CN101466146A - Multi-target orientation method of wireless sensor network based on probability weighting - Google Patents
Multi-target orientation method of wireless sensor network based on probability weighting Download PDFInfo
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- CN101466146A CN101466146A CNA2009100450040A CN200910045004A CN101466146A CN 101466146 A CN101466146 A CN 101466146A CN A2009100450040 A CNA2009100450040 A CN A2009100450040A CN 200910045004 A CN200910045004 A CN 200910045004A CN 101466146 A CN101466146 A CN 101466146A
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
Disclosed is a multiple objective positioning method based on the probability weight in the wireless sensor network, which belongs to the wireless communication technology field. In the invention, gridding division is carried out on the plane area in which the sensor network is distributed, and a gridding probability weight model is established according to the relative position of the gridding and sensor nodes; the sensor nodes detect the objective and gives a certain weight to each gridding according to the probability weight model; and each gridding can sum up the weights given to the gridding by different sensor nodes; the summation of the weights exceeds a gridding coordinate with the certain threshold, namely the objective coordinate, so as to realize the objective positioning. The method avoids the problem of the accumulated error commonly seen in the distributed-type multiple objective positioning, improves the performance of the multiple objective positioning, saves the energy consumption, and correspondingly reduces the requirements for sensor devices.
Description
Technical field
The present invention relates to a kind of method of wireless communication technology field, specifically is based on the multi-target orientation method of probability weight in a kind of wireless sensor network.
Background technology
In wireless sensor network, wireless senser usually is arranged in artificially carries out various monitoring tasks in the varying environment, wherein, be divided into two big classes: based on the method for ranging technology and the method for non-distance measuring technology to the many employings of the research of the target localization method identical with the transducer self poisoning.The former is by the distance or the angle information of point-to-point between measured node, use trilateration, triangulation or maximum likelihood estimate computing node position, this class methods node locating precision is higher, can adopt several different methods to reduce range error to location influence, but need to produce a large amount of calculating and communication overhead, and node is adjusted the distance and the measure error of angle is bigger to location precision; The latter need not distance and angle information, only realizes that according to information such as network connectivties this class methods positioning accuracy is lower, the density of sensor node and unknown object all can influence positioning accuracy, but its simplicity of design, calculating and communication overhead are few, and lower to hardware requirement.
Find by prior art documents, number of patent application is 200710018566.7 Chinese patent, a kind of distributed wireless sensor network node self positioning method has been proposed, its process is: the anchor node in the wireless sensor network sends to all nodes of the whole network with self coordinate, node ID number, corrected value, jumping figure information, makes the whole network obtain the self information of all anchor nodes; Each anchor node carries out the calculating of least mean-square error corrected value after acquiring other anchor node minimum hop count, and this least mean-square error corrected value is sent, and makes all ordinary nodes finally obtain the least mean-square error corrected value of all anchor nodes; Each ordinary node is picked out a least mean-square error corrected value C from described all least mean-square error corrected value information, each the anchor node self information that is obtained with information interaction carries out location, three limits, and carry out probability and select, with the three limit elements of a fix of probability maximum as the ordinary node self-position.
Also find by retrieval, number of patent application is 200710164581.2 Chinese patent, a kind of wireless sensor network node locating method based on ranging technology has been proposed, comprise at least three known anchor nodes, the node to be measured and the backstage centre of location, anchor node sends to the backstage centre of location with the received signal intensity level of node to be measured and the strength of transmitted signals value of anchor node, and the backstage centre of location is calculated node to be measured to distance between the anchor node and coordinate.
More than two kinds of method positioning accuracy height, and first method reduces range error to location influence by selecting the method for least mean-square error corrected value.But two kinds of methods all exist the limitation of ranging technology, and range error is bigger to the influence of positioning accuracy, need to produce a large amount of calculating and communication overhead simultaneously.And, when the target numbers in the location increases, also can cause rolling up of sensor network internal transmission information, to the also corresponding increase of bandwidth requirement.
