CN110146907A - A kind of satellite navigation locating method based on elimination residual phase and improvement TLBO algorithm - Google Patents

A kind of satellite navigation locating method based on elimination residual phase and improvement TLBO algorithm Download PDF

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CN110146907A
CN110146907A CN201811555935.0A CN201811555935A CN110146907A CN 110146907 A CN110146907 A CN 110146907A CN 201811555935 A CN201811555935 A CN 201811555935A CN 110146907 A CN110146907 A CN 110146907A
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程兰
张净
任密蜂
续欣莹
阎高伟
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Taiyuan University of Technology
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
    • G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/42—Determining position
    • G01S19/43—Determining position using carrier phase measurements, e.g. kinematic positioning; using long or short baseline interferometry
    • G01S19/44—Carrier phase ambiguity resolution; Floating ambiguity; LAMBDA [Least-squares AMBiguity Decorrelation Adjustment] method
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Abstract

The invention proposes a kind of based on eliminating residual phase and improving the satellite navigation locating method of TLBO algorithm, belongs to satellite navigation and positioning field, is mainly used for solving the problems, such as residual phase and multi-path jamming in traditional location algorithm.The algorithm is broadly divided into two steps: the first step eliminates the residual phase problem of traditional location algorithm by seeking residual carrier phase and integer ambiguity, has acquired user location.The second step problem big to location precision in view of multi-path jamming, multi-path jamming is introduced into pseudorange model, it is optimized by improved TLBO algorithm and obtains user position update amount, then the position that the first step is found out is updated and obtains end user position.The present invention introduces improved TLBO algorithm on the basis of eliminating residual phase and changing traditional location algorithm to inhibit multi-path jamming, not only reduces computation complexity, and effectively improve positioning accuracy.

Description

Satellite navigation positioning method based on residual phase elimination and TLBO algorithm improvement
Technical Field
The invention belongs to the field of satellite navigation positioning, and particularly relates to a satellite navigation positioning algorithm based on residual phase elimination and TLBO algorithm improvement.
Background
The satellite navigation positioning system is concerned with multiple aspects of politics, economy, military and the like of a country, and is widely applied to the fields of geodetic survey, engineering survey, water conservancy, electric power, traffic, resource exploration, navigation and the like, so that it is extremely important for any country to have an autonomous positioning system and technology. The Global Positioning System (GPS) is developed from the 70 th century in the united states, lasts 20 years, costs $ 200 billion, is built comprehensively in 1994, has the capability of performing omnibearing real-time three-dimensional navigation and positioning in the sea, on the land and in the air, is a new generation satellite navigation and positioning system, and has the remarkable characteristics of high benefit, high precision, automation, globality, all weather and the like. Similar to the GPS in the united states, russia and the european union have also successively introduced their own satellite navigation and positioning systems, i.e., the GLONASS and Galileo (Galileo) systems, intended to break the united states' monopoly for satellite navigation. Although China starts late in the aspect of satellite navigation technology, the Beidou satellite navigation system independently developed in China is very vigorous in development. With the application and development of the modern global navigation positioning system and the improvement of the living standard of people, the requirement of people on the positioning precision is higher and higher, so that the research on the method for improving the positioning precision has important significance for promoting the popularization and the application of the Beidou navigation system.
The positioning accuracy of the receiver is affected by various error sources, wherein multipath interference is one of the main error sources for high-accuracy positioning. Multipath interference is caused by reflections of direct signals by obstacles. The existence of multipath interference makes the receiver unable to distinguish the direct signal from the reflected signal, and directly tracks the combined signal formed by the two signals, resulting in deviation of signal tracking, and further causing positioning error, which is called multipath error. Aiming at the multipath error suppression problem, Chen et al (Chen G Y, Gan M, Chen CL P, et al. A Two-Stage Estimation Based on Variable project method for GPS Positioning [ J ]. IEEE Transactions on instruments & Measurement, PP (99):1-8.) proposes a Two-Stage Estimation Algorithm, namely, firstly, a relatively accurate user position is positioned by using a traditional Positioning method, secondly, multipath interference is added into a pseudo-range model as random interference and is converted into a Separable Nonlinear Least Square (SNLS) problem, and then, the multipath interference is optimized and solved by using a Variable projection method which is a classical method for solving the SNLS problem, and finally, the position of the user is positioned.
