CN-122028170-A - Positioning method of wireless sensor

CN122028170ACN 122028170 ACN122028170 ACN 122028170ACN-122028170-A

Abstract

The application discloses a positioning method of a wireless sensor. The method comprises the steps of determining a weight matrix according to a measured distance between a target node and an anchor node in a wireless sensor network, determining a probability distribution matrix according to a ratio of element values in the weight matrix to target summation results, determining an initialization coordinate of each target node in the probability distribution matrix, determining an optimal coordinate with the minimum error value in the initialization coordinates of all individuals for each target node to obtain the optimal coordinate of all target nodes, generating optimal individuals based on the optimal coordinates of all target nodes, replacing any individual in the initial population with the optimal individual to obtain a new population, outputting the prediction coordinates of the target nodes, and determining the position of the target node in the wireless sensor network according to the prediction coordinates. The application solves the technical problem of lower positioning accuracy caused by the dependence of the node positioning method in the related wireless sensor network on the wireless signal parameters.

Inventors

DAI ZHONGBIN
WU YAHUI
WANG XIA
ZHANG ZIXUAN
LIU CHENGHUA

Assignees

中国电信股份有限公司

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (9)

1. A method of locating a wireless sensor, comprising: Determining a weight matrix according to a measured distance between a target node and an anchor node in a wireless sensor network, wherein the anchor node is used for representing a wireless sensor with a known coordinate, the target node is used for representing the wireless sensor to be positioned which has a communication connection relation with the anchor node, and a row of the weight matrix is used for representing a weight coefficient between the same target node and different anchor nodes, and the weight coefficient is inversely related to the measured distance; Determining a probability distribution matrix according to the ratio of the element values in the weight matrix to a target summation result, wherein the target summation result is the summation result of all element values in the row of the element values, and the row of the probability distribution matrix is used for representing the probability that different anchor nodes are selected by the same target node; For each individual in the population in the locust optimization algorithm, repeating the following steps for a plurality of times to obtain an initial population, wherein for each target node in the probability distribution matrix, three anchor nodes are selected in the probability distribution matrix through roulette, and three-point positioning is performed based on the real coordinates of the three anchor nodes to obtain the initialization coordinates of each target node, wherein each individual comprises the initialization coordinates of all the target nodes; For each target node, determining the optimal coordinate with the minimum error value in the initialized coordinates of all the individuals to obtain the optimal coordinate of all the target nodes, generating optimal individuals based on the optimal coordinates of all the target nodes, replacing any individual in the initial population with the optimal individuals to obtain a new population, iterating, evolving and updating the new population, outputting the predicted coordinate of the target node under the condition that the preset termination condition is met, and determining the position of the target node in the wireless sensor network according to the predicted coordinate.
2. The method of claim 1, wherein determining the weight matrix based on the measured distance between the target node and the anchor node in the wireless sensor network comprises: Determining a distance matrix according to the measured distances between the target node and the anchor nodes, wherein the rows of the distance matrix are used for representing the measured distances between the same target node and different anchor nodes; Determining a minimum value in each row in the distance matrix; Determining target element values of all elements in the ith row according to the ratio of the minimum value in the ith row to the values of all elements in the ith row to obtain target element values of all elements in the N rows, wherein i is a positive integer not greater than N, N is the number of the target nodes, and N is a positive integer greater than 1; and determining the weight matrix according to the target element values of the elements in the N rows.
3. The method of claim 1, wherein selecting three anchor nodes in the probability distribution matrix by roulette and performing three-point positioning based on real coordinates of the three anchor nodes, obtaining initialized coordinates of each of the target nodes, comprises: selecting an ith row element from the probability distribution matrix, wherein i is a positive integer not greater than N, N is the number of the target nodes, and N is a positive integer greater than 1; In the i-th row element, three anchor nodes are selected through roulette; acquiring the real coordinates of the three anchor nodes; And processing the real coordinates of the three anchor nodes by using a three-point positioning algorithm to obtain the initialized coordinates of the ith target node.
4. A method according to claim 3, wherein in the i-th row element, three anchor nodes are selected by roulette, comprising: Determining an fitness function, and calculating the fitness value of each anchor node in the ith row element based on the fitness function; normalizing the fitness value to obtain a selection probability value of each anchor node; Constructing a continuous probability interval according to the selection probability value of each anchor node, and distributing a corresponding subinterval for each anchor node in the continuous probability interval; Generating three random numbers, wherein each random number falls into a target position in the continuous probability interval; and determining anchor nodes associated with subintervals corresponding to the target positions where the three random numbers fall as the three anchor nodes.
5. The method of claim 4, wherein determining a fitness function comprises: And determining the fitness function according to the mean square error of a first distance and a second distance, wherein the first distance is the measured distance between the target node and the anchor node, and the second distance is the distance between the estimated coordinate of the target node and the real coordinate of the anchor node.
6. A method according to claim 3, wherein processing the real coordinates of the three anchor nodes to obtain initialized coordinates of the ith target node comprises: based on the real coordinates of the three anchor nodes, establishing a geometric constraint equation set taking the target node coordinates of the ith target node as unknowns; Converting the geometric constraint equation set into a target matrix; and performing matrix operation on the target matrix to obtain the initialization coordinate of the ith target node.
7. The method of claim 1, further comprising, in the event that the preset termination condition is not met, repeating the steps of: Updating a decremental coefficient according to the current evolution state, wherein the decremental coefficient is a control parameter gradually reduced in the iterative process; based on the updated decremental coefficient, performing individual position updating operation on the current parent population, and regenerating a child population; calculating the fitness value of each individual in the offspring population; determining new optimal individuals from the offspring population according to the fitness values of the individuals in the offspring population; comparing the new optimal individual with a historical global optimal individual, and updating the historical global optimal individual according to a comparison result; And executing elite retention strategies on the current parent population and the child population, selecting a preset number of optimal individuals from the current parent population and the child population based on the fitness value of the individuals to form a new population, and taking the new population as the parent population of the next iteration cycle.
8. The method of claim 7, wherein performing an individual location update operation on the current parent population comprises: acquiring a unified upper bound value of a solution space, a unified lower bound value of the solution space and the decrementing coefficient; Calculating a social interaction resultant force vector born by each individual based on the relative position relation among individuals in the current parent population, wherein the relative position relation comprises Euclidean distance among the individuals and a unit direction vector; Multiplying the social interaction resultant force vector by the decreasing coefficient to obtain a new position vector of each individual; comparing each component of the new position vector with the unified upper bound and the unified lower bound value of the solution space, and carrying out constraint processing on the components exceeding the boundary; the constrained new position vectors are combined into a new population of offspring.
9. The method of claim 1, wherein for each of the target nodes, determining the optimal coordinates with the smallest error value among the initialized coordinates of all the individuals to obtain the optimal coordinates of all the target nodes, comprises: Determining an initial distance between each target node and the anchor node according to the initial coordinates of each target node and the real coordinates of the anchor node; Calculating the error value between the initial distance and a target measured distance, wherein the target measured distance is the measured distance between the target node and the anchor node; And for each target node, determining the optimal coordinates which minimize the error value in the initialized coordinates of all the individuals so as to obtain the optimal coordinates of all the target nodes.

