combination meets the requirements.
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (3)
1. The satellite selection method based on the self-adaptive BFO-PSO is characterized by comprising the following steps:
step 1: combining the satellite space geometric distribution and the signal-to-noise ratio to construct a signal-to-noise ratio weighted geometric precision factor;
step 2: extracting visible satellites from navigation messages received by a receiver, and iteratively searching satellite combinations under a given number by utilizing self-adaptive BFO-PSO;
step 3: selecting an optimized satellite combination which can meet the requirement of the signal-to-noise ratio weighted geometric precision factor by combining the satellite combination searched in the step 2 and the contribution degree of the rest satellites;
the specific process of the step 3 is as follows:
step 3.1: constructing a satellite contribution operator;
order theWherein D is d Represents adding row vector corresponding to the d-th satellite, let U=G T G is then
Wherein the method comprises the steps ofIs a scalar, let->
Then
As can be seen from the above, in step2, in the case of a given satellite combination, V can be chosen to be L，d The largest satellite increases the number of combined satellites, so that V can be calculated L，d As a contribution operator of the remaining satellites, it is assumed that one satellite is added from the remaining m satellites by V L，d Calculation is performed, however, as can be seen from equation (13), if V is selected L，d Then only need to pairThe matrix inversion operation is carried out once, so that the calculation time can be further saved;
step 3.2: gradually increasing the number of combined satellites by calculating the remaining satellite contribution degree operators until the SWGDOP of the selected satellite combination meets the requirements.
2. The method for selecting a satellite based on adaptive BFO-PSO according to claim 1, wherein the specific procedure in step 1 is as follows:
step 1.1: the position of the receiver is used as the origin of a coordinate source, and a station heart coordinate system is constructed by the east direction, the north direction and the zenith direction which are perpendicular to each other, and under the coordinate system, the observation vector from the receiver to the satellite is as follows:
wherein [ x ] s y s z s ] T 、[x r y r z r ] T Respectively representing the position coordinates of the satellite and the receiver under a geocentric coordinate system, wherein Rot is a coordinate transformation matrix, and lambda and phi are the longitude and latitude of the receiver;
step 1.2: by satellite observation vector [ e ] SR n SR u SR ] T The elevation angle theta and the azimuth angle theta of the satellite can be calculated
Step 1.3: the pseudo-range observation equation corrected by the receiver is:
wherein ρ is c Is the error corrected pseudorange measurement, δt r Is the clock difference, epsilon, of the receiver ρ Is a pseudorange measurement error;
step 1.4: for N satellite combinations, a geometric matrix is generated in the positioning calculation process by using a weighted least square method
Step 1.5: introducing a signal-to-noise ratio construction signal-to-noise ratio weighted geometric precision factor SWGDOP:
wherein g=qh, the weight matrix Q is an n×n diagonal matrix, the diagonal elements thereof are corresponding satellite signal-to-noise ratios, tr (·) represents matrix trace operation, and SWGDOP is used as a fitness function of a subsequent satellite selection algorithm.
3. The method for selecting a satellite based on adaptive BFO-PSO according to claim 2, wherein the specific procedure of step 2 is as follows:
step 2.1: extracting satellite parameters from the received navigation message, calculating the elevation angles of all satellites according to the formula (3), deleting satellites with the elevation angles lower than 5 degrees, and taking the rest satellites as visible satellites, wherein the atmospheric delay correction errors and multipath effects of low-elevation satellite signals can be serious, so that the large measurement and positioning errors caused by the low-elevation satellites on the premise of not improving the DOP value are generally considered to be corresponding to the advanced filtering;
step 2.2: assuming that M visible satellites are extracted in total, numbering the visible satellites from 1 to M in turn, presetting N satellite combinations selected from the visible satellites for positioning calculation, then the visible satellites existSeed combinations, each combination is taken as a particle, and L particles are randomly selected as an initial population;
step 2.3: initializing particle swarm parameters, setting BFO particle trend operation times N c Trend to the moving step length C and the migration probability p ed PSO inertial weight boundary omega max 、ω min Velocity boundary v max 、v min Learning factor c 1 、c 2 Maximum iteration number Maxgen, initial iteration number j=1;
step 2.4: let x ij ＝[x i，j，1 ，x i，j，2 ，...，x i，j，N ]Representing the position of the ith particle in the population after j iterations, wherein the element is a satellite number; let v i，j ＝[v i，j，1 ，v i，j，2 ，...，v i，j，N ]Representing the speed of the ith particle after j iterations, wherein the element represents the variation of satellite numbers; let Fp i，j Representing individual extrema, bp, of the particle i，j Is the corresponding position; fg j Representing global extremum of population, bg j Is the corresponding position; the SWGDOP is selected as the fitness function Fit of the star selection i ；
Step 2.5: calculating the fitness of all particles of the initial population, and setting the current fitness and the current position of the particles as the individual extremum Fp of the particles i，0 Corresponding position Bp i，0 Searching global extremum Fg of initial population 0 Position Bg 0 ；
Step 2.6: for all particles in the population, the positions and the speeds of the particles are updated according to the following PSO algorithm formula:
v i，j (t+1)＝ωv i，j (t)+c 1 r 1 (Bp i，j -x i，j (t))+c 2 r 2 (Bg j -x i，j (t)) (8)
x i，j (t+1)＝x i，j (t)+v i，j (t+1) (9)
wherein c 1 、c 2 A non-negative learning factor; r is (r) 1 、r 2 Is subject to [0,1 ]]Uniformly distributed random numbers; omega is an inertia weight, has obvious influence on the searching capability of PSO, and a larger omega can improve the global searching capability of PSO, and a smaller omega can improve the local searching capability; the nonlinear dynamic inertia weight omega in the following form is adopted, so that the algorithm can adaptively update the weight:
wherein, fit min 、Fit avg Respectively the minimum value and the average value of the fitness of population particles;
step 2.7: calculating the particle fitness, comparing the individual fitness with an individual extremum, and if the individual fitness is smaller, updating the individual extremum and the position of the particle; comparing the minimum fitness of the population with the global extremum, and updating the global extremum and the position if the minimum fitness of the population is reduced;
step 2.8: if the global extremum in the step 2.7 is updated, entering the step 2.9 to execute trend operation; otherwise, step 2.13 is entered to execute the migration operation;
step 2.9: initializing k=1;
step 2.10: random inversion formula:
x ij (t+1)＝x ij (t)+C ij R ij (11)
wherein C is ij Is the running step length, R ij Is a unit vector in a random direction;
step 2.11: calculating the adaptability after overturning, if the global extremum is better, the particles move according to the overturning direction of the formula (11), otherwise, the particles do not move; let k=k+1;
step 2.12: if k is less than or equal to N ed Returning to the step 2.10, otherwise, entering the step 2.14;
step 2.13: ordering all particle fitness, selectingThe migration operation is carried out by the particles with larger adaptability, namely, the selected particles are represented by probability p ed Re-initializing the random, additionally->Representing a downward rounding;
step 2.14: and (3) increasing the iteration times j=j+1, returning to the step (2.6) if j is less than or equal to Maxgen, otherwise, outputting the optimal solution, and ending the algorithm.
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