Although the two-stage algorithm improves the positioning accuracy to a certain extent, the traditional positioning algorithm adopted in the first step for calculating the user position is relatively inaccurate, and then the multipath inhibition in the second step is influenced, so that the user position accuracy calculated in the first step is influenced to a certain extent; the variable projection method is used when multipath mitigation is performed in the second step, however, in practice, the unconstrained SNLS problem hardly exists. If the linear part of the variable in the SNLS problem has boundary constraints, the variable projection is invalid. Aiming at the problem, the invention provides a GPS positioning algorithm based on eliminating residual phase and improving TLBO (residual-learning based optimization) algorithm for GPS positioning, which not only improves the traditional positioning algorithm, but also effectively inhibits the most main error (namely multipath interference) influencing the positioning accuracy, thereby greatly improving the positioning accuracy.
Disclosure of Invention
The invention is realized by adopting the following technical scheme: a satellite navigation positioning method based on eliminating residual phase and improving TLBO algorithm, finish positioning in two steps, the first step is to position a user position through the positioning method of eliminating residual phase under the condition of not considering multipath; secondly, adding multipath interference serving as random interference into a pseudo-range model, converting the pseudo-range model into a nonlinear least square problem, optimally solving the least square problem by a satellite navigation positioning method based on an improved TLBO algorithm to obtain an update amount, and updating the user position obtained in the first step to obtain a final user position;
the first step comprises the following specific steps:
(1) after the carrier frequency is obtained by the capturing and tracking, the residual pseudo code phase is obtained, the original signal frequency is reproduced by the carrier frequency obtained by the capturing and tracking, and the residual carrier phase is obtained through the reproduction of the original signal;
(2) calculating the integer ambiguity through the residual pseudo code phase and the residual carrier phase, namely subtracting the residual carrier phase from the residual pseudo code phase;
(3) calculating a measurement pseudo range by using the residual carrier phase and the integer ambiguity;
(4) solving the user position by adopting a least square method through pseudo range and satellite position
The second step comprises the following specific steps:
(1) determining fitness functionWherein,andposition determined for the first stepThe corresponding values are obtained by the first step of the least squares solution, δ x (δ x1, δ x2, δ x3) represents the update amount, c Δ t represents the clock skew, and f (v) ═ v1，v2，…，vm]TRepresenting multipath error, m representing the number of satellites, determining the termination condition e-5The maximum iteration number maxk is 100, and the number of population individuals NP80, the initial iteration number k is 0;
(2) randomly generating an initial population
(3) Calculating the fitness value g (x) of each individual in the initial populationi) Wherein i ═ 1, 2.., NP；
(4) Judging whether the termination condition is reached, wherein the judgment method of the termination condition is g (x)i) If yes, terminating the evolution, and outputting the obtained optimal individual as an optimal solution; if not, continuing;
(5) forming a new individual according to a teaching strategy, comparing fitness function values of the new individual and the old individual, and selecting an individual with a small fitness value;
(6) introducing a cross strategy in a learning stage to obtain a new individual, comparing fitness values of the new individual and an old individual, and selecting an individual with a small fitness value;
(7) the iteration number k is k +1, and (4) is reached.
Further, the frequency of the reproduced original signal in (1) in the first step is obtained by: f. ofim＝L1-fc+ f, wherein finRepresenting the frequency of the original signal without down-conversion, L1 representing the frequency of the carrier L1, fcRepresenting the theoretical intermediate frequency and f the tracked frequency.