Summary of the invention
The objective of the invention is to overcome above-mentioned the deficiencies in the prior art, multi-target orientation method based on probability weight has been proposed in a kind of wireless sensor network, make its energy efficient in the target localization process, non-distance measuring, range detection sensor and transmission range information required energy consumption and bandwidth have been reduced, weighted model based on prior setting is weighted each grid in the zone, is very suitable for the characteristics and the requirement of multi-target orientation method.
The present invention is achieved through the following technical solutions, the present invention includes following steps:
Step 2 according to the distance between grid and the sensor node, is set up the probability weight model that grid is obtained by the sensor node covering;
Step 3, when sensor node detects one or more target, send a signal packet to the base station, the ID or the positional information that have comprised this node itself in the signal packet, the base station receives that sensor node sends show the signal that detects target after, probability weight model according to step 2 is set up is weighted the grid in the sensor coverage;
Step 4 detects the grid weighting procedure of the sensor node implementation step three of target to all, after all weighting procedure is finished, obtains the accumulation weights of each grid in the plane domain;
Step 5 for the single goal location, is chosen the accumulation weights and is surpassed the net region of setting thresholding as coordinates of targets; For the multiple target location, the accumulation weights surpass the grid of setting thresholding will present the multizone distribution, choose the mesh coordinate of accumulation weights maximum in each zone, be coordinates of targets.
Described probability weight model, it is responsible for adding up the probability weights that each grid is covered by sensor node, the reflection sensor node detects the confidence level that target occurs at certain grid, the probability weights are big more, then confidence level is high more, being that target is possible more occurs in this zone, and the expression formula of probability weight model is:
Wherein, C
Xy(S
i) the expression grid is because the weights that the covering of sensor node obtains, R represents the effective radius of investigation of transducer, R
eExpression sensor error radius of investigation,
Distance between expression transducer Si and the arbitrary mess point, α=d (S
i, P)-(R-R
e), λ, β are the weighted model parameter, concrete root of number border situation is factually chosen suitable value;
Compared with prior art, the present invention has following beneficial effect:
1. the present invention can locate a plurality of targets simultaneously by the grid of locating area being divided and grid weighting marking mechanism, is a kind of and the multi-target orientation method of hairdo, has avoided common accumulated error problem in the distributed multiple target location;
2. the present invention can adjust locating accuracy by the mesh-density of search coverage at any time, to adapt to the actual needs of various application, conserve energy consumption;
3. the present invention and range-independence, transducer need not to measure or repayment and target between distance, reduced the energy consumption and the inromation bandwidth of this part of sensor network, because non-distance measuring, to the also corresponding reduction of the requirement of sensor device;
4. complexity of the present invention is lower, and fast convergence rate is applicable to the location in the method for tracking target;
In addition, the present invention compares with multiple target location being resolved into the conventional method that single target locatees one by one, has remedied the deficiency of conventional method, has improved the performance of multiple target location widely.
Description of drawings
Fig. 1 workflow diagram of the present invention;
Fig. 2 is grid dividing mode figure among the present invention;
Fig. 3 is an indifference transducer distribution instance graph among the present invention;
Fig. 4 is a kind of probability weight model curve schematic diagram in the embodiments of the invention.
Embodiment
Below in conjunction with accompanying drawing embodiments of the invention are elaborated: present embodiment is being to implement under the prerequisite with the technical solution of the present invention, provided detailed execution mode and concrete operating process, but protection scope of the present invention is not limited to following embodiment.
