CN108168556B - Ultra-wideband positioning method for tunneling support bracket integrating particle swarm optimization and Taylor series expansion - Google Patents
Ultra-wideband positioning method for tunneling support bracket integrating particle swarm optimization and Taylor series expansion Download PDFInfo
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Abstract
The invention discloses a tunneling support ultra-wideband positioning method integrating particle swarm optimization and Taylor-grade expansion, wherein a positioning model of the tunneling support ultra-wideband positioning method is constructed according to the ultra-wideband base station layout of the tunneling support; ultra-wideband positioning is converted into a class of optimization problems, and a particle swarm optimization algorithm is adopted to perform global optimization to obtain positioning point coordinates with the minimum positioning error; and then, taking the optimal positioning point obtained by the particle swarm optimization algorithm as an initial value, adopting Taylor series expansion, and realizing local positioning coordinate optimization through iteration to obtain the optimal value of the positioning point coordinate. The positioning method is high in precision and easy to realize, has good robustness for ultra-wideband positioning in noise environments such as tunneling support, and has a remarkable application prospect.
Description
Technical Field
The invention belongs to the technical field of indoor positioning in special environment, and particularly relates to a tunneling support bracket ultra-wideband positioning method integrating particle swarm optimization and Taylor-series expansion.
Background
Research on unmanned or less-man mining equipment is an important guarantee for safe and efficient mining of deep dangerous coal seams. The tunneling process is an important link of coal mining, and the safety and the efficiency of the coal mining process are directly influenced by the stability and the reliability of surrounding rock support. After the tunneling operation of each depth is completed, certain empty roof exists in surrounding rocks of the head-on roadway. In order to avoid surrounding rock separation, a hydraulic advanced support bracket is needed to realize temporary support of the empty top of the roadway. Meanwhile, the absolute position of the advance support bracket in the roadway has a direct influence on the determination of the position of the anchor rod in the subsequent anchoring operation. Therefore, the positioning of the excavation support bracket is of great importance.
Considering that the ultra-wideband has the advantages of small volume, low power consumption, high multi-path resolution, strong noise and interference resistance, high positioning accuracy and the like, the ultra-wideband is adopted to realize the position calibration of the forepoling support. However, in the ultra-wideband positioning method, the solution accuracy of the positioning equation set is often low. Therefore, the patent provides a novel ultra-wideband positioning method for the tunneling support bracket, which integrates a particle swarm optimization algorithm and Taylor series expansion.
At present, a positioning method for mining equipment in a tunneling process mainly focuses on position calibration of a tunneling machine, and positioning research on a forepoling support is lacked. The document (childhood, Douchai, Ligaojun, etc.. development of a multi-sensor heading machine positioning system [ J ] coal mine machinery, 2013,34(6): 146-. The principle and characteristics of the automatic guiding and positioning technology of the heading machine based on a total station, a gyroscope, an electronic compass, a laser guide instrument, visual detection and the like are analyzed and compared in the literature (field, automatic guiding and positioning technology exploration [ J ]. industrial and mining automation 2010,8: 26-29). In documents (Zhouying, Donghai, Dou Yu Xin, development of laser and optoelectronics [ J ], 2017,54(4): 180) and 186), the position and the posture of the development machine are detected by adopting the double-laser target image recognition. The patent (Zhangxu. positioning control system of coal mine cantilever type development machine, 201320714937.6[ P ].2013) adopts laser range finder, laser pointer, etc. to realize the positioning of development machine. The patent (Tianyuan, Zhankuan, Yangxongjie, etc. a four-point type heading machine automatic positioning and orientation method, 201611235639.3[ P ] 2016) provides a heading machine automatic positioning and orientation method based on machine vision technology. The patent (Wangkandong, Chenbing, Sunwang, etc. heading machine positioning system and positioning method based on three laser marking point images, 201610614160.4[ P ] 2016) realizes the positioning of heading machine by utilizing a wireless camera, a wireless base station, a computer, a triangular laser marker, etc. The patent (Stone, a heading machine positioning system, 201510592517.9[ P ].2015) detects the spatial position relationship of the heading machine relative to the roadway by a high-frequency pulse device. The patent (Tongming, Tongbiang, Li Chong, etc. heading machine wireless navigation positioning system and method, 201310047061.9[ P ] 2013) utilizes a wireless node device to realize navigation positioning in the heading process. The patent (Tongming, Tongbai, Xunan, etc. heading machine laser guiding positioning and orienting device and method, 201010278942.8[ P ] 2010) realizes the positioning of the heading machine by using a laser guiding method. However, the underground environment is complex, the dust concentration is large, and the transmission of infrared rays, wireless sensor network signals, laser and visible light is not facilitated. The document (Wu' 2815656, Jia Shanghai, Huawei, and the like, an autonomous measurement method of the boom-type roadheader position and posture based on the spatial convergence measurement technology [ J ] journal of coal science 2015,40(11): 2596-.
