CN107396309A - A kind of wireless sensor network forest localization method - Google Patents

A kind of wireless sensor network forest localization method Download PDF

Info

Publication number
CN107396309A
CN107396309A CN201710565938.1A CN201710565938A CN107396309A CN 107396309 A CN107396309 A CN 107396309A CN 201710565938 A CN201710565938 A CN 201710565938A CN 107396309 A CN107396309 A CN 107396309A
Authority
CN
China
Prior art keywords
mrow
msub
mtd
msubsup
mtr
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201710565938.1A
Other languages
Chinese (zh)
Other versions
CN107396309B (en
Inventor
余乐
易晓梅
莫路锋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang A&F University ZAFU
Original Assignee
Zhejiang A&F University ZAFU
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang A&F University ZAFU filed Critical Zhejiang A&F University ZAFU
Priority to CN201710565938.1A priority Critical patent/CN107396309B/en
Publication of CN107396309A publication Critical patent/CN107396309A/en
Application granted granted Critical
Publication of CN107396309B publication Critical patent/CN107396309B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/14—Determining absolute distances from a plurality of spaced points of known location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a kind of forest wireless sensor network locating method, including：(1) whole localization region is divided into the coefficient of dispersion of different zones by m positioning subregion according to RSSI, and is fitted the RSSI path loss models for establishing each positioning subregionForm the RSSI path loss models R of whole localization regionN；(2) RSSI path loss models R is mergedNWith the log path loss model R of whole localization regionD, obtain forest path loss model R；(3) according to forest path loss model R, multiple wireless senser positioning nodes are determined using subregion trilateration localization method；(4) multiple wireless senser positioning nodes are clustered using k means clustering methods, it is determined that final wireless senser positioning node.This method has taken into full account influence of the complicated deep woods environment to wireless sensor signal intensity, can obtain the positioning to wireless senser exactly.