Below in conjunction with the multi-target orientation method based on probability weight proposed by the invention, adopt different grids to divide density, three concrete embodiment:
Embodiment one
As shown in Figure 1, present embodiment comprises the steps:
The first step, the 60cm that coordinate is known * 60cm plane domain is divided grid by 60 * 60, as shown in Figure 2, makes 13 indifference transducer S
1～S
13Evenly distribute by the ranks order in the zone, shown in stain among Fig. 3, make that this zone is all standing, the coverage of single-sensor is approximately the circle that radius is R=50cm, and error is R
e=R/3;
In second step,, set up the probability weight model that grid is obtained by the sensor node covering: definition sensor node S according to the distance between grid and the sensor node
iCoordinate S (x
i, y
i), 1≤i≤13 wherein, (x, y), the definition grid is to sensor node S for definition mesh coordinate P
iDistance
According to d (S
i, P) set up the probability weight model, promptly (x is y) because S for grid P
iThe weights C that covering obtained
Xy(S
i), model curve as shown in Figure 4:
Wherein R represents the effective radius of investigation of transducer, R
eExpression sensor error radius of investigation, α=d (S
i, P)-(R-R
e), λ, β is the weighted model parameter, gets λ=1, β=1;
The 3rd step, transducer S
iWhen detecting one or more target, send a signal packet to the base station, comprising the ID or the positional information of this node itself, the base station receives S
iSend show the signal that detects target after, according to the probability weight model of having set up, to S
i(x y) is weighted grid P in the coverage;
The 4th step, all sensor nodes that detect target are implemented the grid weighting procedure in the 3rd step, after all weighting procedure is finished, obtain the accumulation weights of each grid in the plane domain;
In the 5th step,, choose the accumulation weights and surpass the net region of setting thresholding as coordinates of targets for the single goal location, locate for multiple target, the grid that the accumulation weights surpass certain thresholding will present the multizone distribution, choose the mesh coordinate of accumulation weights maximum in each zone, be coordinates of targets.
The error criterion of the mean square error of the target location of gained and its physical location as this method adopted in the calculating of positioning accuracy: error=(x
Cal-x
Real)
2+ (y
Cal-y
Real)
2, wherein error represents error distance, unit is centimetre (cm), x
Cal, y
CalThe coordinate of gained target, x are calculated in expression
Real, y
RealThe coordinate of expression actual measurement target, under the situations of grid by 60 * 60 divisions, position error error=0.8945.
Embodiment two
As shown in Figure 1, present embodiment comprises the steps:
The first step, the 60cm that coordinate is known * 60cm plane domain is divided grid by 100 * 100, as shown in Figure 2, makes 13 indifference transducer S
1～S
13Evenly distribute by the ranks order in the zone, as shown in Figure 3, make that this zone is all standing, the coverage of single-sensor is approximately the circle that radius is R=50cm, and error is R
e=R/3;
In second step,, set up the probability weight model that grid is obtained by the sensor node covering: definition sensor node S according to the distance between grid and the sensor node
iCoordinate S (x
i, y
i), 1≤i≤13 wherein, (x, y), the definition grid is to sensor node S for definition mesh coordinate P
iDistance
According to d (S
i, P) set up the probability weight model, promptly (x is y) because S for grid P
iThe weights C that covering obtained
Xy(S
i), model curve as shown in Figure 4:
Wherein R represents the effective radius of investigation of transducer, R
eExpression sensor error radius of investigation, α=d (S
i, P)-(R-R
e), λ, β is the weighted model parameter, gets λ=1, β=1;
The 3rd step, transducer S
iWhen detecting one or more target, send a signal packet to the base station, comprising the ID or the positional information of this node itself, the base station receives S
iSend show the signal that detects target after, according to the probability weight model of having set up, to S
i(x y) is weighted grid P in the coverage;
The 4th step, all sensor nodes that detect target are implemented the grid weighting procedure in the 3rd step, after all weighting procedure is finished, obtain the accumulation weights of each grid in the plane domain;
The 5th step, locate for single goal, choosing the accumulation weights surpasses in the net region of certain thresholding, the mesh coordinate of accumulation weights maximum, be coordinates of targets, for the multiple target location, the grid that the accumulation weights surpass certain thresholding will present the multizone distribution, choose the mesh coordinate of accumulation weights maximum in each zone, be coordinates of targets.
The error criterion of the mean square error of the target location of gained and its physical location as this method adopted in the calculating of positioning accuracy: error=(x
Cal-x
Real)
2+ (y
Cal-y
Real)
2, wherein, error represents error distance, unit is centimetre (cm), x
Cal, y
CalThe coordinate of gained target, x are calculated in expression
Real, y
RealThe coordinate of expression actual measurement target, under the situations of grid by 100 * 100 divisions, position error error=0.7571.