Further, the residual pseudo code phase, the residual carrier phase and the integer ambiguity in (2) of the first step are obtained by the following steps:
residual pseudo code phase:
residual carrier phase:
integer ambiguity:
td represents the chip corresponding to the last sampling point of the current tracking period, fcaRepresenting the frequency of the C/A code, f1Representing the sampling frequency, s representing the number of samples in a period, f1Representing the sampling frequency, N1Representing the number of carrier cycles in a sample period.
Further, in the step (3) of the first step, the measured pseudorange is obtained by using the residual carrier phase and the integer ambiguity: determining propagation time Wherein s isaThe absolute sampling number of the current positioning time point is represented, and the propagation time is multiplied by the speed (namely the light speed) to obtain the corrected measured pseudo range(i denotes satellite, i ═ 1, …, m).
Further, the random generation of the initial population in (2) of the second step is obtained by: i.e. randomly generating an initial populationEach individual is composed of m + 4-dimensional vectors, i.e., xi＝(xi，1，xi，2，...，xi，m+4) Where x isi，jIs composed ofAndthe smaller value of the medium fitness function;
wherein
i＝1，2，...，NP
j＝1，2，...，m+4
Lj_minAnd Lj_maxRepresenting the minimum and maximum values of the j-dimension variable.
Further, the teaching strategy in step (5) in the second step is completed by introducing a self-adaptive teaching factor, and the specific steps are as follows: obtaining a new individual of the ith individual according to a teaching strategy
xbestAndrespectively representing the average value of the optimal individual and each dimension variable in the current population; t isFRepresenting teaching factors, where adaptive teaching factors are used:and comparing the fitness function values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
Further, in the second step (6), the learning stage is completed by introducing a cross strategy, and the specific steps are as follows: for the current ith individual, randomly selecting an individual p, wherein p is not equal to i, and obtaining a new individual of the ith individual by adopting a learning strategy
After learning is completed, a new individual is obtained by adopting a crossover strategy, namely
Wherein r is1，r2，r3R4 is a random number generated from (0, 1), DjIs from [1, m +4 ]]Is generated as a random integer, j ∈ [1, m +4 ]](ii) a And comparing the fitness function values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
The invention has the following beneficial effects:
1. the invention corrects by eliminating the residual carrier phase, and solves the problem of residual phase in the positioning process.
2. The invention restrains the multipath interference by correcting the pseudo range, and has the advantages of low cost and easy algorithm transplantation.
3. The invention converts the multipath estimation problem into the optimization problem, realizes the global optimization of the problem by adopting the improved TLBO algorithm and reduces the calculation complexity.
4. The invention adopts an improved TLBO algorithm, namely, introduces a self-adaptive teaching factor and a cross strategy, and effectively improves the performance of the algorithm.
5. On the basis of improving the traditional positioning algorithm, the improved TLBO algorithm is adopted to carry out multipath inhibition, and the positioning accuracy is effectively improved.
Drawings
Fig. 1 is a general flowchart of a satellite navigation positioning method according to the present invention.
Fig. 2 is a graph of the relationship between residual phase and sampling points.
Fig. 3 is a flow chart of the improved TLBO algorithm of the present invention.
Detailed Description
The traditional positioning algorithm is to sample the intermediate frequency signal, and then capture, track and navigate to settle and finally position the user. When signal synchronization is carried out, the first sampling point in each period is used as the starting point of the period where the sampling point is located, and the positioning accuracy is extremely poor, so the method is provided for solving the problem, and meanwhile, in order to inhibit the influence of multipath interference on the positioning accuracy, the satellite navigation positioning method based on residual phase elimination and TLBO algorithm improvement is provided. The method mainly comprises two steps, wherein the first step is to obtain a more accurate user position by a method for eliminating residual phases on the basis of the traditional positioning algorithm, and the second step is to perform multi-path interference suppression based on the improved TLBO algorithm to finally position the user position. The present invention will be described with reference to a GPS system as an example.