An ultra-wideband signal is a wideband, non-sinusoidal, pulsed radio signal. The distance is calculated by detecting the two-way flight Time (TOA for short) of the ultra-wideband signal between the ranging module and the base station, so that the method has high ranging precision, and the influence of complex environment and working condition of the fully mechanized excavation working face on the positioning precision of the hydraulic support can be effectively reduced.
The ultra-wideband positioning equation system based on the TOA is a nonlinear equation system containing noise, and can be solved by using a traditional analysis method. Firstly, ultra-wideband positioning is realized by adopting a Taylor correlation method. The method mainly comprises the following steps: direct-Taylor composite positioning algorithm (Jiang Wemei, Wang Mei), UWB direct-Taylor composite positioning algorithm based on TOA [ J ] academic paper of Guilin electronics industry institute, 2006,26(1):1-5), LSE-Taylor combined positioning algorithm (Liulikun, Xuyubin, LSE-Taylor combined positioning algorithm based on signal arrival time research [ C ] Chinese information technology and application academic forum, 2008,35(4):261 + 262), cooperative positioning combining APIT and Taylor (simulation and analysis of ginger sensitivity. ultra wide band positioning algorithm [ J ] electronic technology, 2008,21(11):56-58), centroid-Taylor mixed positioning algorithm (Zhang-Repean, Zhang-Zhongjuan, Lloy-just, UWB indoor positioning algorithm research [ J ] reassurance, post, 23(6): 721, 717, A mixed positioning algorithm (Wang Lei, Lipeng, Jia Zong) based on the total centroid-Taylor comprises a UWB indoor positioning algorithm [ J ] based on the total centroid-Taylor, a sensor and a micro system, 2017,36(6): 146-. The positioning calculation method based on Taylor has high solving precision and high convergence speed, but has strong dependence on the initial value. And secondly, ultra-wideband positioning is realized by adopting a least square method. The literature (Koelreuteria, Wangping, a weighted TOA underground space UWB indoor positioning algorithm [ J ]. Industrial control computer, 2014,27(1):73-75) proposes an ultra wide band positioning algorithm suitable for underground space indoor environments. The literature (Shaowei Yang, Bo Wang. reactive Based Weighted Square Algorithm for Bluetooth/UWB Indor Localization System [ C ]. proceedingsofthe 36th Chinese Control Conference,2017: 5959-. The influence of a geometric precision factor on a positioning error is analyzed in a literature (Feng G, Shen C, Long C, et al. GDOP index in UWB index positioning system experiment [ J ]. Sensors.2015:1-4.) based on an ultra-wideband positioning method of a least square method. Although the method is simple, the method needs inverse matrix calculation and has limited positioning precision. In addition to the two major types of localization methods described above, the literature (Jie D, Cui X R, Zhang H, et al. A Ultra-Wireless location Algorithm Based on Neural Network [ C ]. IEEE International conference Wireless Communications Networking and Mobile computing.2010:1-4) utilizes a reverse Neural Network Algorithm for localization. The document (Yan Fangchi, Maoqing. an ultra wide band indoor positioning algorithm [ J ] based on Kalman filtering, a sensor and a microsystem, 2017,36(10): 137-. In consideration of the defects of the existing resolving method of the positioning equation set, the ultra-wideband positioning problem is converted into a class of optimization problems, the positioning point with the minimum positioning error is obtained through global optimization by utilizing the parallel solving capability of the particle swarm optimization algorithm and is used as an initial value of Taylor series expansion, and then the optimal positioning coordinate is obtained through iteration. The method reduces the positioning error by improving the initial value positioning precision of the Taylor positioning method.