Embodiment three
As shown in Figure 1, present embodiment comprises the steps:
The first step: the 60cm that coordinate is known * 60cm plane domain is divided grid by 200 * 200, as shown in Figure 2, makes 13 indifference transducer S
1～S
13Evenly distribute by the ranks order in the zone, as shown in Figure 3, make that this zone is all standing, the coverage of single-sensor is approximately the circle that radius is R=50cm, and error is R
e=R/3;
Second step:, set up the probability weight model that grid is obtained by the sensor node covering: definition sensor node S according to the distance between grid and the sensor node
iCoordinate S (x
i, y
i), 1≤i≤13 wherein, (x, y), the definition grid is to sensor node S for definition mesh coordinate P
iDistance
According to d (S
i, P) set up the probability weight model, promptly (x is y) because S for grid P
iThe weights C that covering obtained
Xy(S
i), model curve as shown in Figure 4:
Wherein R represents the effective radius of investigation of transducer, R
eExpression sensor error radius of investigation, α=d (S
i, P)-(R-R
e), λ, β is the weighted model parameter, gets λ=1, β=1;
The 3rd step: transducer S
iWhen detecting one or more target, send a signal packet to the base station, comprising the ID or the positional information of this node itself, the base station receives S
iSend show the signal that detects target after, according to the probability weight model of having set up, to S
i(x y) is weighted grid P in the coverage;
The 4th step: all sensor nodes that detect target are implemented the grid weighting procedure in the 3rd step, after all weighting procedure is finished, obtain the accumulation weights of each grid in the plane domain;
The 5th step: locate for single goal, choosing the accumulation weights surpasses in the net region of certain thresholding, the mesh coordinate of accumulation weights maximum, be coordinates of targets, locate for multiple target, the grid that the accumulation weights surpass certain thresholding will present the multizone distribution, choose the mesh coordinate of accumulation weights maximum in each zone, be coordinates of targets.
The target location of the calculating employing gained of positioning accuracy and the mean square error of its physical location are as the error criterion of this method, error=(x
Cal-x
Real)
2+ (y
Cal-y
Real)
2, wherein error represents error distance, unit is centimetre (cm), x
Cal, y
CalThe coordinate of gained target, x are calculated in expression
Real, y
RealThe coordinate of expression actual measurement target is pressed under 200 * 200 situations of dividing at grid, position error error=0.5806.
Claims (2)
1, in a kind of wireless sensor network based on the multi-target orientation method of probability weight, it is characterized in that, comprise the steps:
Step 1, the plane domain that coordinate is known are pressed grid and are divided, and a plurality of indifference sensor nodes are evenly distributed by the ranks order in the zone, make that this zone is all standing;
Step 2 according to the distance between grid and the sensor node, is set up the probability weight model that grid is obtained by the sensor node covering;
Step 3, when sensor node detects one or more target, send a signal packet to the base station, the ID or the positional information that have comprised this node itself in the signal packet, the base station receives that sensor node sends show the signal that detects target after, probability weight model according to step 2 is set up is weighted the grid in the sensor coverage;
Step 4 detects the grid weighting procedure of the sensor node implementation step three of target to all, after all weighting procedure is finished, obtains the accumulation weights of each grid in the plane domain;
Step 5 for the single goal location, is chosen the accumulation weights and is surpassed the net region of setting thresholding as coordinates of targets; For the multiple target location, the accumulation weights surpass the grid of setting thresholding will present the multizone distribution, and the mesh coordinate of choosing accumulation weights maximum in each zone is a coordinates of targets.