Description

Positioning method of wireless sensor Technical Field The application relates to the technical field of signal processing, in particular to a positioning method of a wireless sensor. Background In a wireless sensor network, node positioning is a precondition for realizing many applications, and the positioning technology generally depends on wireless signal parameters such as received signal strength indication, arrival time, time difference and the like. However, the wireless signal may be affected by multipath effect, obstruction, antenna directivity, environmental noise, and other factors during the propagation process, so that the positioning accuracy is not high. In view of the above problems, no effective solution has been proposed at present. Disclosure of Invention The application provides a positioning method of a wireless sensor, which at least solves the technical problem of lower positioning accuracy caused by the fact that a node positioning method in a related wireless sensor network depends on wireless signal parameters. According to one aspect of the application, a positioning method of a wireless sensor is provided, which comprises the steps of determining a weight matrix according to a measured distance between a target node and an anchor node in a wireless sensor network, wherein the anchor node is used for representing a wireless sensor with known coordinates, the target node is used for representing the wireless sensor to be positioned which has a communication connection relation with the anchor node, and the row of the weight matrix is used for representing weight coefficients between the same target node and different anchor nodes, and the weight coefficients are inversely related to the measured distance; determining a probability distribution matrix according to the ratio of element values in the weight matrix to target summation results, wherein the target summation results are summation results of all element values in the row of the element values, the row of the probability distribution matrix is used for representing the probability that different anchor nodes are selected by the same target node, repeatedly executing the following processes for each individual in the population in the locust optimization algorithm to obtain an initial population, selecting three anchor nodes in the probability distribution matrix through roulette for each target node in the probability distribution matrix, performing three-point positioning based on the real coordinates of the three anchor nodes to obtain the initialization coordinates of each target node, wherein each individual comprises the initialization coordinates of all target nodes, determining the optimal coordinates with the minimum error value in the initialization coordinates of all the individual for each target node to obtain the optimal coordinates of all the target nodes, generating the optimal individual based on the optimal coordinates of all the target nodes, replacing any individual in the initial population with the optimal individual to obtain a new population, evolving, updating the new population, outputting the predicted target nodes under the condition of meeting the preset termination condition, and determining the position of the target node in the wireless sensor network according to the predicted coordinates. The method comprises the steps of determining a weight matrix according to a measured distance between a target node and an anchor node, determining a distance matrix according to the measured distance between the target node and the anchor node, wherein a row of the distance matrix is used for representing the measured distance between the same target node and different anchor nodes, determining a minimum value in each row in the distance matrix, determining a target element value of each element in an ith row according to a ratio of the minimum value in the ith row to each element value in the ith row, and obtaining target element values of each element in N rows, wherein i is a positive integer not greater than N, N is the number of the target nodes, N is a positive integer greater than 1, and determining the weight matrix according to the target element values of each element in the N rows. The method comprises the steps of selecting three anchor nodes in a probability distribution matrix through roulette, and carrying out three-point positioning based on real coordinates of the three anchor nodes to obtain initialization coordinates of each target node, wherein in the probability distribution matrix, an ith row element is selected, i is a positive integer which is not more than N, N is the number of the target nodes, N is a positive integer which is more than 1, in the ith row element, three anchor nodes are selected through roulette, the real coordinates of the three anchor nodes are obtained, and the real coordinates of the three anchor nodes are processed through a three-point positioning algorithm t