Disclosure of Invention
The purpose of the invention is as follows: in the process of tunneling a coal mine tunnel, the positioning of the forepoling support mainly depends on operators at present, the positioning precision is low and unstable, so that the subsequent anchoring operation has larger installation error, and the overall stability and safety of surrounding rocks of the tunnel are influenced. Therefore, in order to overcome the defects in the prior art, the invention provides the ultra-wideband positioning method of the tunneling support combined with particle swarm optimization and Taylor-series expansion, which is an efficient and accurate autonomous positioning method of the support.
The technical scheme is as follows: in order to achieve the purpose, the invention adopts the technical scheme that:
the ultra-wideband positioning of the tunneling support bracket is realized by combining particle swarm optimization and Taylor series expansion. Constructing an ultra-wideband positioning model according to the working principle of a tunneling support in a tunneling roadway; and then, converting the ultra-wideband positioning problem into a class of optimization problem, solving the positioning point with the minimum positioning error by utilizing a particle swarm optimization algorithm to serve as an initial value of Taylor series expansion, and finally solving the optimal coordinate of the positioning point through iterative solution.
The positioning method comprises the following concrete implementation steps:
1. establishing ultra-wideband positioning model
The ultra-wideband technology is adopted to position the tunneling support in the coal mine tunnel, and the layout of the base station and the ranging module is shown in figure 1. The ultra-wideband ranging module is installed on a positioning point of a tunneling support, K base stations are arranged behind a roadway, and coordinates of each base station relative to a roadway reference are accurately calibrated by a ground survey person before tunneling the roadway. And the K base stations sequentially carry out ultra wide band ranging on the positioning points of the tunneling support. And establishing a positioning equation set according to the ranging information, further calculating the coordinates of the positioning points of the tunneling support, and calculating to obtain the position of the tunneling support in the roadway.
Let the position coordinates of K base stations be (x) with i, i being 1,2Bi,yBi,zBi) The position coordinate of the positioning point of the tunneling support bracket is (x)s,ys,zs) The measurement distance between the ith base station and the positioning point of the tunneling support is diAnd then the TOA observation equation set between all the base stations and the positioning points of the tunneling support is as follows:
2. ultra-wideband positioning method for tunneling support bracket integrating Taylor and particle swarm optimization algorithm
The positioning calculation method based on Taylor has strong dependence on the initial value, so that a better value obtained by a particle swarm optimization algorithm is used as the initial value of iterative operation, and the positioning accuracy is improved.
2.1 particle swarm optimization Algorithm
Assuming that the coordinates of the positioning points satisfy xs∈[xsmin,xsmax]，ys∈[ysmin,ysmax]，zs∈[zsmin,zsmax]. The extreme value of the positioning point depends on the space size of the coal mine tunneling roadway. The ultra-wideband positioning problem is converted into the following optimization problem:
in the particle swarm optimization algorithm, the error function from the positioning point to each base station is used as the target function of the ultra-wideband positioning equation set solution, and the positioning point (x)s,ys,zs) And as the particles, resolving the ultra-wideband positioning initial value by adopting the WPSO algorithm. The specific process is as follows:
step 1: randomly initializing the position and the speed of the particles, and setting key parameters of an algorithm;
step 2: updating the speed and position of the particles;
step 3: evaluating the fitness value of each particle;
step 4: for each particle, updating the historical optimal value p of the particle according to the current particle adaptive valuei(t)；
Step 5: for the whole particle swarm, updating the global optimal value p according to the current optimal value of the particle swarmg(t)；
Step 6: if the maximum iteration times are reached, outputting an optimization result; otherwise, go to Step 2.