2, in the wireless sensor network according to claim 1 based on the multi-target orientation method of probability weight, it is characterized in that, described probability weight model, it is responsible for adding up the probability weights that each grid is covered by sensor node, the reflection sensor node detects the confidence level that target occurs at certain grid, the probability weights are big more, and then confidence level is high more, and the expression formula of probability weight model is:
Wherein, C
Xy(S
i) the expression grid is because the weights that the covering of sensor node obtains, R represents the effective radius of investigation of transducer, R
eExpression sensor error radius of investigation,
Distance between expression transducer Si and the arbitrary mess point, α=d (S
i, P)-(R-R
e), λ, β are the weighted model parameter, concrete root of number border situation is factually chosen suitable value.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7904244B2 (en) * | 2003-11-18 | 2011-03-08 | Sarimo Technologies, LLC | Determining a location or position using information from multiple location and positioning technologies and applications using such a determined location or position |
| CN102386974A (en) * | 2011-12-13 | 2012-03-21 | 中国电信股份有限公司 | PON (passive optical network) network fault detection method and device |
| CN103037503A (en) * | 2011-09-30 | 2013-04-10 | 无锡物联网产业研究院 | Wireless sensor network positioning method and wireless sensor network positioning system |
| CN103034866A (en) * | 2011-09-29 | 2013-04-10 | 无锡物联网产业研究院 | Target identification method, device and system |
| CN103081545A (en) * | 2010-07-08 | 2013-05-01 | Sk电信有限公司 | Method and device for discriminating positioning error using wireless LAN signal |
| CN103716867A (en) * | 2013-10-25 | 2014-04-09 | 华南理工大学 | Wireless sensor network multiple target real-time tracking system based on event drive |
| CN103869278A (en) * | 2012-12-10 | 2014-06-18 | 日电（中国）有限公司 | Multi-target positioning method and device based on distance measurement |
| CN105025434A (en) * | 2014-04-29 | 2015-11-04 | 东北大学 | GSM base station location method based on Pearson's correlation coefficient |
| CN111199636A (en) * | 2017-06-07 | 2020-05-26 | 吴玉芳 | Working method of hydrological meteorological service system based on cloud computing |
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2009
- 2009-01-08 CN CNA2009100450040A patent/CN101466146A/en active Pending
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7904244B2 (en) * | 2003-11-18 | 2011-03-08 | Sarimo Technologies, LLC | Determining a location or position using information from multiple location and positioning technologies and applications using such a determined location or position |
| CN103081545B (en) * | 2010-07-08 | 2016-03-09 | Sk电信有限公司 | Wireless LAN signals is utilized to distinguish the method and apparatus of position error |
| CN103081545A (en) * | 2010-07-08 | 2013-05-01 | Sk电信有限公司 | Method and device for discriminating positioning error using wireless LAN signal |
| CN103034866B (en) * | 2011-09-29 | 2016-02-10 | 无锡物联网产业研究院 | A kind of target identification method, Apparatus and system |
| CN103034866A (en) * | 2011-09-29 | 2013-04-10 | 无锡物联网产业研究院 | Target identification method, device and system |
| CN103037503B (en) * | 2011-09-30 | 2015-06-10 | 无锡物联网产业研究院 | Wireless sensor network positioning method and wireless sensor network positioning system |
| CN103037503A (en) * | 2011-09-30 | 2013-04-10 | 无锡物联网产业研究院 | Wireless sensor network positioning method and wireless sensor network positioning system |
| CN102386974B (en) * | 2011-12-13 | 2014-10-22 | 中国电信股份有限公司 | PON (passive optical network) network fault detection method and device |
| CN102386974A (en) * | 2011-12-13 | 2012-03-21 | 中国电信股份有限公司 | PON (passive optical network) network fault detection method and device |
| CN103869278A (en) * | 2012-12-10 | 2014-06-18 | 日电（中国）有限公司 | Multi-target positioning method and device based on distance measurement |
| CN103869278B (en) * | 2012-12-10 | 2016-06-15 | 日电（中国）有限公司 | Multi-target orientation method and device based on range finding |
| CN103716867A (en) * | 2013-10-25 | 2014-04-09 | 华南理工大学 | Wireless sensor network multiple target real-time tracking system based on event drive |
| CN103716867B (en) * | 2013-10-25 | 2017-10-27 | 华南理工大学 | Based on event driven wireless sensor network multi-target real-time tracking system |
| CN105025434A (en) * | 2014-04-29 | 2015-11-04 | 东北大学 | GSM base station location method based on Pearson's correlation coefficient |
| CN105025434B (en) * | 2014-04-29 | 2019-07-16 | 东北大学 | GSM base station positioning method based on Pearson correlation coefficient |
| CN111199636A (en) * | 2017-06-07 | 2020-05-26 | 吴玉芳 | Working method of hydrological meteorological service system based on cloud computing |
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