CN113326912B - Information sharing Harris eagle optimization-based ultra-wideband positioning method - Google Patents

Information sharing Harris eagle optimization-based ultra-wideband positioning method Download PDF

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CN113326912B
CN113326912B CN202110591874.9A CN202110591874A CN113326912B CN 113326912 B CN113326912 B CN 113326912B CN 202110591874 A CN202110591874 A CN 202110591874A CN 113326912 B CN113326912 B CN 113326912B
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万新旺
张海成
董帅
李逸玮
王鹤
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Nanjing University of Posts and Telecommunications
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Abstract

The invention relates to an ultra-wideband positioning method based on information sharing Harris eagle optimization, which comprises the following steps: step 1: measuring distance by adopting a bilateral two-way distance measuring method; step 2: initializing a population; and step 3: calculating the fitness, and selecting an optimal individual by a greedy mechanism; and 4, step 4: information sharing and updating; and 5: calculating escape energy by using a sine and cosine disturbance term escape energy calculation method, judging | E | >1, if yes, executing a step 6, and if not, executing a step 7; step 6: in the searching stage, searching and updating are carried out, and then step 8 is executed; and 7: in the development stage, development and updating are carried out; and 8: and calculating the fitness, and selecting the optimal individual to update the prey position by a greedy mechanism. The invention improves the existing Harris eagle optimized ultra-wideband positioning method, introduces an information sharing mechanism to increase the population diversity, improves the balanced search and development stage of the escape energy calculation method, and effectively improves the node positioning precision.

Description

Information sharing Harris eagle optimization-based ultra-wideband positioning method
Technical Field
The invention relates to the technical field of wireless positioning, in particular to an ultra-wideband positioning method based on information sharing Harris eagle optimization.
Background
Ultra Wide Band (UWB) technology appeared in the 60 th 20 th century, and was used mainly in the military field due to its low power and good concealment, and along with the increasing demand for transmission rate in the development of wireless networks, UWB technology has been developed rapidly and is continuously applied in the civil field, especially in the positioning of wireless networks.
Positioning methods are very important in positioning, and node positioning methods are to estimate the position of an unknown node through communication with other nodes according to a reference node with known position information, and existing positioning methods can be roughly divided into two types: based on ranging (range-based) and based on non-ranging (range-free). Based on the distance measurement positioning algorithm, positioning is carried out by measuring the actual distance between the nodes, and the positioning precision is higher, such as: RSSI (received Signal Strength indicator), TOA (time of arrival), AOA (angle of arrival), etc.; and based on the non-ranging positioning method, the Distance information is indirectly obtained according to the information such as network connectivity and relevance, the positioning accuracy is relatively poor, but the cost is relatively low, such as central, APIT (Approximate point in standardization), DV-Hop (Distance Vector-Hop), and the like.
The ultra-wideband signal has high time resolution and high positioning precision, and is suitable for a time-based positioning method, a time-based algorithm is a typical positioning method based on ranging, and the basic idea is that the distance from an unknown node to a reference node represents the signal propagation time multiplied by the speed of light, the positioning precision of the time-based algorithm is very high under the ideal condition, but the actual environment is complicated, and delay and non-line-of-sight error in the signal propagation process can cause great influence on positioning, so that the positioning result is inaccurate.
In practical application, the ultra wide band location is used under complicated indoor environment more, and the monitoring area has the barriers that influence wireless signal transmission such as wall body, desk, door usually, thereby the ultra wide band signal receives blocking of barrier and can make range finding time overlength to lead to the fact the error to the distance between the node, and this kind of error has made very big influence to traditional positioning algorithm positioning accuracy, has many scientific research workers at present to utilize intelligent optimization algorithms such as particle swarm to improve positioning accuracy. Harris hawk optimization Algorithm (HHO) is a novel bionic intelligent optimization Algorithm proposed by Heideri et al inspired by the life habits of Harris Hawks in 2019, and is published in an article entitled Harris hawk optimization in Future of Future generation systems, Algorithm and applications, the Algorithm adopts non-gradient search, has strong global search capability, and has few parameters needing to be adjusted and applied to a plurality of fields, but the traditional Harris hawk Algorithm has certain limitation in the application of the positioning field, does not completely simulate the characteristic of the Harris hawk, and finds out the Harris hawk on a tree through two strategies in the search stage, the two strategies improve the diversity of the population, but do not fully utilize the information sharing capability among the Harris Hawks, the optimal search capability is not easy to fall into the local solution, meanwhile, the algorithm can only enter a development stage in the later iteration stage, so that the searching capability and the convergence speed of the algorithm are reduced, and the positioning accuracy of the algorithm is limited.