Description

Wireless sensor network forest positioning method
Technical Field
The invention belongs to the field of forest management, and particularly relates to a Wireless Sensor Network (WSN) forest positioning method.
Background
The positioning problem of the wireless sensor network in the forest has become one of the key problems in the research of the wireless sensor network. At present, the main problems of the WSN forest positioning research are that the positioning environment is complex, and signals have multipath effect, so that the positioning error is larger. In ranging positioning, the signal intensity is seriously interfered by an obstacle, so that ranging is inaccurate, and the traditional path loss model is not suitable for forest positioning. Due to the complexity of the forest environment, the Received Signal Strength (RSSI) error is large, and the current RSSI path loss model cannot meet the requirement of sensor node positioning in the forest.
Disclosure of Invention
In view of the above, the invention provides a forest wireless sensor network positioning method, which fully considers the influence of a complex deep forest environment on the signal intensity of a wireless sensor and can accurately obtain the positioning of the wireless sensor.
The technical scheme of the invention is as follows:
a forest wireless sensor network positioning method comprises the following steps:
(1) dividing the whole positioning area into m positioning sub-areas according to the discrete coefficients of the RSSI in different areas, and fitting and establishing an RSSI path loss model of each positioning sub-areaRSSI path loss model R forming the whole positioning areaN(ii) a j is the serial number of the positioning subarea, and j is 1,2,3, …, m;
(2) fusing the RSSI path loss model RNLogarithmic path loss model R with the whole positioning areaDObtaining a forest path loss model R;
(3) determining a plurality of wireless sensor positioning nodes by adopting a regional trilateration positioning method according to the forest path loss model R;
(4) and clustering the plurality of wireless sensor positioning nodes by adopting a k-means clustering method to determine the final wireless sensor positioning node.
In a forest environment, the environments of different areas are different from each other greatly, uncertainty is increased correspondingly along with the change of signal propagation distance, the interference of a signal by an obstacle is larger, and the dispersion degree of RSSI is increased gradually. Therefore, the method of fitting in different regions according to the discrete coefficients of the RSSI is adopted, so that each RSSI path loss model established by fitting is more consistent with the actual situation, and the accuracy is higher.
The specific steps in the step (1) are as follows:
(1-1) obtaining n discrete RSSI arrays (RSSI) by experimental measurementi,di)，RSSIiIs the ith RSSI value collected, and i is 1,2,3, …, n, diIs equal to RSSIiThe corresponding distance is the length between the acquisition signal point and the signal transmitting node;
(1-2) searching a divided region node by adopting a sliding window, calculating a discrete coefficient of discrete RSSI in the sliding window in real time, and when the discrete coefficient is suddenly changed, taking a distance corresponding to a median value of the discrete RSSI in the sliding window as the divided region node to obtain m positioning sub-regions;
(1-3) fitting the discrete RSSI array in each positioning sub-area to obtain an RSSI path loss model of RSSI along with the change of distance
(1-4) combining m RSSI Path loss modelsObtaining an RSSI path loss model RN：
Wherein for the whole positioning area [0, a ]]，a1,a2,…,aj,…,anTo be divided region nodes.
The transmitting node is a sensor node which actively transmits data frames to other nodes.
RSSI is greatly influenced by environment in forest environment, and logarithmic path loss model RDEnvironmental impact factors are well considered, so a logarithmic distance path loss model R is selectedDAs part of the forest path loss model R.
In a complex environment, a logarithmic distance loss model R is mostly adoptedDThe loss condition of the RSSI is calculated, and the specific model is shown in formula (2):
P(d)＝P0(d0)+10vlg(d/d0)+Xσ(2)
in the formula (2), d0For reference distances, the value is usually 1 m; d is the actual signal propagation distance, i.e. the distance from the transmitting signal node; p0(d0) Is a reference distance d0The path loss of (d); p (d) is the path loss of the signal after the distance d; v is a path loss index, and the magnitude of the v value reflects the change rate of the RSSI along with the increase of the propagation distance and is related to environmental influence factors; xσIs a gaussian random variable with mean 0 and standard deviation σ.
The RSSI at a distance d from the transmitting node is shown in equation (3):
RSSI＝P-P(d) (3)
in formula (3), P is the transmission power of the signal source.
Distance transmitting node d0Reference point path loss P of0(d0) P- ｃA, where ｃA denotes the received signal strength at the reference point, and d01, mixing P0(d0) formulｃA (4) is obtained by substituting formulｃA (2) for P- ｃA, and formulｃA (5) is obtained by substituting formulｃA (3) for formulｃA (4):
P(d)＝P-A+10vlg(d)+Xσ(4)
RSSI＝A-10vlg(d)-Xσ(5)
due to XσThe mean value is 0, thus giving equation (6):
to reduce experimental error, RSSI is measured and averaged multiple timesSubstituting equation (6) to obtain equation (7):
the method for determining the RSSI value A and the environmental impact factor v at the reference point in the logarithmic path loss model comprises the following steps:
in a specific forest environment, taking an RSSI value at a position 1m away from a transmitting node as A;
since the environment varies greatly in different areas, the environmental impact factors vary greatly for each area. Therefore, the invention determines the environmental impact factor v belonging to the jth localization area on the basis of determining each localization areaj：
In the formula (8), d12To belong toAnchor node K in j positioning sub-regions1And K2A is the RSSI value at 1m from the transmitting node,is an anchor node m1、m2The average of the RSSI values. The anchor node is a sensor node determined by specific position coordinates.
Finally determined logarithmic path loss model R of the whole positioning areaDComprises the following steps:
in the step (2), a logarithmic path loss model R of the environmental influence factor is consideredDWith RSSI path loss model R taking into account distance impact factorsN(d) And combining to obtain a forest path loss model R (d) more suitable for the complex deep forest environment:
in the formula (10), αjAnd βjRespectively representAndthe occupied specific gravity coefficient of (2).
Determination of specific gravity coefficient αjAnd βjThe method comprises the following steps:
firstly, in the j sub-area, through a plurality of experiments, a plurality of groups of data (RSSI, d) are collected and the average value of the RSSI values is calculated
Then, from the sets of data (RSSI, d) collected, a model is determined using the method described aboveAnd
finally, equation (11) is established and expressed as f (α)j,βj) The minimum objective is solved for equation (11) and determined αj,βj，
In an actual environment, the RSSI value fluctuates due to the influence of an external environment, and the RSSI value is a direct factor affecting the positioning accuracy, so that the RSSI value needs to be effectively filtered before the specific position of an unknown node is solved.
Preferably, the method further comprises filtering the RSSI using a gaussian filtering model. The Gaussian filtering can effectively reduce the influence of small probability and improve the positioning precision. RSSI obeys (0, sigma)2) A gaussian distribution with a gaussian probability density function as shown in equation (12):
in the formula (12), the first and second groups,RSSI is the signal strength value received by the node.
The high probability interval is (mu-sigma, mu + sigma) according to experience, the occurrence probability of the interval is 0.6826, so that abnormal values outside the interval are excluded, and the RSSI value in the interval is selected as experimental data. The Gaussian filtering partially solves the problems that the RSSI is easy to interfere in the actual environment and the like, and effectively improves the positioning accuracy.
And (3) accurately calculating the distance between the nodes according to the signal intensity between the nodes aiming at the obtained forest path loss model R, and further determining the specific position of the unknown node. Preferably, the method for determining the positioning nodes of the plurality of wireless sensors by adopting the regional trilateration positioning method comprises the following steps: the selected position coordinates are respectively (x)1,y1)、(x2,y2)、(x3,y3) The anchor node A, B, C is used as a signal transmitting node, and distance equations between an unknown node and three anchor nodes are respectively established to form an equation set:
solving equation (13) to determine the location coordinates (x, y), d of the unknown node (wireless sensor positioning node)A,dB,dCRespectively, the distance between the anchor node A, B, C and the target node. And (3) acquiring a plurality of wireless sensor positioning nodes by utilizing a formula (13) through multiple times of positioning in the same area and repeated positioning in different areas.
The plurality of wireless sensor positioning nodes obtained in the step (3) are concentrated in a certain range, most of the positioning nodes are concentrated, the concentration is high, and a small number of the positioning nodes deviate from a positioning node concentrated area. The invention considers that the positioning nodes deviating from the positioning node dense area are some positioning results with larger positioning errors. In order to improve the positioning precision, the invention adopts a K-means clustering method to eliminate errors. The method specifically comprises the following steps:
a plurality of wireless sensor positioning nodes are clustered by using a K-means clustering method, and the cluster center of the cluster with the most positioning nodes is selected as the final wireless sensor positioning node, so that the positioning precision can be greatly improved.
Compared with the prior art, the invention has the beneficial technical effects that:
(1) the invention fuses the path loss model and segments the path loss model according to the complexity of the environment, so that the segmented path loss model can be more suitable for the variability of the environment.
(2) The invention extracts the positioning result by using K-means, eliminates other interference in the positioning process and ensures that the positioning result is more accurate.
Drawings
FIG. 1 is a flow chart of a forest wireless sensor network positioning method of the present invention;
FIG. 2 is a graph of RSSI versus distance d in the present invention;
FIG. 3 is a schematic view of the sub-area positioning and the sub-area trilateration positioning of the present invention;
FIG. 4 is a schematic diagram of the clustering results and the determination of the final wireless sensor positioning node using the K-means clustering method of the present invention;
FIG. 5 is a schematic diagram of RSSI dispersion within 25m from a transmitting node in an open environment;
FIG. 6 is a schematic diagram of RSSI dispersion within 25m from a transmitting node in a bamboo forest environment;
FIG. 7 is a diagram illustrating the positioning result of an unknown node in the embodiment;
FIG. 8 is a diagram illustrating an experimental result of positioning an unknown node by using a K-means algorithm in the embodiment.
Detailed Description
In order to more specifically describe the present invention, the following detailed description is provided for the technical solution of the present invention with reference to the accompanying drawings and the specific embodiments.
Referring to fig. 1, the forest wireless sensor network positioning method of the invention comprises the following steps:
s01, dividing the whole positioning area into m positioning sub-areas according to the discrete coefficients of the RSSI in different areas, and fitting and establishing an RSSI path loss model of each positioning sub-areaRSSI path loss model R forming the whole positioning areaN(ii) a j is the number of the positioning sub-region, j is 1,2,3, …, m.
Referring to fig. 2, the specific steps of S01 are:
(1-1) obtaining a series of discrete RSSI arrays (RSSI) by experimental measurementi,di)，RSSIiIs the ith RSSI value collected, and i is 1,2,3, …, n, diIs equal to RSSIiThe corresponding distance is the length between the acquisition signal point and the signal transmitting node;