2.2Taylor series expansion
Using the positioning point with the minimum positioning error obtained by the particle swarm optimization algorithm as an initial coordinate (x) of Taylor series expansions0,ys0,zs0) And carrying out Taylor series expansion on the positioning equation set, and neglecting components with more than two orders to obtain positioning errors (delta x, delta y and delta z). Correcting the coordinates of the positioning points according to the positioning errors; and repeating the iteration until the absolute value of the absolute. The finally obtained optimal positioning point coordinates are
Has the advantages that: the patent integrates particle swarm optimization and Taylor series expansion, and provides a novel ultra-wideband positioning method for a tunneling support bracket. Firstly, establishing an ultra-wideband positioning model of a tunneling support bracket; secondly, obtaining initial positioning coordinates expanded by a Taylor series by adopting a particle swarm optimization algorithm, and resolving an ultra-wideband positioning equation set by iteration to obtain optimal positioning point coordinates; finally, under the noise ranging environment, the average positioning errors of the five existing algorithms and the proposed algorithm are compared. According to the positioning result, the following steps are carried out: the WPSO-Taylor algorithm provided by the patent has the advantages of obvious advantages and highest positioning accuracy, and can better meet the requirement of a tunneling support bracket on the positioning accuracy.
Drawings
FIG. 1 is a schematic diagram of a coal mine roadway excavation support positioning system; in the figure, 1,2,3, 4-positioning base station; p, tunneling a support positioning point;
FIG. 2 is an algorithm flow of a tunneling support bracket ultra-wideband positioning method combining particle swarm optimization and Taylor series expansion;
FIG. 3 is an average positioning error of six positioning methods at different ranging distances in a noisy environment;
FIG. 4 is a block diagram of positioning errors of six positioning methods in a noisy environment at different ranging distances; in the figure, under each distance measurement, six comparison algorithms are an LS algorithm, a direct method, a WPSO algorithm, an LS-Taylor algorithm, a direct-Taylor algorithm and a WPSO-Taylor algorithm from left to right in sequence.
Detailed Description
The invention discloses a tunneling support bracket ultra-wideband positioning method integrating particle swarm optimization and Taylor-grade expansion. Constructing a positioning model of the ultra-wideband base station according to the layout of the ultra-wideband base station of the tunneling support; ultra-wideband positioning is converted into a class of optimization problems, and a particle swarm optimization algorithm is adopted to perform global optimization to obtain positioning point coordinates with the minimum positioning error; and taking the optimal positioning point obtained by the particle swarm optimization algorithm as an initial value, adopting Taylor series expansion, and realizing local positioning coordinate optimization through iteration to obtain the optimal coordinate of the positioning point. The positioning method is high in precision and easy to realize, has good robustness for ultra-wideband positioning in noise environments such as tunneling support, and has a remarkable application prospect.
The invention is further described with reference to the following figures and examples.
Examples
The ultra-wideband positioning of the tunneling support bracket is realized by combining particle swarm optimization and Taylor series expansion. According to an ultra-wideband base station and a ranging module which are arranged in a coal mine excavation roadway, establishing an ultra-wideband positioning model according to the working principle of an excavation supporting bracket in the excavation roadway, and further obtaining a positioning equation set; the ultra-wideband positioning problem is converted into an optimization problem, a particle swarm optimization algorithm and a Taylor series expansion method are fused to solve a positioning equation set, a positioning point which enables the positioning error to be minimum is sought, namely the positioning point which enables the positioning error to be minimum is solved by the particle swarm optimization algorithm and serves as an initial value of Taylor series expansion, and the optimal coordinates of the positioning point are finally solved through iterative solution.
The specific implementation process is as follows:
1. establishing ultra-wideband positioning model
The ultra-wideband technology is adopted to position the tunneling support in the coal mine tunnel, and the layout of the base station and the ranging module is shown in figure 1. The ultra-wideband ranging module is installed on a positioning point of a tunneling support, K base stations are arranged behind a roadway, and coordinates of each base station relative to a roadway reference are accurately calibrated by a ground survey person before tunneling the roadway. And the K base stations sequentially carry out ultra wide band ranging on the positioning points of the tunneling support. And establishing a positioning equation set according to the ranging information, further calculating the coordinates of the positioning points of the tunneling support, and calculating to obtain the position of the tunneling support in the roadway.
Assuming that the i, i is 1,2, …, the position coordinates of K base stations are (x)Bi,yBi,zBi) The position coordinate of the positioning point of the tunneling support bracket is (x)s,ys,zs) The measurement distance between the ith base station and the positioning point of the tunneling support is diAnd then the TOA observation equation set between all the base stations and the positioning points of the tunneling support is as follows:
the purpose of resolving the positioning equation is to obtain the most accurate position of the tunneling support and record the position as the most accurate position
2. Ultra-wideband positioning method for tunneling support bracket integrating particle swarm optimization and Taylor series expansion
The positioning calculation method based on the Taylor series expansion has strong dependence on the initial value, so that a better value obtained by the particle swarm optimization algorithm is used as the initial value of the iterative operation, and the positioning accuracy is improved.
2.1 particle swarm optimization Algorithm
The particle swarm optimization algorithm is a swarm intelligence optimization method proposed by Eberhart and Kennedy in 1995, has the advantages of simple realization, high convergence rate and the like, and is widely applied to the fields of function optimization, artificial neural network training and the like. The patent applies the particle swarm optimization algorithm to solve the problem of positioning calculation.
The particle swarm optimization algorithm is derived from the research on the predation behavior of the bird swarm, and the basic idea is as follows: a group is formed by a plurality of particles, and the optimal solution of the problem is found through cooperation and information sharing among the particles in the group. In the algorithm, each particle corresponds to one possible solution to the optimization problem, whose direction of flight and distance are described by velocity and position. The performance of the particles depends on the objective function of the problem to be optimized.
Assume that the population of particles contains n particles. The position and flight speed of the ith particle in the t generation are respectively marked as xi(t) and vi(t) its historical optimum position is noted as pi(t) global optimum of particle is denoted as pg(t), the particles are updated in terms of flight speed and position according to the following formula:
xi(t+1)＝vi(t+1)+xi(t) (2)
vi(t+1)＝w(t)vi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t)) (3)
in the formula, c1And c2Is a learning factor, r1And r2Is [0,1 ]]And satisfy uniformly distributed random numbers. And w is an inertia weight and is used for coordinating global search and local exploration capacity. In the adaptive Weight Particle Swarm Optimization (WPSO), w is adaptively updated:
in the formula, wmaxAnd wminRespectively the maximum and minimum values of the inertia weight, t is the current time, tmaxIs the maximum number of iterations. And (5) outputting an optimization result by repeatedly iterating the position and the speed of the particles until an algorithm termination condition is met.
Suppose the coordinates of the anchor point are (x)s,ys,zs) Satisfy xs∈[xsmin,xsmax]，ys∈[ysmin,ysmax]，zs∈[zsmin,zsmax]. The extreme value of the positioning point depends on the space size of the coal mine tunneling roadway. Recording the measurement distance from the ultra-wideband base station to a positioning point as diThen, the ultra-wideband positioning problem is transformed into the following optimization problem:
in the particle swarm optimization algorithm, the error function from the positioning point to each base station is used as the target function of ultra-wideband positioning, and the positioning point (x)s,ys,zs) And (4) solving the ultra-wideband positioning point by adopting the WPSO algorithm as the particles. The specific process is as follows:
step 1: randomly initializing the position and the speed of the particles, and setting key parameters of an algorithm;
step 2: updating the speed and position of the particles;
step 3: evaluating the fitness value of each particle;
step 4: for each particle, updating the historical optimal value p of the particle according to the current particle adaptive valuei(t)；
Step 5: for the whole particle swarm, updating the global optimal value p according to the current optimal value of the particle swarmg(t)；
Step 6: if the maximum iteration times are reached, outputting an optimization result; otherwise, go to Step 2.
2.2Taylor positioning method
The Taylor positioning method is a recursive algorithm, and based on a positioning initial value, the real coordinates of a positioning node are obtained through repeated iteration. The true coordinates of the anchor point are assumed to be (x)sa,ysa,zsa) The initial value is (x)s0,ys0,zs0) The real positioning point coordinates and the positioning point coordinates after calculation satisfy the following relations:
based on initial coordinates (x)s0,ys0,zs0) And performing Taylor series expansion on the positioning equation set, and neglecting components with more than two orders to obtain a positioning error (delta x, delta y, delta z) as follows:
wherein:
and correcting and resolving the coordinates of the positioning points according to the positioning errors, and repeating iteration until the requirement that | delta x | + | delta y | + | delta z | < epsilon, wherein epsilon is a preset error threshold value. The finally obtained optimal positioning point coordinates are
Obviously, in the above Taylor positioning method, the obtained final positioning coordinates are sensitive to the setting of the initial coordinates. In order to effectively improve the positioning precision, the patent provides a novel positioning method integrating particle swarm optimization and Taylor series expansion (WPSO-Taylor). Firstly, converting the solving problem of the ultra-wideband positioning equation set into a class of optimization problems, and obtaining an optimal positioning point coordinate by optimizing by adopting a particle swarm optimization algorithm; then, the initial coordinates of the Taylor positioning method are used to obtain the final positioning point coordinates. Therefore, the novel positioning method fully utilizes the global parallel search capability of the particle swarm optimization algorithm and the rapid local search capability of Taylor series expansion, thereby effectively improving the positioning precision. An algorithm flow of the tunneling support positioning method combining particle swarm optimization and Taylor series expansion is shown in fig. 2.
3. Experimental analysis and description of results
In order to fully verify the reasonability and the effectiveness of the ultra-wideband positioning method provided by the patent, the verification is carried out under the conditions that the actual operation environment of a driving tunnel exists and the positioning equation set has a distance measurement error.
3.1 test Environment and parameter settings
Assuming that the rectangular roadway is 4.2m wide, 3.9m high and 100m long, the advance support bracket is located by using 4 base stations, and the coordinates of the 4 base stations are (0,0,0), (1.9,1.9,0), (-1.9,1.9,0), (0,0,3.8), respectively. And (3) carrying out ultra-wideband ranging on the positioning points of the tunneling support bracket in sequence every 10m by 4 base stations, and assuming that ranging errors are subjected to normal distribution with the average value of 0 and the standard deviation of 2 cm. Under the positioning environment, 1000 times of distance measurement is carried out, and the average value is taken as a distance estimation value to carry out simulation calculation.
Since the excavation support is located in the long and narrow coal mine tunnel, the search range of the particles depends on the tunnel size, namely xs∈[-2,2]，ys∈[10,100]，zs∈[0,4]. Selecting a particle swarm with the particle swarm size of 40, the maximum iteration number of 300 generations, and the maximum particle velocity vmax0.4, learning factor c1＝c2The error threshold in the Taylor-series expansion is set to be ∈ 0.5.
The coordinates of a resolving positioning point obtained after the kth operation are assumed to be
The coordinates of the real positioning point are (x)sa,ysa,zsa) And L is the running times, then the average positioning error is defined as:
the known anchor point is (x)sa,ysa,zsa) (0, m,3.5), m ═ 10,20, …, 100. For each m, the following system of localization equations is satisfied:
3.2 analysis of the results of the solution for different positioning methods
In a real tunneling roadway, certain noise exists by adopting ultra-wideband ranging. Under the noise environment, the direct method, the LS algorithm, the WPSO algorithm, the LS-Taylor algorithm, the direct-Taylor algorithm and the proposed WPSO-Taylor algorithm are respectively used for solving the ultra-wideband positioning equation set of the tunneling support. Positioning errors after 500 independent operations under different ranging distance conditions are shown in table 1; the corresponding average positioning error and its boxplot are shown in fig. 3 and 4.
TABLE 1 average positioning error (/ m) results of six solutions in noisy environment
Comparing the average positioning error of the six algorithms can know that: the average positioning error of the six algorithms increases linearly with the increase of the measurement distance. In contrast, the overall positioning accuracy of the LS algorithm and the direct algorithm is low. The WPSO algorithm is superior to the LS algorithm and the direct method, but has a positioning accuracy inferior to the LS-Taylor algorithm and the direct-Taylor algorithm. Obviously, the three positioning methods based on the Taylor-series expansion have relatively good performance. The WPSO-Taylor algorithm provided by the patent has higher positioning precision than LS-Taylor algorithm and direct-Taylor algorithm.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (8)
1. A tunneling support bracket ultra-wideband positioning method integrating particle swarm optimization and Taylor series expansion is characterized in that: utilizing the parallel solving capability of the particle swarm optimization algorithm to carry out global optimization to obtain a positioning point with the minimum positioning error as an initial value of Taylor series expansion, and then obtaining an optimal positioning coordinate through iteration;
the positioning error is reduced by improving the initial value positioning precision of the Taylor positioning method, and the specific algorithm steps are as follows:
step 1: constructing an ultra-wideband positioning model;
step 2: randomly initializing the position and the speed of the particles, and setting key parameters of an algorithm;
step 3: updating the speed and position of the particles;
step 4: evaluating the fitness value of each particle;
step 5: for each particle, updating the historical optimal value p of the particle according to the current particle adaptive valuei(t)；
Step 6: for the whole particle swarm, updating the global optimal value p according to the current optimal value of the particle swarmg(t)；
Step 7: judging whether the maximum iteration times is reached, and if the maximum iteration times is reached, setting the positioning coordinates as the initial value of the Taylor method; otherwise, go to Step 3;
step 8: carrying out Taylor series expansion at the initial value and neglecting components above the second order;
step 9: calculating a positioning error;
step 10: correcting the coordinates of the positioning points according to the positioning errors;
step 11: judging whether the requirement of the error threshold value is met, and outputting a final positioning coordinate if the requirement is met; otherwise, go to Step 9.
2. The ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step1 comprises the following steps: constructing an ultra-wideband positioning model according to an ultra-wideband layout mode of a tunneling support bracket, and assuming that the ith and the i are 1,2 and …, and the position coordinates of K base stations are (x)Bi,yBi,zBi) The position coordinate of the positioning point of the tunneling support bracket is (x)s,ys,zs) The measurement distance between the ith base station and the positioning point of the tunneling support is diPositioning points of all base stations and tunneling supportThe system of TOA observation equations in between is:
3. the ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step3 comprises the following steps: suppose the position and flight speed of the ith particle in the t generation in the particle swarm are xi(t), i ═ 1,2, …, n and vi(t) the historical optimum position is pi(t) the global optimum value of the particle swarm is pg(t), then the particle update flight velocity and position are as follows:
xi(t+1)＝vi(t+1)+xi(t) (2)
vi(t+1)＝w(t)vi(t)+c1r1(pi(t)-xi(t))+c2r2(pg(t)-xi(t)) (3)
in the formula, c1And c2Is a learning factor, r1And r2Is [0,1 ]]And w is the self-adaptive updating inertia weight and is recorded as:
in the formula, wmaxAnd wminRespectively the maximum and minimum values of the inertia weight, t is the current time, tmaxIs the maximum number of iterations.
4. The ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step4 comprises the following steps: assuming that the i, i is 1,2, …, the position coordinates of K base stations are (x)Bi,yBi,zBi) The coordinates of the locating point are (x)s,ys,zs) Positioning the ith base station and the tunneling supportThe measured distance of the points being diAnd then the ultra-wideband positioning problem is converted into the following optimization problem for evaluating the particle adaptive value:
5. the ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step5 comprises the following steps: for each particle xi(t) adapting its current adaptation value f (x)i(t)) and its historical optimum f (p)i(t)) comparing, and if the current particle adaptive value is superior to the historical optimal adaptive value, keeping the current state of the particle as the historical optimal value; otherwise, the original historical optimal value is kept:
6. the ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step6 comprises the following steps: compare each pi(t) and the global optimum pg(t) adapted value of pi(t) is better than the global optimum, the global optimum is updated to pi(t); otherwise, the original global optimum value is retained.
7. The ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step8, 9 is as follows: using the positioning point with the minimum positioning error obtained by the particle swarm optimization algorithm as the initial coordinate of the Taylor series expansion
Based on initial coordinates (x)s0,ys0,zs0) And performing Taylor series expansion on the positioning equation set, and neglecting components with more than two orders to obtain a positioning error (delta x, delta y, delta z) as follows:
where, the ith, i ═ 1,2, …, and the position coordinates of K base stations are (x)Bi,yBi,zBi) The measurement distance between the ith base station and the positioning point of the tunneling support is di。
8. The ultra-wideband positioning method for the tunneling support bracket integrating particle swarm optimization and Taylor-grade expansion as claimed in claim 1, wherein the ultra-wideband positioning method comprises the following steps: the specific method of Step11 comprises the following steps: judging whether the positioning error meets | delta x | + | delta y | + | delta z | < epsilon, wherein epsilon is a preset error threshold and is set to be 0.5; if the judgment condition is met, outputting the coordinates of the optimal positioning point as
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| CN110927668A (en) * | 2019-11-21 | 2020-03-27 | 山东科技大学 | Sound source positioning optimization method of cube microphone array based on particle swarm |
| CN111852456B (en) * | 2020-07-29 | 2023-04-07 | 中国矿业大学 | Robust UWB (ultra wide band) underground anchor rod drilling positioning method based on factor graph |
| CN111948602A (en) * | 2020-08-17 | 2020-11-17 | 南京工程学院 | Two-dimensional UWB indoor positioning method based on improved Taylor series |
| CN113923591B (en) * | 2021-10-08 | 2022-07-22 | 西安电子科技大学 | Intelligent filter function optimization method and system for improving ultra-wideband ranging precision |
| CN114245301B (en) * | 2021-12-20 | 2024-02-09 | 杭州电子科技大学 | UWB underground target positioning method based on redundant distance |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620270A (en) * | 2009-07-23 | 2010-01-06 | 重庆邮电大学 | Wireless location method based on cluster-fusion |
| CN106941663A (en) * | 2017-05-16 | 2017-07-11 | 重庆邮电大学 | It is a kind of to merge convex optimization and the UWB localization methods of multi-objective particle swarm |
-
2018
- 2018-01-11 CN CN201711334978.1A patent/CN108168556B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101620270A (en) * | 2009-07-23 | 2010-01-06 | 重庆邮电大学 | Wireless location method based on cluster-fusion |
| CN106941663A (en) * | 2017-05-16 | 2017-07-11 | 重庆邮电大学 | It is a kind of to merge convex optimization and the UWB localization methods of multi-objective particle swarm |
Non-Patent Citations (2)
| Title |
|---|
| Experimental UWB indoor channel characterization in stationary and mobility scheme;Ahmed M. Al-Sammana;《Measurement》;20171231;第333-339页 * |
| 改进粒子群优化算法在TDOA定位中的应用;翟彦蓉等;《传感器与微系统》;20131231;第32卷(第4期);第145-152页 * |
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