Disclosure of Invention
Aiming at the problems, the invention provides an ultra-wideband positioning method based on information sharing Harris eagle optimization, which is improved on the basis of a Harris eagle optimization algorithm, adopts an improved population to initialize to improve the convergence speed of the algorithm, introduces an information sharing updating mode, increases the diversity of the population, avoids falling into a local optimal solution, and finally adopts an escape energy calculation method of sine and cosine disturbance terms to balance a search stage and a development stage, thereby effectively improving the positioning precision of nodes.
The invention relates to an ultra-wideband positioning method based on information sharing Harris eagle optimization, which comprises the following steps:
step 1, measuring the distance d between an unknown node and an anchor node by adopting a bilateral two-way distance measurement method i，k ；
Step 2, initializing, setting a maximum iteration number T and a fitness threshold lambda, initializing a population, and replacing an element in the population by using a coarse positioning individual to obtain an improved initial population;
step 3, calculating the individual fitness according to the fitness function calculation method, and selecting the individual with the best fitness as the position X of the prey rabbit (t)；
Step 4, selecting an updating individual according to an information sharing updating mode and updating through the average value of the population individuals and the position information of other individuals;
step 5, calculating escape energy E according to an escape energy calculation method for increasing sine and cosine disturbance, judging whether the escape energy E enters a search stage or a development stage, and executing step 6 if the escape energy E enters the search stage, or executing step 7 if the escape energy E does not enter the development stage;
step 6: a searching stage, updating the individual position according to the global searching updating formula, and executing the step 8;
and 7: in the development stage, a development strategy is selected according to the | E | and r for updating, and the step 8 is executed;
and 8: calculating an updated fitness value, selecting an individual with the best fitness as the optimal prey position of the current iteration, judging whether the fitness is smaller than a threshold value or reaches the maximum iteration number by the iteration number +1, finishing the algorithm if the condition is satisfied, and returning to the step 4 if the condition is not satisfied.
Further, the specific process of step 2 is as follows:
firstly, determining the size N of a population, wherein the problem dimension dim is 2, the upper limit ub of a search space and the lower limit lb of the search space; randomly generating the positions of individuals in the population by using a formula according to the population scale and the search space limit:
X i ＝rand(1,2)×(ub-lb)+lb
wherein, X i Indicates the position of the ith individual in the population, i belongs to 1-N, and rand (1,2) indicates the randomly generated element [0,1]]And replacing an element in the population by using the coarse positioning individual position information obtained by the TOA mixed trilateral positioning algorithm to obtain an improved initial population.
Further, in the step 3, the fitness is calculated according to the following fitness function calculation method, and the position of the individual with the best fitness is selected as the position X of the prey rabbit ；
Figure BDA0003089557620000031
Where K represents the number of range measurements, (x) i ，y i ) Indicating the location of the unknown tag node (x) k ，y k ) Indicating the location of the base station, d i,k Represents the distance, f (X), between the unknown node i and the anchor node K i ) Representing the value of fitness, comparing the value of fitness, and selecting the individual X with smaller fitness i Position X as prey rabbit That is to say X rabbit Is all individuals X i The medium fitness is the smallest.
Further, the specific process of step 4 is as follows:
step 4-1, determining the number of Harris hawks in the search stage, wherein most of the Harris hawks are required to be explored in the early stage of the algorithm, and the number of the Harris hawks required to be explored after a prey is found is reduced, so that the following relation is established by simulating the way:
Figure BDA0003089557620000032
wherein s _ num represents the current number participating in the search, s _ max and s _ min respectively represent the maximum minimum number participating in the search, T represents the current iteration number, and T represents the maximum iteration number;
step 4-2, obtaining information by an information sharing Harris eagle optimization algorithm: and randomly generating a random number in the range of [0,1], and acquiring information from the shared area if the number is more than or equal to 0.5, otherwise acquiring the information from the partner.