(1-2) searching a divided region node by adopting a sliding window, calculating a discrete coefficient of discrete RSSI in the sliding window in real time, and when the discrete coefficient is suddenly changed, taking a distance corresponding to a median value of the discrete RSSI in the sliding window as the divided region node to obtain m positioning sub-regions;
(1-3) fitting the discrete RSSI array in each positioning sub-area to obtain an RSSI path loss model of RSSI along with the change of distance
(1-4) combining m RSSI Path loss modelsObtaining an RSSI path loss model RN(d)：
Wherein for the whole positioning area [0, a ]]，a1,a2,…,anTo be divided region nodes.
S02 fusing the RSSI path loss model RNLogarithmic path loss model R with the whole positioning areaDAnd obtaining a forest path loss model R.
In this step, in a complex environment, a logarithmic distance loss model R is mostly adoptedDThe loss condition of the RSSI is calculated, and the specific model is shown in formula (2):
P(d)＝P0(d0)+10vlg(d/d0)+Xσ(2)
in the formula (2), d0For reference distances, the value is usually 1 m; d is the actual signal propagation distance, i.e. the distance from the transmitting signal node; p0(d0) Is a reference distance d0The path loss of (d); p (d) is the path loss of the signal after the distance d; v is a path loss index, and the magnitude of the v value reflects the change rate of the RSSI along with the increase of the propagation distance and is related to environmental influence factors; xσIs a gaussian random variable with mean 0 and standard deviation σ.
The RSSI at a distance d from the transmitting node is shown in equation (3):
RSSI＝P-P(d) (3)
in formula (3), P is the transmission power of the signal source.
Distance transmitting node d0Reference point path loss P of0(d0) P- ｃA, where ｃA denotes the received signal strength at the reference point, and d01, mixing P0(d0) formulｃA (4) is obtained by substituting formulｃA (2) for P- ｃA, and formulｃA (5) is obtained by substituting formulｃA (3) for formulｃA (4):
P(d)＝P-A+10vlg(d)+Xσ(4)
RSSI＝A-10vlg(d)-Xσ(5)
due to XσThe mean value is 0, thus giving equation (6):
to reduce experimental error, RSSI is measured and averaged multiple timesSubstituting equation (6) to obtain equation (7):
the method for determining the RSSI value A and the environmental impact factor v at the reference point in the logarithmic path loss model comprises the following steps:
in a specific forest environment, taking an RSSI value at a position 1m away from a transmitting node as A;
since the environment varies greatly in different areas, the environmental impact factors vary greatly for each area. Therefore, the invention determines the environmental impact factor v belonging to the jth localization area on the basis of determining each localization areaj：
In the formula (8), d12For anchor node K in jth positioning sub-region1And K2A is the RSSI value at 1m from the transmitting node,is an anchor node m1、m2The average of the RSSI values.
Finally determined logarithmic path loss model R of the whole positioning areaDComprises the following steps:
based on the above, in S02, the logarithmic path loss model R of the environmental impact factor will be consideredDWith RSSI path loss model R taking into account distance impact factorsN(d) And combining to obtain a forest path loss model R (d) more suitable for the complex deep forest environment:
in the formula (10), αjAnd βjRespectively representAndthe occupied specific gravity coefficient of (2).
Determination of specific gravity coefficient α by collecting data through multiple experimentsjAnd βjThe specific process is as follows:
firstly, in the j sub-area, through a plurality of experiments, a plurality of groups of data (RSSI, d) are collected and the average value of the RSSI values is calculated
Then, from the sets of data (RSSI, d) collected, a model is determined using the method described aboveAnd
finally, equation (11) is established and expressed as f (α)j,βj) Minimum sizeSolving equation (11) for the object, and determining αj,βj，
And S03, filtering the RSSI by adopting a Gaussian filtering model.
The Gaussian filtering can effectively reduce the influence of small probability and improve the positioning precision. RSSI obeys (0, sigma)2) A gaussian distribution with a gaussian probability density function as shown in equation (12):
in the formula (12), the first and second groups,RSSI is the signal strength value received by the node.
The high probability interval is (mu-sigma, mu + sigma) according to experience, the occurrence probability of the interval is 0.6826, so that abnormal values outside the interval are excluded, and the RSSI value in the interval is selected as experimental data. The Gaussian filtering partially solves the problems that the RSSI is easy to interfere in the actual environment and the like, and effectively improves the positioning accuracy.
And S04, determining a plurality of wireless sensor positioning nodes by adopting a regional trilateration positioning method according to the forest path loss model R.
In this step, the specific position of the unknown node is determined by using a partition trilateration positioning method, which specifically comprises the following steps: referring to fig. 3, the coordinates of the selected positions are (x) respectively1,y1)、(x2,y2)、(x3,y3) The anchor node A, B, C is used as a signal transmitting node, and distance equations between the unknown node O and the three anchor nodes are respectively established to form an equation set:
solving equation (13) to determine the location coordinates (x, y), d of the unknown node (wireless sensor positioning node)A,dB,dCRespectively, the distance between the anchor node A, B, C and the target node. And (3) acquiring a plurality of wireless sensor positioning nodes by utilizing a formula (13) through multiple times of positioning in the same area and repeated positioning in different areas.
And S05, clustering the plurality of wireless sensor positioning nodes by adopting a k-means clustering method, and determining the final wireless sensor positioning node.
In this step, referring to fig. 4, a K-means clustering method is used to cluster a plurality of wireless sensor positioning nodes, and a cluster center of a cluster with the most positioning nodes is selected as a final wireless sensor positioning node, so that the positioning accuracy can be greatly improved.
Examples