When the user position is found accurately in the first step, because the first sampling point in each period is not the starting point of the period where the first sampling point is located in the signal synchronization process, the distance between the first sampling point in the period where the first sampling point is located and the starting point of the period is called a residual phase. The pseudo code is affected seriously by noise in the signal propagation process, the tracking precision is not high, the carrier frequency of the GPS L1 is 1540 times of the frequency of the C/A code, and the influence of an error source is far less than the frequency of the C/A code, so that the carrier is adopted to replace the pseudo code for positioning, namely the residual carrier phase is calculated in a mode of reproducing the original signal frequency in the tracking stage. However, if the carrier signal is shifted back and forth by 1 cycle, the value is identical to the effect of signal alignment, which means that there is a whole-cycle phase ambiguity in the residual carrier phase, i.e. the true residual carrier phase may be the residual carrier phase ± N carrier cycles at that time. And the residual pseudo code phase can be obtained in the tracking stage, and the residual pseudo code phase has no influence of the integer ambiguity, so that the approximate integer ambiguity can be obtained by subtracting the residual carrier phase from the residual pseudo code phase. The residual carrier phase plus the integer ambiguity is then the true residual phase.
In short, the accuracy of the measured pseudorange is improved by eliminating the residual phase, and finally, the relatively accurate user position can be obtained by measuring two values of the pseudorange and the satellite position by adopting a least square method
And secondly, on the basis of solving the relatively accurate user position after the first step is finished, adding multipath interference serving as random interference into a pseudo-range model, converting the random interference into an optimization problem, solving an optimal solution by adopting an improved TLBO algorithm, and updating the user position through the optimal solution to obtain the final user position. In particular to the optimization problem
The solution is performed to obtain the update quantity δ x (δ x1, δ x2, δ x3), the clock deviation c Δ t and the multipath error f (v). Then, the position obtained in the first step is determinedUpdating to obtain the final user position
Wherein,andis a positionThe corresponding value can be obtained by the least square method in the first step
f(v)＝[v1，v2，…，vm]T
f (v) the multipath error value corresponding to the satellite used for positioning. Wherein, the SNR of the signal to noise ratio of the ith satelliteiIs greater than or equal to 42dB, vi0, otherwise vi∈Ωi。
A GPS positioning method based on an improved TLBO algorithm mainly comprises the following steps:
1. determining a fitness function g (x), a termination condition epsilon, a maximum iteration number maxk and a population individual number NPInitial iteration number k.
2. Random generation of initial population using reverse learning concept
3. Fitness values for each individual in the initial population are calculated.
4. Judging whether the end condition g (x) is reachedi) Epsilon is less than or equal to epsilon or the iteration times k reach the maximum, namely k is more than maxk. If so, terminating iteration and outputting the obtained optimal individual as an optimal solution; if not, continuing.
5. A teaching stage: calculating the average value of each one-dimensional variable, the current optimal individual and the self-adaptive teaching factor, obtaining a new individual according to a teaching strategy, comparing the fitness values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
6. A learning stage: and after the teaching is completed, obtaining a new individual according to the learning strategy and the cross strategy, comparing the fitness values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
7. And (4) turning to the step 4 when the iteration number k is equal to k + 1.
The following is a detailed description of specific embodiments of the present invention. A GPS positioning algorithm based on elimination of residual phase and improvement of TLBO algorithm, as shown in fig. 1, includes the following two steps:
step 1: based on a positioning algorithm for eliminating residual phases, the position of the first sampling point in each period is not at the starting point of the period, the error is called as the residual phase, the error is eliminated in the invention by eliminating the residual carrier phase, and the specific steps are as follows:
(1) the frequency at which the receiver captures and tracks the intermediate frequency signal samples is the frequency after down-conversion, where the tracked frequency is used to reproduce the original signal frequency, i.e., fin＝L1- fc+ f, wherein finRepresenting the frequency of the original signal without down-conversion, L1 representing the frequency of the carrier L1, fcRepresenting the theoretical intermediate frequency and f the tracked frequency.
(2) By passingAnd solving the residual code phase, wherein td represents the chip corresponding to the last sampling point of the current tracking period, fcaRepresenting the frequency of the C/A code, f1Representing the sampling frequency.
(3) By passingDetermining the residual carrier phase, rem2Representing residual carrier phase, s representing the number of samples in a period, f1Representing the sampling frequency, N1Representing the number of carrier cycles in a sample period.
(4) Calculating integer ambiguity
(5) Determining propagation timeWherein s isaThe absolute sampling number of the current positioning time point is represented, and the measured pseudo range can be obtained by multiplying the propagation time by the speed (namely the light speed)(i denotes satellite, i ═ 1, …, m).
(6) Last passing pseudorangeAnd satellite position (x)i，yi，zi) (calculating and solving through tracking result) and adopting least square method to solve relatively accurate positioning result
Step 2: a GPS positioning method based on an improved TLBO algorithm, as shown in fig. 3, includes the following specific steps:
(1) determining fitness functionEnd value e-5Maximum iteration algebra maxk is 100, population number NP80, the initial iteration number k is 0.
(2) Random generation of initial population using reverse learning conceptEach individual is composed of m + 4-dimensional vectors, i.e., xi＝(xi，1，xi，2，...，xi，m+4) Wherein (a)xi，1，xi，2，...，xi，m+4) Respectively corresponding to (delta x1, delta x2, delta x3, delta x4, v1，v2，...，vm). Where x isi，jIs composed ofAndthe smaller fitness function value.
Wherein
i＝1，2，...，NP
j＝1，2，...，m+4
LjminAnd Lj_maxRepresenting the minimum and maximum values of the j-dimension variable.
(3) Calculating the fitness value g (x) of each individual in the initial populationi)。
(4) Judging whether the end condition g (x) is reachedi) Epsilon is less than or equal to epsilon or the iteration times k reach the maximum, namely k is more than maxk. If so, terminating iteration and outputting the obtained optimal individual as an optimal solution; if not, continuing.
(5) A teaching stage: obtaining a new individual of the ith individual according to a teaching strategy
WhereinxbestAndrespectively representing the average value of the optimal individual and each dimension variable in the current population; t isFRepresenting teaching factors, where adaptive teaching factors are used:
comparing the fitness function values of the new individual and the old individual, and selecting the individual with smaller fitness value
(6) A learning stage: for the current ith individual, randomly selecting an individual p, wherein p is not equal to i, and obtaining a new individual of the ith individual by adopting a learning strategy
After learning is completed, a new individual is obtained by adopting a crossover strategy, namely
Wherein r is1，r2，r3R4 is a random number generated from (0, 1), DjIs from [1, m +4 ]]Is generated as a random integer, j ∈ [1, m +4 ]]。
Comparing the fitness function values of the new individual and the old individual, and selecting the individual with smaller fitness value
(7) And (4) turning to the step (4) when the iteration number k is equal to k + 1.
The above description is only an embodiment of the present invention, but the structural features of the present invention are not limited thereto, and any changes or modifications within the scope of the present invention by those skilled in the art are covered by the present invention.

Claims (7)

1. A satellite navigation positioning method based on residual phase elimination and TLBO algorithm improvement is characterized in that: positioning is completed in two steps, wherein in the first step, a user position is positioned by a positioning method for eliminating residual phases under the condition of not considering multipath; secondly, adding multipath interference serving as random interference into a pseudo-range model, converting the pseudo-range model into a nonlinear least square problem, optimally solving the least square problem by a satellite navigation positioning method based on an improved TLBO algorithm to obtain an update amount, and updating the user position obtained in the first step to obtain a final user position;
the first step comprises the following specific steps:
(1) after the carrier frequency is obtained by the capturing and tracking, the residual pseudo code phase is obtained, the original signal frequency is reproduced by the carrier frequency obtained by the capturing and tracking, and the residual carrier phase is obtained through the reproduction of the original signal;
(2) calculating the integer ambiguity through the residual pseudo code phase and the residual carrier phase, namely subtracting the residual carrier phase from the residual pseudo code phase;
(3) calculating a measurement pseudo range by using the residual carrier phase and the integer ambiguity;
(4) solving the user position by adopting a least square method through pseudo range and satellite position
The second step comprises the following specific steps:
(1) determining fitness functionWherein,andposition determined for the first stepThe corresponding values are obtained by the first step of the least squares solution, δ x (δ x1, δ x2, δ x3) represents the update amount, c Δ t represents the clock skew, and f (v) ═ v1，v2，…，vm]TRepresenting multipath error, m representing the number of satellites, determining the termination condition e-5The maximum iteration number maxk is 100, and the number of population individuals NP80, the initial iteration number k is 0;
(2) randomly generating an initial population
(3) Calculating the fitness value g (x) of each individual in the initial populationi) Wherein i ═ 1, 2.., NP；
(4) Judging whether the termination condition is reached, wherein the judgment method of the termination condition is g (x)i) If yes, terminating the evolution, and outputting the obtained optimal individual as an optimal solution; if not, continuing;
(5) forming a new individual according to a teaching strategy, comparing fitness function values of the new individual and the old individual, and selecting an individual with a small fitness value;
(6) introducing a cross strategy in a learning stage to obtain a new individual, comparing fitness values of the new individual and an old individual, and selecting an individual with a small fitness value;
(7) the iteration number k is k +1, and (4) is reached.
2. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the original signal frequency reproduced in (1) in the first step is obtained by the following steps: f. ofin＝L1-fc+ f, wherein finRepresenting the frequency of the original signal without down-conversion, L1 representing the frequency of the carrier L1, fcRepresenting the theoretical intermediate frequency and f the tracked frequency.
3. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the residual pseudo code phase, residual carrier phase and integer ambiguity in (2) of the first step are obtained by the following steps:
residual pseudo code phase:
residual carrier phase:
integer ambiguity:
td represents the chip corresponding to the last sampling point of the current tracking period, fcaRepresenting the frequency of the C/A code, f1Representing the sampling frequency, s representing the number of samples in a period, f1Representing the sampling frequency, N1Representing the number of carrier cycles in a sample period.
4. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the step (3) of obtaining the measured pseudorange from the residual carrier phase and the integer ambiguity is performed by: determining propagation timeWherein s isaThe absolute sampling number of the current positioning time point is represented, and the propagation time is multiplied by the speed (namely the light speed) to obtain the corrected measured pseudo range(i denotes satellite, i ═ 1, …, m).
5. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the random generation of the initial population in the second step (2) is obtained by the following steps: i.e. randomly generating an initial populationEach individual is composed of m + 4-dimensional vectors, i.e., xi＝(xi，1，xi，2，...，xi，m+4) Where x isi，jIs composed ofAndthe smaller value of the medium fitness function;
wherein
Lj_minAnd Lj_maxRepresenting the minimum and maximum values of the j-dimension variable.
6. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the teaching strategy in the second step (5) is implemented by introducing adaptive teaching factors, comprising the following steps: obtaining a new individual of the ith individual according to a teaching strategy
xbestAndrespectively representing the average value of the optimal individual and each dimension variable in the current population; t isFRepresenting teaching factors, where adaptive teaching factors are used:and comparing the fitness function values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
7. The method for satellite navigation and positioning based on elimination of residual phase and improvement of TLBO algorithm as claimed in claim 1, wherein the learning phase in (6) of the second step is performed by introducing a cross strategy, comprising the following specific steps: for the current ith individual, randomly selecting an individual p, wherein p is not equal to i, and obtaining a new individual of the ith individual by adopting a learning strategy
After learning is completed, a new individual is obtained by adopting a crossover strategy, namely
Wherein r is1，r2，r3，r4Is a random number, D, generated from (0, 1)jIs from {1, m +4]Is generated as a random integer, j ∈ [1, m +4 ]](ii) a And comparing the fitness function values of the new individual and the old individual, and selecting the individual with a smaller fitness value.
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