Further, in step 4-2, information is obtained from the shared area:
Figure BDA0003089557620000033
X i (t+1)＝X i (t)+rand()·[X rabbit (t)-β1·X mean ]
wherein, X mean Mean, X, representing position in the population i (t) represents the location of the ith individual in the tth iteration population, X i (t +1) represents the position of the ith individual in the t +1 th iteration population, and rand () represents [0,1]]Beta 1 represents an information acquisition factor used for determining the amount of information acquired by an individual from a shared area, and the value range is also [0, 1%]。
Further, in step 4-2, information is shared among collaborators:
X i (t+1)＝X i (t)+levy(dim)·[X rabbit (t)-X j (t)]，i≠j
where levy (dim) represents the flight function, dim represents the dimension, X i (t)、X j (t) indicates the location of the different individuals in the population at the t-th iteration.
Further, the specific process of step 5 is as follows:
calculating escape energy according to an escape energy calculation method for increasing sine and cosine disturbance represented by the following formula, judging the state, converting into a search transition state when the energy | E | is more than or equal to 1, and entering a search stage; when the energy | E | <1, the method is converted into a development state, and a development stage is entered:
Figure BDA0003089557620000041
Figure BDA0003089557620000042
where δ represents a perturbation term and rand () represents [0,1]]T represents the current iteration number, T represents the maximum iteration number, alpha is a constant and determines the position of the disturbance peak, E represents the escape energy, E represents the maximum iteration number 0 Represents the initial value of the escape energy, and is automatically updated to [ -1, 1] at the beginning of each iteration]A random number in between.
Further, the specific process of step 6 is as follows: and updating the search stage, wherein the expression is as follows:
Figure BDA0003089557620000043
wherein t represents the number of iterations, X rand (t) random individuals for the t-th iteration, X i (t)、X i (t +1) denotes the position of the ith individual at the current position and at the next iteration, X rabbit (t) represents the position of the prey at the t-th iteration, namely the position of the individual with the optimal current fitness, ub and lb represent the upper and lower bounds of the search space, r 1 、r 2 、r 3 、r 4 Is represented by [0,1]Q is also [0,1]]Random number between them, representing the random selection of equal probability for updating the two strategies, X mean (t) represents the average position of the t iteration individuals, and is calculated by the following formula:
Figure BDA0003089557620000044
wherein N represents the size of the population.
Further, in step 7, the development strategy is:
firstly, randomly generating a random number r between [0,1 ];
when | E | is more than or equal to 0.5 and less than or equal to 1 and r is more than or equal to 0.5, adopting a soft enclosure strategy to update the position, wherein the position updating formula is as follows:
X i (t+1)＝ΔX i (t)-E|j·X rabbit (t)-X i (t)|
wherein k represents [0,2 ]]Random number in between, Δ X i (t) represents the difference between the prey location and the current individual location, calculated as follows:
ΔX i (t)＝X i (t+1)-X i (t)
when | E | <0.5 and r ≧ 0.5, a hard attack strategy is adopted to update the position, and the position update formula is as follows:
X i (t+1)＝X rabbit (t)-E|ΔX i (t)|
when the | E | is more than or equal to 0.5 and less than 1 and r is less than 0.5, adopting a soft enclosure strategy of gradual quick dive to update the position, wherein the position updating formula is as follows:
Figure BDA0003089557620000051
Y＝X rabbit (t)-E·|j·X rabbit (t)-X i (t)|
Z＝Y+S×levy(dim)
wherein f (·) represents a fitness function, S represents a two-dimensional random vector, and all elements in the random vector are random numbers between [0 and 1], wherein dim represents a dimension, levy (dim) represents a levy flight function, and the formula is as follows:
Figure BDA0003089557620000052
wherein u and mu represent random numbers between [0,1], beta is a constant and takes a value of 1.5, and gamma (1+ beta) represents a gamma function;
when | E | <0.5 and r <0.5, the position is updated by adopting a hard attack strategy of progressive fast dive, and the position update formula is as follows:
Figure BDA0003089557620000053
Y＝X rabbit (t)-E|j·X rabbit (t)-X mean (t)|
Z＝Y+S×levy(dim)。
the invention has the beneficial effects that: on the basis of the Harris eagle optimization algorithm, the improved initial population is adopted to increase the convergence speed of the algorithm, an information sharing and updating mode is introduced, the diversity of the population in the iteration process is increased, the algorithm is prevented from falling into a local optimal solution, and finally the search and development stages of the algorithm are balanced by adopting a sine and cosine disturbance term method, so that the positioning precision of the algorithm in a complex environment is improved, and the running time of the algorithm is reduced. Compared with the classical Harris eagle optimization algorithm and the existing improved intelligent optimization positioning algorithm, the method can reduce the error rate, improve the positioning accuracy and solve the problem of poor positioning accuracy of the complex environment.