The Telosb sensor nodes are adopted for experiments, 15 anchor nodes are uniformly deployed in a 30 m-30 m bamboo forest, and 5 unknown nodes are randomly deployed. The Telosb sensor node sends the information to a computer end for positioning experiment. The experimental scene is a bamboo forest with uneven density, and the sensor nodes are placed on a shelf with the height of 1.2m in the experiment, so that the sensor nodes are ensured to be arranged on the same height level.
Dividing a positioning area:
because the density of the trees in different areas in the forest environment is greatly different, if the trees are not distinguished in the positioning experiment, the positioning accuracy is greatly influenced. The positioning area is divided by using the discrete conditions of the RSSI in different areas, and the density of trees in the same area is very close. The RSSI dispersion within 25m from the transmitting node was experimentally tested. Since the main factor influencing the RSSI is the density of the trees, in the experiment, taking a bamboo forest as an example, the open environment and the bamboo forest environment are compared, the bamboo forest environment is processed in the experiment, four kinds of bamboo forests with different densities are arranged, the experimental results are shown in fig. 5 and 6, fig. 5 is the open environment result, fig. 6 is the bamboo forest environment result, the abscissa is the distance between the nodes, and the ordinate is the RSSI value.
In the experiment, the RSSI discrete coefficient is calculated by using a sliding window technology, the discrete coefficients are relatively close in an open field environment, the values of the discrete coefficients fluctuate from-0.018 to-0.018, the discrete coefficients obviously fluctuate three times in a bamboo forest environment, and the distances from a transmitting node are respectively 5.5m, 12.5m and 19.5 m. The three positions divide the bamboo forest into four areas, and the corresponding RSSI discrete coefficients are-0.051, -0.037, -0.026 and-0.008 respectively. Experimental results show that the division of the forest positioning region by the RSSI discrete coefficient is reliable.
And (3) regional positioning:
the positioning accuracy of the logarithmic path loss model under the condition of zoning and non-zoning is compared. The division of the positioning areas in the bamboo forest is determined through an area division experiment, and the environmental influence factors v of each area are respectively 2.96, 1.96, 2.61 and 2.71. The environmental impact factor v without demarcated areas is 2.42 and the reference value a is-56.59. In the experiment, three anchor nodes are randomly selected to position 5 unknown nodes for 30 times under the condition of not dividing the area, and the 5 unknown nodes are positioned according to the positioning method of S04 under the condition of dividing the area. The experimental results are shown in fig. 7, with the abscissa representing the number of experiments (experimental number) and the ordinate representing the positioning error (development).
In the experiment, when the positioning area is not divided: the accuracy (Non-resolution-Kmeans) is improved without adopting a K-means algorithm, and the positioning error is about 3.8 m; the positioning precision can be improved by about 0.3m by adopting a K-means algorithm (Non-summary).
When the positioning area is divided: and respectively calculating the environmental influence factors of different areas, and selecting anchor nodes in the different areas for positioning. If the K-means algorithm () Subrection is not adopted), the positioning error is about 2.5 m; if a K-means algorithm (Subretion-Kmeans) is adopted, the positioning precision can be improved by about 0.2m, and the positioning precision is greatly fluctuated under the condition of no region division, so that the positioning is more unstable.
The experimental result shows that the positioning precision can be greatly improved by dividing the positioning area according to the RSSI discrete coefficient, because the different areas have different bamboo densities, the environmental influence factors are different, the key of the distance measurement by utilizing the logarithmic path loss model is whether the environmental influence factors are accurate, and the experimental result verifies that the K-means algorithm can effectively improve the positioning precision.
Fusion model:
a fitting Model (Fusion Model) is established by adopting actually measured data, and then the fitting Model is fused with a logarithmic Path Loss Model (Path Loss Model), so that the established Model is more suitable for complex environments. Calculating alpha and beta values of each region to be (5.79, -5.05), (-9.42,10.01), (-3.85,4.95), (10.88, -9.99) respectively according to the method of S02, then establishing a fusion path loss model, performing regional positioning on 5 unknown nodes in the positioning region, wherein the environmental impact factors v of each region in the experiment are 2.96, 1.96, 2.61, 2.71 respectively, the reference value A is-56.59, each result is a result of positioning 30 times, repeating the positioning experiment for 10 times, and the positioning accuracy is improved by using a K-means algorithm in the experiment, and the experiment result is shown in FIG. 8. The abscissa is the number of positioning experiments (Experiment number), and the ordinate is the positioning error (development) (average positioning error of 5 unknown nodes), and the unit is meter (m).
The experimental result shows that the fusion model can better describe the signal loss condition than the number path loss model under the condition of regional positioning. By utilizing the fusion model, a more accurate positioning result can be obtained according to the signal intensity. Because the fitting model is more consistent with the positioning environment, but the fitting model lacks applicability, the positioning accuracy is reduced by replacing the positioning environment, and the fitting model needs to be reestablished. In order to increase the applicability of the fusion model, the RSSI path loss fusion model with reliable positioning accuracy and applicability is established by fusing a logarithmic path loss model and a fitting model.
The above-mentioned embodiments are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only the most preferred embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions, equivalents, etc. made within the scope of the principles of the present invention should be included in the scope of the present invention.

Claims (9)

1. A forest wireless sensor network positioning method comprises the following steps:
(1) dividing the whole positioning area into m positioning sub-areas according to the discrete coefficients of the RSSI in different areas, and fitting and establishing an RSSI path loss model of each positioning sub-areaRSSI path loss model R forming the whole positioning areaN(ii) a j is the serial number of the positioning subarea, and j is 1,2,3, …, m;
(2) fusing the RSSI path loss model RNLogarithmic path loss model R with the whole positioning areaDObtaining a forest path loss model R;
(3) determining a plurality of wireless sensor positioning nodes by adopting a regional trilateration positioning method according to the forest path loss model R;
(4) and clustering the plurality of wireless sensor positioning nodes by adopting a k-means clustering method to determine the final wireless sensor positioning node.
2. A forest wireless sensor network positioning method as claimed in claim 1, wherein the specific steps in the step (1) are as follows:
(1-1) obtaining n discrete RSSI arrays (RSSI) by experimental measurementi,di)，RSSIiIs the ith RSSI value collected, and i is 1,2,3, …, n, diIs equal to RSSIiThe corresponding distance is the length between the acquisition signal point and the signal transmitting node;
(1-2) searching a divided region node by adopting a sliding window, calculating a discrete coefficient of discrete RSSI in the sliding window in real time, and when the discrete coefficient is suddenly changed, taking a distance corresponding to a median value of the discrete RSSI in the sliding window as the divided region node to obtain m positioning sub-regions;
(1-3) fitting the discrete RSSI array in each positioning sub-area to obtain an RSSI path loss model of RSSI along with the change of distance
(1-4) combining m RSSI Path loss modelsObtaining an RSSI path loss model RN：
<mrow> <msup> <mi>R</mi> <mi>N</mi> </msup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mn>1</mn> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mn>2</mn> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mn>...</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mi>j</mi> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow></mrow> </mtd> <mtd> <mn>...</mn> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mi>m</mi> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <mi>a</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </mrow>
Wherein for the whole positioning area [0, a ]]，a1,a2,…,aj,…,anTo be divided region nodes.
3. A forest wireless sensor network positioning method as claimed in claim 2, characterised in that the logarithmic path loss model R of the whole positioning areaDComprises the following steps:
<mrow> <msup> <mi>R</mi> <mi>D</mi> </msup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mn>1</mn> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>:</mo> <mi>d</mi> <mo>=</mo> <msup> <mn>10</mn> <mfrac> <mrow> <mi>A</mi> <mo>-</mo> <mover> <mrow> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <mn>10</mn> <msub> <mi>v</mi> <mn>1</mn> </msub> </mrow> </mfrac> </msup> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mn>2</mn> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>:</mo> <mi>d</mi> <mo>=</mo> <msup> <mn>10</mn> <mfrac> <mrow> <mi>A</mi> <mo>-</mo> <mover> <mrow> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <mn>10</mn> <msub> <mi>v</mi> <mn>2</mn> </msub> </mrow> </mfrac> </msup> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mi>j</mi> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>:</mo> <mi>d</mi> <mo>=</mo> <msup> <mn>10</mn> <mfrac> <mrow> <mi>A</mi> <mo>-</mo> <mover> <mrow> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <mn>10</mn> <msub> <mi>v</mi> <mi>j</mi> </msub> </mrow> </mfrac> </msup> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msubsup> <mi>R</mi> <mi>m</mi> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>:</mo> <mi>d</mi> <mo>=</mo> <msup> <mn>10</mn> <mfrac> <mrow> <mi>A</mi> <mo>-</mo> <mover> <mrow> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <mn>10</mn> <msub> <mi>v</mi> <mi>m</mi> </msub> </mrow> </mfrac> </msup> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <mi>a</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>2</mn> <mo>)</mo> </mrow> </mrow>
wherein,log path loss model for jth sub-region, A represents received signal strength at reference point, vjThe environmental impact factor of the jth location sub-region.
4. A forest wireless sensor network locating method as claimed in claim 3, characterised in that A and v are as defined in claim 3jThe determination method comprises the following steps:
taking the RSSI value at a position 1m away from the transmitting node as A;
environmental impact factor v belonging to the jth localization sub-regionj：
<mrow> <msub> <mi>v</mi> <mi>j</mi> </msub> <mo>=</mo> <mfrac> <mrow> <mi>A</mi> <mo>-</mo> <mover> <mrow> <msub> <mi>RSSI</mi> <mn>12</mn> </msub> </mrow> <mo>&amp;OverBar;</mo> </mover> </mrow> <mrow> <mn>10</mn> <msub> <mi>lgd</mi> <mn>12</mn> </msub> </mrow> </mfrac> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow>
In the formula (3), d12For anchor node K in jth positioning sub-region1And K2A is the RSSI value at 1m from the transmitting node,is an anchor node m1、m2The average of the RSSI values.
5. A forest wireless sensor network locating method as claimed in claim 3, characterised in that the forest path loss model R is:
<mrow> <mi>R</mi> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mn>1</mn> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mn>1</mn> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;beta;</mi> <mn>1</mn> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mn>1</mn> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <mn>0</mn> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>1</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mn>2</mn> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mn>2</mn> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;beta;</mi> <mn>2</mn> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mn>2</mn> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mn>1</mn> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mn>2</mn> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mi>j</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>j</mi> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;beta;</mi> <mi>j</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>j</mi> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>j</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <msub> <mi>a</mi> <mi>j</mi> </msub> </mrow> </mtd> </mtr> <mtr> <mtd> <mn>...</mn> </mtd> <mtd> <mrow></mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msub> <mi>&amp;alpha;</mi> <mi>m</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>m</mi> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;beta;</mi> <mi>m</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>m</mi> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>,</mo> </mrow> </mtd> <mtd> <mrow> <msub> <mi>a</mi> <mrow> <mi>m</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>&amp;le;</mo> <mi>d</mi> <mo>&lt;</mo> <mi>a</mi> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>4</mn> <mo>)</mo> </mrow> </mrow>
in the formula (4), αjAnd βjRespectively representAndthe occupied specific gravity coefficient of (2).
6. A forest wireless sensor network locating method as claimed in claim 5, characterised in that the specific gravity coefficient α is determinedjAnd βjThe method comprises the following steps:
firstly, in the j sub-area, through a plurality of experiments, a plurality of groups of data (RSSI, d) are collected and the average value of the RSSI values is calculated
Then, from the sets of data (RSSI, d) collected, a model is determined using the method described aboveAnd
finally, equation (5) is established and expressed as f (α)j,βj) The minimum objective is solved for equation (5) and determined αj,βj，
<mrow> <mi>f</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;alpha;</mi> <mi>j</mi> </msub> <mo>,</mo> <msub> <mi>&amp;beta;</mi> <mi>j</mi> </msub> <mo>)</mo> </mrow> <mo>=</mo> <mo>|</mo> <msub> <mi>&amp;alpha;</mi> <mi>j</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>j</mi> <mi>N</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>+</mo> <msub> <mi>&amp;beta;</mi> <mi>j</mi> </msub> <mo>&amp;times;</mo> <msubsup> <mi>R</mi> <mi>j</mi> <mi>D</mi> </msubsup> <mrow> <mo>(</mo> <mi>d</mi> <mo>)</mo> </mrow> <mo>-</mo> <mover> <mrow> <mi>R</mi> <mi>S</mi> <mi>S</mi> <mi>I</mi> </mrow> <mo>&amp;OverBar;</mo> </mover> <mo>|</mo> <mo>.</mo> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>5</mn> <mo>)</mo> </mrow> </mrow>
7. A forest wireless sensor network locating method as claimed in claim 1, characterised in that the method further comprises filtering the RSSI using a gaussian filtering model.
8. The forest wireless sensor network positioning method of claim 1, wherein the determining the plurality of wireless sensor positioning nodes by using a regional trilateration positioning method specifically comprises:
the selected position coordinates are respectively (x)1,y1)、(x2,y2)、(x3,y3) The anchor node A, B, C is used as a signal transmitting node, and distance equations between an unknown node and three anchor nodes are respectively established to form an equation set:
<mrow> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>1</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msubsup> <mi>d</mi> <mi>A</mi> <mn>2</mn> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>2</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msubsup> <mi>d</mi> <mi>B</mi> <mn>2</mn> </msubsup> </mrow> </mtd> </mtr> <mtr> <mtd> <mrow> <msup> <mrow> <mo>(</mo> <mi>x</mi> <mo>-</mo> <msub> <mi>x</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <msup> <mrow> <mo>(</mo> <mi>y</mi> <mo>-</mo> <msub> <mi>y</mi> <mn>3</mn> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>=</mo> <msubsup> <mi>d</mi> <mi>C</mi> <mn>2</mn> </msubsup> </mrow> </mtd> </mtr> </mtable> </mfenced> <mo>-</mo> <mo>-</mo> <mo>-</mo> <mrow> <mo>(</mo> <mn>6</mn> <mo>)</mo> </mrow> </mrow>
solving equation (6) to determine the location coordinates (x, y), d of the unknown nodeA,dB,dCThe distances between anchor node A, B, C and the target node, respectively;
and (3) obtaining a plurality of wireless sensor positioning nodes by utilizing the formula (6) through multiple times of positioning in the same area and repeated positioning in different areas.
9. The forest wireless sensor network positioning method of claim 1, wherein in the step (4), a plurality of wireless sensor positioning nodes are clustered by using a K-means clustering method, and a cluster center of a cluster with the most positioning nodes is selected as a final wireless sensor positioning node.
CN201710565938.1A 2017-07-12 2017-07-12 A kind of wireless sensor network forest localization method Active CN107396309B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710565938.1A CN107396309B (en) 2017-07-12 2017-07-12 A kind of wireless sensor network forest localization method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710565938.1A CN107396309B (en) 2017-07-12 2017-07-12 A kind of wireless sensor network forest localization method