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In order that the present invention may be more readily and clearly understood, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
FIG. 1 is a flow chart of a positioning method of the present invention;
FIG. 2 is a positioning scene diagram;
FIG. 3 is a comparison of root mean square error versus population size for the present invention and prior algorithms;
FIG. 4 is a graph comparing fitness value versus iteration number for the present invention and a prior art algorithm;
FIG. 5 is a graph comparing the root mean square error versus the variance of the measurement error for the present invention and a prior algorithm;
FIG. 6 is a comparison of the root mean square error of the present invention versus the number of base stations of the prior art algorithm;
FIG. 7 is a graph comparing the cumulative probability of the present invention compared to prior algorithms.
Detailed Description
As shown in fig. 1, the ultra-wideband positioning method based on information sharing harris eagle optimization according to the present invention includes the following steps:
step 1, the unknown node and the anchor node have UWB signal transceiving functions, and the distance d between the unknown node and the anchor node is measured by adopting a bilateral two-way distance measurement method i，k ；
Step 2, initializing, setting a maximum iteration number T and a fitness threshold lambda, initializing a population, and replacing an element in the population by using a coarse positioning individual to obtain an improved initial population;
the population scale represents the number of random positions to be generated, the problem dimension determines the dimension of the vector, and the fitness threshold and the iteration times are used for judging whether the end is reached or not; firstly, determining the size N of a population, wherein the problem dimension dim is 2, the upper limit ub of a search space and the lower limit lb of the search space, and randomly generating the positions of individuals in the population by using a formula according to the population size and the search space limit:
X i ＝rand(1,2)×(ub-lb)+lb
wherein, X i Indicates the position of the ith individual in the population, i belongs to 1-N, and rand (1,2) indicates the randomly generated element [0,1]]And (3) randomly replacing an element in the population by using the position information of the roughly positioned individual obtained by the TOA mixed trilateral positioning algorithm to obtain an improved initial population.
Step 3, calculating the fitness according to the following fitness function calculation method, and selecting the individual position with the best fitness as the position X of the prey rabbit ；
Figure BDA0003089557620000071
Where K represents the number of range measurements, (x) i ，y i ) Indicating the location of the unknown tag node (x) k ，y k ) Indicating the location of the base station, d i,k Represents the distance, f (X), between the unknown node i and the anchor node K i ) Representing the value of fitness, comparing the value of fitness, and selecting the individual X with smaller fitness i To serve as the location X of a prey rabbit That is to say X rabbit Is all individuals X i The medium fitness is the smallest.
And 4, randomly selecting two information sharing and updating modes, selecting an updating individual and updating through the mean value of the population individuals or the position information of other individuals, wherein the specific flow is as follows:
step 4-1, determining the number of Harris hawks in the search stage, wherein most of the Harris hawks are required to be explored in the early stage of the algorithm, and the number of the Harris hawks required to be explored after a prey is found is reduced, so that the following relation is established by simulating the way:
Figure BDA0003089557620000072
wherein s _ num represents the current number participating in the search, s _ max and s _ min respectively represent the maximum minimum number participating in the search, T represents the current iteration number, and T represents the maximum iteration number;
step 4-2, information sharing is carried out to obtain information;
the information sharing Harris eagle optimization algorithm has two main ways for acquiring information, wherein the first way is to acquire information from a shared area, and the other way is to acquire information from a partner; two kinds of information sharing and updating, and the like.
Firstly, randomly generating a random number in the range of [0,1], if the number is more than or equal to 0.5, acquiring information from the shared area, otherwise, acquiring the information from the partner.
(1) Obtaining information from the shared area:
Figure BDA0003089557620000073
X i (t+1)＝X i (t)+rand()·[X rabbit (t)-β1·X mean ]
wherein, X mean Mean, X, representing position in the population i (t) represents the location of the ith individual in the tth iteration population, X i (t +1) represents the position of the ith individual in the t +1 th iteration population, and rand () represents [0,1]]Beta 1 represents an information acquisition factor used for determining the amount of information acquired by an individual from a shared area, and the value range is also [0, 1%]。
(2) Sharing information between collaborators:
X i (t+1)＝X i (t)+levy(dim)·[X rabbit (t)-X j (t)]，i≠j
where levy (dim) represents the flight function, dim represents the dimension, X i (t)、X j (t) indicates the location of the different individuals in the population at the t-th iteration.
Step 5, calculating escape energy E according to an escape energy calculation method for increasing sine and cosine disturbance, judging whether the escape energy E enters a search stage or a development stage, and executing step 6 if the escape energy E enters the search stage, or executing step 7 if the escape energy E does not enter the development stage;
calculating escape energy according to an escape energy calculation method for increasing sine and cosine disturbance represented by the following formula, judging the state, converting into a search transition state when the energy | E | is more than or equal to 1, and entering a search stage; when the energy | E | <1, the method is converted into a development state, and a development stage is entered:
Figure BDA0003089557620000081
Figure BDA0003089557620000082
where δ represents a disturbance term, and rand () represents [0,1]]T represents the current iteration number, T represents the maximum iteration number, alpha is a constant, determines the position of the disturbance peak, and is set to be 2.5 in the experiment, E represents the escape energy, E represents the maximum escape energy 0 Represents an initial value of the escape energy, which is automatically updated to [ -1, 1] at the beginning of each iteration]A random number in between.
Step 6: a searching stage, updating the individual position according to the global searching updating formula, and executing the step 8; the specific process comprises the following steps: and updating the search stage, wherein the expression is as follows:
Figure BDA0003089557620000083
wherein t represents the number of iterations, X rand (t) random individuals for the t-th iteration, X i (t)、X i (t +1) denotes the position of the ith individual at the current position and at the next iteration, X rabbit (t) represents the position of the prey at the t-th iteration, namely the position of the individual with the optimal current fitness, ub and lb represent the upper and lower bounds of the search space, r 1 、r 2 、r 3 、r 4 Is represented by [0,1]Q is also a random number between 0 and 1, two strategies of random selection with equal probability are represented for updating, and X is mean (t) represents the average position of the t iteration individuals, and is calculated by the following formula:
Figure BDA0003089557620000091
wherein N represents the size of the population.
And 7: in the development stage, a development strategy is selected according to the | E | and r for updating, and the step 8 is executed;
the development strategy is as follows:
firstly, randomly generating a random number r between [0,1 ];
when | E | is more than or equal to 0.5 and less than or equal to 1 and r is more than or equal to 0.5, adopting a soft enclosure strategy to update the position, wherein the position updating formula is as follows:
X i (t+1)＝ΔX i (t)-E|j·X rabbit (t)-X i (t)|
wherein j represents [0,2 ]]Random number in between, Δ X i (t) represents the difference between the prey location and the current individual location, calculated as follows:
ΔX i (t)＝X i (t+1)-X i (t)
when | E | <0.5 and r ≧ 0.5, a hard attack strategy is adopted to update the position, and the position update formula is as follows:
X i (t+1)＝X rabbit (t)-E|ΔX i (t)|
when the | E | is more than or equal to 0.5 and less than 1 and r is less than 0.5, adopting a soft enclosure strategy of gradual quick dive to update the position, wherein the position updating formula is as follows:
Figure BDA0003089557620000092
Y＝X rabbit (t)-E·|j·X rabbit (t)-X i (t)|
Z＝Y+S×levy(dim)
wherein f (·) represents a fitness function, S represents a two-dimensional random vector, and all elements in the random vector are random numbers between [0 and 1], wherein dim represents a dimension, levy (dim) represents a levy flight function, and the formula is as follows:
Figure BDA0003089557620000093
wherein u and mu represent random numbers between [0,1], beta is a constant and takes a value of 1.5, and gamma (1+ beta) represents a gamma function;
when | E | <0.5 and r <0.5, the position is updated by adopting a hard attack strategy of progressive fast dive, and the position update formula is as follows:
Figure BDA0003089557620000094
Y＝X rabbit (t)-E|j·X rabbit (t)-X mean (t)| Z＝Y+S×levy(dim)。
and 8: calculating an updated fitness value, selecting an individual with the best fitness as the optimal prey position of the current iteration, judging whether the fitness is smaller than a threshold value or reaches the maximum iteration number by the iteration number +1, finishing the algorithm if the condition is satisfied, and returning to the step 4 if the condition is not satisfied.
Comparing the positioning results of the ultra-wideband positioning algorithm (ISHHO) based on information sharing Harris eagle optimization, the ultra-wideband positioning algorithm (HHO) based on traditional Harris eagle optimization, the enhanced whale optimized ultra-wideband positioning algorithm (EWOA) and the adaptive differential variation particle swarm optimization ultra-wideband algorithm (APSO-DE), the experimental parameter selection comprises the following steps:
as shown in fig. 2, in the case that the position of the reference node is not changed, a total of 100 nodes are randomly distributed in a monitoring area of 50m × 50m, and all simulation data are averaged after repeating 100 times under the same condition in order to eliminate random errors as much as possible.
Experiment 1: the four algorithms are simulated, the number of the base stations is set to be 8, the noise variance is used for simulating the non-line-of-sight error, the population scale is changed under the conditions that the noise variance is 1 and the iteration times are 60, the relationship between the positioning result error and the population scale is compared through simulation, as shown in figure 3, the positioning accuracy of the algorithm provided by the text is higher under the same population scale, the algorithm provided by the text is not obvious in change of the population scale, and the algorithm tends to be stable from the left to the right of the population scale of 10.
Experiment 2: the four algorithms are simulated, the number of the base stations is set to be 8, the noise variance is utilized to simulate the non-line-of-sight error, the noise variance is 1, the population size is set to be 30, the iteration frequency is increased from 0 to 60, the step length is 10, the relation between the positioning fitness value and the iteration frequency is compared through simulation and is shown in figure 4, the algorithm provided by the method has higher convergence speed, and the algorithm has stronger optimizing capability, lower fitness value after convergence and more excellent positioning performance.
Experiment 3: the four algorithms are simulated, the number of base stations is set to be 8, the population scale is set to be 30, the iteration number is set to be 60, noise variance is utilized to simulate non-line-of-sight errors, under the condition that the noise variance is increased from 0 to 2 and the step length is 0.2, the relation between the root mean square error of positioning and the measurement error is compared through simulation as shown in figure 5, the positioning accuracy of the algorithm provided by the method is higher under the same measurement error, when the variance of the measurement error is 0.2, the positioning accuracy of the algorithm is close to the lower limit of Cramer-Rao and can reach 0.08m, the positioning accuracy is highest in all comparison algorithms, when the measurement error is increased to 2, the positioning error is still lower than 0.77m, the positioning accuracy of an APSO-DE algorithm is improved by 0.07m, and the positioning accuracy of a traditional HHO algorithm is improved by 0.3 m.
Experiment 4: the four algorithms are simulated, the population scale is set to be 30, the iteration times are set to be 60, the noise variance is used for simulating non-line-of-sight errors, the noise variance is set to be 1, the number of base stations is increased from 4 to 8, the relation between the positioning root mean square error and the number of the base stations is compared through simulation, as shown in figure 6, the positioning accuracy of different algorithms is improved along with the increase of the number of the base stations, under the condition of the same number of the base stations, the root mean square error of the ISHHO algorithm provided by the method is smaller than that of other algorithms, and the reduction amplitude of the positioning error is increased along with the increase of the number of the base stations, so that the ISHHO algorithm can well improve the positioning accuracy by using redundancy measurement values, and the positioning performance is excellent in a positioning scene with more base stations.
Experiment 5: the four algorithms are simulated, the population size is set to be 30, the iteration number is set to be 60, the noise variance is used for simulating the non-line-of-sight error, the noise variance is set to be 1, the number of base stations is set to be 8, the cumulative probability of each algorithm is compared through simulation and is shown in figure 7, and it can be seen from the figure that the positioning errors of HHO, EWOA, APSO-DE and ISHHO positioning algorithm in the text can be controlled within 0.5741m, 0.5078m, 0.4334cm and 0.3635m respectively. The HHO, EWOA, APSO-DE and ISHHO algorithm positioning errors at 90% CDF are 0.4906m, 0.4378m, 0.3759m and 0.3240m respectively. Comparing the data, the positioning errors of the ultra-wideband positioning algorithm based on information sharing Harris eagle optimization provided by the invention under the same CDF are smaller than those of other comparison algorithms, the overall accuracy of the algorithm can reach about 0.3635m under the condition that the ranging error variance is 1, compared with the traditional HHO algorithm, the positioning accuracy is improved by 0.2106m, 0.1443m compared with the EWOA algorithm, 0.0699m compared with the APSO-DE algorithm, and the excellent positioning performance is shown under the condition of non-line-of-sight.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and all equivalent variations made by using the contents of the present specification and the drawings are within the protection scope of the present invention.

Claims (7)

1. An ultra-wideband positioning method based on information sharing Harris eagle optimization is characterized by comprising the following steps:
step 1, measuring the distance d between an unknown node and an anchor node by adopting a bilateral two-way distance measurement method i，k ；
Step 2, initializing, setting a maximum iteration number T and a fitness threshold lambda, initializing a population, and replacing an element in the population by using a coarse positioning individual to obtain an improved initial population;
step 3, calculating the individual fitness according to the fitness function calculation method, and selecting the individual with the best fitness as the position X of the prey rabbit (t)；
Step 4, selecting an updating individual according to an information sharing updating mode and updating through the average value of the population individuals and the position information of other individuals;
the specific process of the step 4 is as follows:
step 4-1, determining the number of Harris hawks in the search stage, wherein most of the Harris hawks are required to be explored in the early stage of the algorithm, and the number of the Harris hawks required to be explored after a prey is found is reduced, so that the following relation is established by simulating the way:
Figure FDA0003720767570000011
wherein s _ num represents the current number participating in the search, s _ max and s _ min respectively represent the maximum minimum number participating in the search, T represents the current iteration number, and T represents the maximum iteration number;
step 4-2, obtaining information by an information sharing Harris eagle optimization algorithm: randomly generating a random number in the range of [0,1], if the number is more than or equal to 0.5, acquiring information from the shared area, otherwise, acquiring the information from the partner;
in step 4-2, information is obtained from the shared area:
Figure FDA0003720767570000012
X i (t+1)＝X i (t)+rand()·[X rabbit (t)-β1·X mean (t)]
wherein, X mean (t) represents the mean value of the positions in the population of this iteration, X i (t) represents the location of the ith individual in the tth iteration population, X i (t +1) represents the position of the ith individual in the t +1 th iteration population, and rand () represents [0,1]]Beta 1 represents an information acquisition factor used for determining the amount of information acquired by an individual from a shared area, and the value range is also [0, 1%]；
Step 5, calculating escape energy E according to an escape energy calculation method for increasing sine and cosine disturbance, judging whether the escape energy E enters a search stage or a development stage, and executing step 6 if the escape energy E enters the search stage, or executing step 7 if the escape energy E does not enter the development stage;
step 6: a searching stage, updating the individual position according to the global searching updating formula, and executing the step 8;
and 7: in the development stage, a development strategy is selected according to the | E | and r for updating, and the step 8 is executed;
and 8: calculating an updated fitness value, selecting an individual with the best fitness as the optimal prey position of the current iteration, judging whether the fitness is smaller than a threshold value or reaches the maximum iteration number by the iteration number +1, finishing the algorithm if the condition is satisfied, and returning to the step 4 if the condition is not satisfied.
2. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that the specific process of the step 2 is as follows:
firstly, determining the size N of a population, wherein the problem dimension dim is 2, the upper limit ub of a search space and the lower limit 1b of the search space; randomly generating the positions of individuals in the population by using a formula according to the population scale and the search space limit:
X i ＝rand(1，2)×(ub-lb)+lb
wherein, X i Indicates the position of the ith individual in the population, i belongs to 1-N, and rand (1,2) indicates the randomly generated element [0,1]]And replacing an element in the population by using the coarse positioning individual position information obtained by the TOA mixed trilateral positioning algorithm to obtain an improved initial population.
3. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that in the step 3, the fitness is calculated according to the following fitness function calculation method, and the best fitness individual is selected as the position of the prey, and the best fitness individual is selected as the position X of the prey rabbit (t)；
Figure FDA0003720767570000021
Wherein, f (X) i ) A value representing fitness, K representing the number of ranging measurements, (x) i ，y i ) Indicating the location of the unknown tag node (x) k ，y k ) Indicating the location of the base station, d i，k Represents the distance, X, between the unknown node i and the anchor node K rabbit (t) denotes all individuals X i The medium fitness is the smallest.
4. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that in step 4-2, the collaborators share information:
X i (t+1)＝X i (t)+levy(dim)·[X rabbit (t)-X j (t)]，i≠j
where levy (dim) represents the flight function, dim represents the dimension, X i (t)、X j (t) indicates the location of the different individuals in the population at the t-th iteration.
5. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that the specific process of the step 5 is as follows:
calculating escape energy according to an escape energy calculation method for increasing sine and cosine disturbance represented by the following formula, judging the state, converting into a search transition state when the energy E is more than or equal to 1, and entering a search stage; when the energy | E | is less than 1, the method is converted into a development state and enters a development stage:
Figure FDA0003720767570000031
Figure FDA0003720767570000032
where δ represents a perturbation term and rand () represents [0,1]]T represents the current iteration number, T represents the maximum iteration number, alpha is a constant and determines the position of the disturbance peak, E represents the escape energy, E represents the maximum iteration number 0 Represents the initial value of the escape energy, and is automatically updated to [ -1, 1] at the beginning of each iteration]A random number in between.
6. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that the specific process of the step 6 is as follows: and updating the search stage, wherein the expression is as follows:
Figure FDA0003720767570000033
wherein t represents the number of iterations, X rand (t) random individuals for the t-th iteration, X i (t)、X i (t +1) denotes the position of the ith individual at the current position and at the next iteration, X rabbit (t) indicates the location of the prey at the tth iteration, i.e. of the currently fitness-optimized individualPosition, ub, lb represent the upper and lower bounds of the search space, r 1 、r 2 、r 3 、r 4 Is represented by [0,1]Q is also [0,1]]Random number between, representing the probability of an equal random selection strategy to update, X mean (t) represents the average position of the t iteration individuals, and is calculated by the following formula:
Figure FDA0003720767570000034
wherein N represents the size of the population.
7. The ultra-wideband positioning method based on information sharing harris eagle optimization according to claim 1, characterized in that in step 7, the development strategy is:
firstly, randomly generating a random number r between [0,1 ];
when the absolute value E is more than or equal to 0.5 and less than 1 and r is more than or equal to 0.5, adopting a soft enclosure strategy to update the position, wherein the position updating formula is as follows:
X i (t+1)＝ΔX i (t)-E|j·X rabbit (t)-X i (t)|
wherein j represents [0,2 ]]Random number between, X i (t) represents the location of the ith individual in the tth iteration population, X i (t +1) denotes the position of the ith individual in the t +1 th iteration population, Δ X i (t) represents the difference between the prey location and the current individual location, calculated as follows:
ΔX i (t)＝X i (t+1)-X i (t)
when the | E | is less than 0.5 and r is more than or equal to 0.5, adopting a hard attack strategy to update the position, wherein the position updating formula is as follows:
X i (t+1)＝X rabbit (t)-E|ΔX i (t)|
when the | E | is more than or equal to 0.5 and less than 1 and r is less than 0.5, adopting a gradual fast diving soft enclosure strategy to update the position, wherein the position updating formula is as follows:
Figure FDA0003720767570000041
Y＝X rabbit (t)-E·|j·X rabbit (t)-X i (t)|
Z＝Y+S×levy(dim)
wherein f (·) represents a fitness function, S represents a two-dimensional random vector, and all elements in the random vector are random numbers between [0 and 1], wherein dim represents a dimension, levy (dim) represents a levy flight function, and the formula is as follows:
Figure FDA0003720767570000042
wherein u and mu represent random numbers between [0,1], beta is a constant and takes a value of 1.5, and gamma (1+ beta) represents a gamma function;
when | E | <0.5 and r <0.5, adopting a hard attack strategy of gradual fast dive to update the position, wherein the position update formula is as follows:
Figure FDA0003720767570000043
Y＝X rabbit (t)-E|j·X rabbit (t)-X mean (t)|
Z＝Y+S×levy(dim)
X mean and (t) represents the mean value of the positions in the population of the current iteration.
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