Publications (2)

Publication Number Publication Date
CN107396309A true CN107396309A (en) 2017-11-24
CN107396309B CN107396309B (en) 2019-10-01

Family

ID=60340529

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710565938.1A Active CN107396309B (en) 2017-07-12 2017-07-12 A kind of wireless sensor network forest localization method

Country Status (1)

Country Link
CN (1) CN107396309B (en)

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108398662A (en) * 2018-01-23 2018-08-14 佛山市顺德区中山大学研究院 A method of improving spatial positioning accuracy
CN108594175A (en) * 2018-04-16 2018-09-28 中国林业科学研究院森林生态环境与保护研究所 Object localization method in a kind of fire prevention of forest aviation
CN109375163A (en) * 2018-08-31 2019-02-22 福建三元达网络技术有限公司 A kind of high-precision indoor orientation method and terminal
CN114629578A (en) * 2021-11-05 2022-06-14 成都市以太节点科技有限公司 Forest region signal propagation path loss model construction method and device, electronic equipment and storage medium
US20220397413A1 (en) * 2021-06-15 2022-12-15 Hyundai Motor Company Augmented Reality Based Point of Interest Guide Device and Method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070132577A1 (en) * 2005-12-09 2007-06-14 Honeywell International Inc. Method and apparatus for estimating the location of a signal transmitter
CN101715232A (en) * 2009-11-20 2010-05-26 西安电子科技大学 Positioning method of weighted wireless sensor network nodes based on RSSI and LQI
CN102761913A (en) * 2011-04-26 2012-10-31 航天信息股份有限公司 Positioning method of wireless signal transmission parameter determination based on area division
CN103889057A (en) * 2014-04-18 2014-06-25 上海海事大学 Wireless sensor network search-and-rescue target location method based on maritime environment self-adaptation RSST distance measurement

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070132577A1 (en) * 2005-12-09 2007-06-14 Honeywell International Inc. Method and apparatus for estimating the location of a signal transmitter
CN101715232A (en) * 2009-11-20 2010-05-26 西安电子科技大学 Positioning method of weighted wireless sensor network nodes based on RSSI and LQI
CN102761913A (en) * 2011-04-26 2012-10-31 航天信息股份有限公司 Positioning method of wireless signal transmission parameter determination based on area division
CN103889057A (en) * 2014-04-18 2014-06-25 上海海事大学 Wireless sensor network search-and-rescue target location method based on maritime environment self-adaptation RSST distance measurement

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108398662A (en) * 2018-01-23 2018-08-14 佛山市顺德区中山大学研究院 A method of improving spatial positioning accuracy
CN108594175A (en) * 2018-04-16 2018-09-28 中国林业科学研究院森林生态环境与保护研究所 Object localization method in a kind of fire prevention of forest aviation
CN109375163A (en) * 2018-08-31 2019-02-22 福建三元达网络技术有限公司 A kind of high-precision indoor orientation method and terminal
CN109375163B (en) * 2018-08-31 2021-04-09 福建三元达网络技术有限公司 High-precision indoor positioning method and terminal
US20220397413A1 (en) * 2021-06-15 2022-12-15 Hyundai Motor Company Augmented Reality Based Point of Interest Guide Device and Method
CN114629578A (en) * 2021-11-05 2022-06-14 成都市以太节点科技有限公司 Forest region signal propagation path loss model construction method and device, electronic equipment and storage medium
CN114629578B (en) * 2021-11-05 2024-04-16 成都市以太节点科技有限公司 Forest signal propagation path loss model construction method and device, electronic equipment and storage medium

Also Published As

Publication number Publication date
CN107396309B (en) 2019-10-01

Similar Documents

Publication Publication Date Title
CN107396309B (en) A kind of wireless sensor network forest localization method
CN109963287B (en) Antenna direction angle optimization method, device, equipment and medium
CN103209478B (en) Based on the indoor orientation method of classification thresholds and signal strength signal intensity weight
CN105629198B (en) The indoor multi-target tracking method of fast search clustering algorithm based on density
CN103747419B (en) A kind of indoor orientation method based on signal strength difference and dynamic linear interpolation
CN105635964A (en) Wireless sensor network node localization method based on K-medoids clustering
CN106066470B (en) A kind of gross error recognition methods of mobile target RSSI positioning
EP2976900B1 (en) Building floor determination for a location based service
CN110213003B (en) Wireless channel large-scale fading modeling method and device
CN105407529B (en) Localization Algorithm for Wireless Sensor Networks based on fuzzy C-means clustering
CN103491591B (en) Zoning method and node positioning method for complicated zone of wireless sensor network
CN104159297B (en) A kind of polygon localization method of wireless sensor network based on cluster analysis
CN108307301A (en) Indoor orientation method based on RSSI rangings and track similitude
CN112462329B (en) Centroid positioning improvement-based wireless sensor network node positioning algorithm
CN112484625B (en) High-precision displacement measurement method based on UWB channel impulse response
CN103533647A (en) Radio frequency map self-adaption positioning method based on clustering mechanism and robust regression
CN104902567A (en) Centroid localization method based on maximum likelihood estimation
CN105307118A (en) Node localization method based on centroid iterative estimation
Wang et al. Research on APIT and Monte Carlo method of localization algorithm for wireless sensor networks
CN108650706A (en) Sensor node positioning method based on second order Taylors approximation
CN112444778A (en) Reference point weighted trilateral centroid positioning method based on DBSCAN
Xu et al. A hybrid approach using multistage collaborative calibration for wireless sensor network localization in 3D environments
US20240292365A1 (en) Data processing method for node localization in wireless sensor networks
CN104301996A (en) Wireless sensor network positioning method
CN108574927B (en) Mobile terminal positioning method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant