CN108459298A - A kind of outdoor positioning method based on LoRa technologies - Google Patents
A kind of outdoor positioning method based on LoRa technologies Download PDFInfo
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Abstract
The present invention relates to a kind of outdoor positioning methods based on LoRa technologies, belong to wireless positioning field.This method is modified the rssi measurement value of the LoRa signals of acquisition first with BP neural network；Using modified RSSI value, dynamic optimization is carried out to the parameter of LoRa path loss models Z, Z is made to be suitable for different localizing environments；The linear orientation model G based on RSSI is combined using trained Z, the LoRa based on RSSI is established and positions linear model L3M, obtain the Primary Location results set R of destination node；Using L3M C location models, clustering processing is carried out to R, obtains positioning result set R1；Using L3M MRE location models, the selection of least estimated RSSI errors is carried out to R, obtains positioning result set R2；Using L3M W selection strategy location models, to R1And R2Minimum average B configuration estimation RSSI errors selection is carried out, the best estimate position of destination node is obtained.The present invention in an outdoor environment, can reduce the influence that NLOS errors and ambient noise propagate signal, improve positioning accuracy.
Description
Technical field
The invention belongs to wireless positioning fields, are related to a kind of outdoor positioning method based on LoRa technologies.
Background technology
Internet of Things is more and more using the requirement to positioning, such as equipment tracking or smart city, and it is geographical fixed to be required for
Position.It is studied according to Machina Reasearch, arrives the year two thousand twenty bottom, Internet of Things will have more than 1,500,000,000 connection equipment.Wherein about
For one third by heavy dependence geodata, 60% application will likely include geodata.
Up to the present, the major technique that can be used for the positioning of Internet of Things object has bluetooth, GPS and GSM.Currently, positioning chases after
Track method mostly uses greatly Bluetooth technology and carries out location tracking, but Bluetooth transmission distance is only 10m~100m, and transmission range is too short,
Power consumption is big simultaneously, it is difficult to carry out effective tracing and positioning.It is to use GPS in the common method of outdoor carry out geo-location, but it is right
In certain low-power consumption, the Internet of Things application of low cost, the high of high cost and high energy consumption of GPS, cannot meet the needs of practical application.
And the positioning and tracing method transmitted currently based on gsm communication, there are GSM transmission power consumptions height, it can not accomplish point-to-point real-time control
The problem of processed.
Location technology based on RSSI carries out location estimation by received signal strength.In the environment, it is fixed to influence RSSI
Position precision factor it is main there are two：(1) under actual location scene there are a large amount of barrier, transmitting signal can be by when positioning
Change to signal propagation characteristics in influence (2) positioning scene of non-line-of-sight propagation with the change of environment, fixed path attenuation
Module parameter is difficult influence of the environment to signal for reflecting entire localization region comprehensively and accurately.
LoRa is a kind of remote wireless communication technique of low-power consumption, and the main feature of the technology of LoRa wireless communications has：
(1) remote：In intensive urban environment and city, LoRa have stronger penetration capacity, transmission range up to 3km with
On；In spacious suburb, transmission range is up to 15km~20km or even farther.(2) low-power consumption：LoRa has exclusively for low work(
The agreement for consuming and developing, battery life is up to many decades.(3) receiving sensitivity is high：LoRa receive sensitivity reached-
148dbm, it is ensured that the reliability of network connection.(4) inexpensive：LoRa modules are at low cost.
Therefore, it can be associated by the characteristic of the LoRa itself, using LoRa transmission technologys in outdoor positioning technology
It is realized in conjunction with improved RSSI location algorithms.
Invention content
In view of this, the purpose of the present invention is to provide a kind of outdoor positioning methods based on LoRa technologies, in outdoor ring
When being positioned using LoRa transmission technologys under border, the influence that NLOS errors and ambient noise propagate signal can be reduced, is improved
Positioning accuracy.
In order to achieve the above objectives, the present invention provides the following technical solutions：
A kind of outdoor positioning method based on LoRa technologies, specifically includes following steps：
S1：Using BP neural network to RSSI (the Received Signal Strength of the LoRa signals of acquisition
Indication, the instruction of received signal intensity) value is modified；
S2：Using modified RSSI value, dynamic optimization is carried out to the parameter A and N of LoRa path loss models Z, Z is made to be applicable in
In different localizing environments；
S3：The linear orientation model G based on RSSI is combined using trained Z, establishes the LoRa position lines based on RSSI
Property model L3M, obtains the Primary Location results set R of destination node；
S4：Using preset L3M-C location models, clustering processing is carried out to R, obtains positioning result collection R1；
S5：Using preset L3M-MRE location models, the selection of least estimated RSSI errors is carried out to R, obtains positioning result
Collect R2；
S6：Using preset L3M-W selection strategies location model, minimum average B configuration estimation RSSI errors choosing is carried out to R1 and R2
It selects, obtains the best estimate position of destination node.
Further, the step S1 is specially：It is M region, every two pieces of different regions by actual location region division
The LoRa anchor nodes D of three known locations is placed in not exclusively overlapping in every sub-regions1、D2、D2, measure every sub-regions phase
The RSSI value answered, the feature vector of M 6 dimension of structure：
P (i)={ RSSI21, RSSI31,RSSI12,RSSI32,RSSI13,RSSI23, i=1,2,3 ... M
Input by P (i) as BP neural network, is trained BP neural network, finally exports corresponding per height
The feature vector of M 6 dimension under the ecotopia of region：R (i)={ rssi21,rssi31,rssi12,rssi32,rssi13,
rssi23, realize amendment of the BP neural network to the RSSI value of LoRa signals.
Further, the step S2 is specially：According to the modified RSSI values of step S1, for RSSI path losses
Model：
Z (d)=Z (d0)-10N×log10d+ψ
To parameter A=Z (d0)+ψ and N carry out dynamic optimization, and then adjust the parameter of location model, A indicates that receiving terminal exists
The intensity value and undulating value of reception signal at 1m, N indicate that signal propagation characteristics change with the change of environment in localization region
Parameter, Z (d) indicate receiving terminal d at receive signal intensity value, Z (d0) indicate receiving terminal in d0The reception signal at place
Intensity value, d indicate that the distance between receiving terminal and transmitting terminal, ψ indicate the Gaussian distributed random variable that mean value is zero；
Using A and N as the environmental parameter of localization region, is utmostly met LoRa in the environment of localization region and declined in path
Subtract model：Z (d)=A-10N × log10d。
Further, the step S3 is specially：
Linear orientation model based on RSSI is：In conjunction with the LoRa path attenuation models optimized, base is obtained
It is in the LoRa positioning linear model L3M of RSSI：Calculate the Primary Location results set R of destination node.
Wherein
WithIndicate the position coordinates set of anchor node,WithIndicate the Primary Location results set R of destination node,Indicate H
Anchor node received signal intensity value, RHThe H anchor node is indicated to the distance for positioning origin, whereinH is indicated
The number of anchor node, N indicate that the parameter that signal propagation characteristics change with the change of environment in localization region, A indicate receiving terminal
The intensity value and undulating value of reception signal at 1m.
Further, the step S4 is specially：
S41：All estimated locations of destination node are divided into k cluster using K-means clustering algorithms；
S42：Calculate the estimation RSSI errors of each cluster；
S43：The cluster for finding out estimation RSSI error minimums, is denoted as：k*；
S44：Calculate the total degree that each anchor node occurs in all clusters of residue in addition to * cluster of kth；
S45：The most anchor node of occurrence number is found out, it is removed from location Calculation；
S46：Remaining H-1 anchor node reuses L3M and is positioned；
S47：Export the positioning result collection R of destination node1。
Further, the step S5 is specially：
S51：To the Primary Location results set R of destination node, m-th of estimated location that destination node in R is arranged is
(xm,ym), find out (xm,ym) averaged power spectrum RSSI errors：WhereinIndicate (xm,ym)
To the averaged power spectrum RSSI errors of all anchor nodes,Indicate i-th of anchor node received signal strength value set,It indicates
(xm,ym) to the received signal strength value set of i-th of anchor node, H indicates the number of anchor node.
S52：It utilizesFormula finds out estimation RSSI error minimumsThe corresponding estimation positions m*
The positioning result collection R2 as destination node is set,It indicatesIn minimum value set.
Further, the step S6 is specially：
Positioning result collection R is found out respectively1And R2Estimated location to all anchor nodes averaged power spectrum RSSI errors, it is assumed that
(a, b) is R1And R2In some estimated position points, have：
Wherein,Indicate the estimation RSSI errors of (a, b),Indicate (a, b) connecing to i-th anchor node
Collection of letters intensity set；
It utilizesFormula finds out averaged power spectrum RSSI error minimumsCorresponding (x*,y*), the as best estimate position of destination node；Wherein (X1,Y1) and (X2,Y2) R1 is indicated respectively
The estimated location coordinate of destination node in gathering with R2；Wherein,It indicatesWithIn set most
Small value.
The beneficial effects of the present invention are：The present invention positions low-power consumption wide area network LoRa transmission technologys and improved RSSI
Algorithm is combined, and in a manner of remote, low-power consumption and low cost, can be accurately positioned in an outdoor environment to target object.
Description of the drawings
In order to keep the purpose of the present invention, technical solution and advantageous effect clearer, the present invention provides following attached drawing and carries out
Explanation：
Fig. 1 is the outdoor positioning method flow schematic diagram of the present invention based on LoRa technologies；
Fig. 2 is the BP neural network correction model of rssi measurement value in a nlos environment of the present invention；
Fig. 3 is of the present invention based on BP neural network correction algorithm flow chart；
Fig. 4 is L3M-C location algorithms flow chart of the present invention；
Fig. 5 is L3M-MRE location algorithms flow chart of the present invention；
Fig. 6 is L3M-W location algorithms flow chart of the present invention.
Specific implementation mode
Below in conjunction with attached drawing, the preferred embodiment of the present invention is described in detail.
Fig. 1 is the outdoor positioning method flow schematic diagram of the present invention based on LoRa technologies；This method includes as follows
Step：
S101：The rssi measurement value of the LoRa signals of acquisition is modified first with BP neural network；
Specially：It is M sub-regions by actual location region division, every two pieces of different regions are not exclusively overlapped, every
The LoRa anchor nodes D of three known locations is placed in sub-regions1、D2、D3, the corresponding RSSI value of every sub-regions is measured, is built
The feature vector of M 6 dimension：
P (i)={ RSSI21, RSSI31,RSSI12,RSSI32,RSSI13,RSSI23, i=1,2,3 ... M
The BP correction models of rssi measurement value as shown in Figure 2 are initially set up to export them after input layer receives signal
To hidden layer.Hidden layer neuron number is selected as 18, using Sigmod type functions f1(x)=tanh (x), hidden layer is to signal
Certain response is generated, and is output to output layer.The model is made of 6 neurons, and output layer uses linear transfer function
f2(x)=kx, revised RSSI value are the output of output layer, and output vector is：
R (i)={ rssi21,rssi31,rssi12,rssi32,rssi13,rssi23}
If input neuron number is r, input layer p, neuron number is s in hidden layer1, activation primitive f1It indicates,
Connection weight between input layer and hidden layer is ωij, then the output of i-th of neuron is in hidden layer：
If neuron number is s in output layer2, activation primitive f2It indicates, target vector T exports as A.Hidden layer with
Connection weight between output layer is ωki, then the output of k-th of neuron of output layer is：
Define error function：
By gradient descent method, the weights of output layer change, institute directly proportional to the negative gradient of output layer weights to error function
It is with the variation of weights：
The weights of output layer update as the following formula：ωki(t+1)=ωki(t)+Δωki(t)
Similarly, the variation of hidden layer weights is directly proportional to the negative gradient of hidden layer weights to error function, so the change of weights
It turns to：
The weights of hidden layer update as the following formula：
ωij(t+1)=ωij(t)+Δωij(t)
S102：As shown in figure 3, according to BP neural network repairing to the rssi measurement values of LoRa signals is realized described in S101
Just, dynamic optimization is carried out to the parameter A and N of LoRa path loss models Z, so that Z is can be suitably used for different localizing environments, specifically
For：
According to the modified RSSI of step S1, for RSSI path loss models：
Z (d)=Z (d0)-10N×log10d+ψ
To parameter A=Z (d0)+ψ and N carry out dynamic optimization, and then adjust the parameter of location model, A indicates that receiving terminal exists
The intensity value and undulating value of reception signal at 1m, N indicate that signal propagation characteristics change with the change of environment in localization region
Parameter, Z (d) indicate receiving terminal d at receive signal intensity value, Z (d0) indicate receiving terminal in d0The reception signal at place
Intensity value, d indicate that the distance between receiving terminal and transmitting terminal, ψ indicate the Gaussian distributed random variable that mean value is zero.
R (i) according to S101 and D1, D2, D3, with D1To refer to anchor node, formula is utilized：
Obtain one group of A1,
D can similarly be passed through2, D3Anchor node related data obtains 3 groups of A, then N values seek the average value of three：
As the environmental parameter of the localization region, utmostly met the path attenuation of LoRa signals in current environment
Model：Z (d)=A-10N × log10d。
S103：The linear orientation model G based on RSSI is combined using trained Z, establishes the LoRa positioning based on RSSI
Linear model L3M obtains the Primary Location result set R of destination node, specially：
Linear orientation model based on RSSI is：In conjunction with the LoRa path attenuation models optimized, base is obtained
It is in the LoRa positioning linear model L3M of RSSI：Calculate the Primary Location results set R of destination node.
Wherein WithIndicate the position coordinates set of anchor node,WithIndicate the Primary Location results set R of destination node,Indicate H
A anchor node received signal intensity value, RHThe H anchor node is indicated to the distance for positioning origin, whereinH tables
Show that the number of anchor node, N indicate that the parameter that signal propagation characteristics change with the change of environment in localization region, A indicate to receive
The intensity value and undulating value of reception signal of the end at 1m.
Fig. 4 is a kind of L3M-C location algorithms flow chart of the present invention, as shown in figure 4, including the following steps：
S104：It is fixed using preset L3M-C according to all Primary Location results set R of the obtained destination nodes of S103
Bit model carries out clustering processing to R, obtains positioning result collection R1, specially：
All estimated locations of destination node are divided into k cluster using K-means clustering algorithms；
Calculate the estimation RSSI errors of each cluster；
The cluster for finding out estimation RSSI error minimums, is denoted as：k*；
Calculate the total degree that each anchor node occurs in all clusters of residue in addition to * cluster of kth；
The most anchor node of occurrence number is found out, it is removed from location Calculation；
Remaining H-1 anchor node reuses L3M and is positioned；
Export the positioning result collection R of destination node1。
Fig. 5 is a kind of L3M-MRE location algorithms flow chart of the present invention, as shown in figure 5, including the following steps：
S105：Using preset L3M-MRE location models, the selection of least estimated RSSI errors is carried out to R, obtains positioning knot
Fruit set R2, specially：
To the Primary Location results set R of destination node, m-th of estimated location that destination node in R is arranged is (xm,ym),
Find out (xm,ym) averaged power spectrum RSSI errors：WhereinIndicate (xm,ym) arrive all anchors
The averaged power spectrum RSSI errors of node,Indicate i-th of anchor node received signal strength value set,Indicate (xm,ym) arrive
The received signal strength value set of i-th of anchor node.
It recyclesFormula finds out estimation RSSI error minimumsThe corresponding estimated locations of m*
The as positioning result collection R2 of destination node；It indicatesIn minimum value set.
Fig. 6 is a kind of L3M-W location algorithms flow chart of the present invention, as shown in fig. 6, including the following steps：
S106：Are carried out by minimum average B configuration estimation RSSI and is missed by R1 and R2 using preset L3M-W selection strategies location model
Difference selection, obtains the best estimate position of destination node, specially：
Positioning result collection R is found out respectively1And R2Estimated location to all anchor nodes averaged power spectrum RSSI errors, it is assumed that
(a, b) is R1And R2In some estimated position points, have：
Wherein,Indicate the estimation RSSI errors of (a, b),Indicate (a, b) connecing to i-th anchor node
Collection of letters intensity set, the number of H expression anchor nodes, the distance of d (a, b, i) expressions (a, b) to i-th of anchor node,Table
Show i-th of anchor node received signal strength value set.
It utilizesFormula finds out averaged power spectrum RSSI error minimumsCorresponding (x*,y*), the as best estimate position of destination node；Wherein (X1,Y1) and (X2,Y2) R1 is indicated respectively
The estimated location coordinate of destination node in gathering with R2；Wherein,It indicatesWithIn set most
Small value.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (7)
1. a kind of outdoor positioning method based on LoRa technologies, which is characterized in that this method specifically includes following steps：
S1：Using BP neural network to RSSI (the Received Signal Strength of the LoRa signals of acquisition
Indication, the instruction of received signal intensity) value is modified；
S2：Using modified RSSI value, dynamic optimization is carried out to the parameter A and N of LoRa path loss models Z, Z is made to be suitable for not
Same localizing environment；
S3：The linear orientation model G based on RSSI is combined using trained Z, the LoRa based on RSSI is established and positions linear mould
Type L3M obtains the Primary Location results set R of destination node；
S4：Using preset L3M-C location models, clustering processing is carried out to R, obtains positioning result collection R1；
S5：Using preset L3M-MRE location models, the selection of least estimated RSSI errors is carried out to R, obtains positioning result collection
R2；
S6：Using preset L3M-W selection strategies location model, minimum average B configuration estimation RSSI errors selection is carried out to R1 and R2,
Obtain the best estimate position of destination node.
2. a kind of outdoor positioning method based on LoRa technologies according to claim 1, which is characterized in that the step S1
Specially：It is M region by actual location region division, every two pieces of different regions are not exclusively overlapped, in every sub-regions
Place the LoRa anchor nodes D of three known locations1、D2、D2, measure the corresponding RSSI value of every sub-regions, the spy of M 6 dimension of structure
Sign vector：
P (i)={ RSSI21, RSSI31,RSSI12,RSSI32,RSSI13,RSSI23, i=1,2,3 ... M
Input by P (i) as BP neural network, is trained BP neural network, finally exports corresponding per sub-regions
The feature vector of M 6 dimension under ecotopia：R (i)={ rssi21,rssi31,rssi12,rssi32,rssi13,rssi23, it is real
Existing amendment of the BP neural network to the RSSI value of LoRa signals.
3. a kind of outdoor positioning method based on LoRa technologies according to claim 1, which is characterized in that the step S2
Specially：According to the modified RSSI values of step S1, for RSSI path loss models：
Z (d)=Z (d0)-10N×log10d+ψ
To parameter A=Z (d0)+ψ and N carry out dynamic optimization, and then adjust the parameter of location model, A indicates receiving terminal at 1m
The intensity value and undulating value of signal are received, N indicates the parameter that signal propagation characteristics change with the change of environment in localization region,
Z (d) indicates the intensity value of reception signal of the receiving terminal at d, Z (d0) indicate receiving terminal in d0The intensity value of the reception signal at place,
D indicates that the distance between receiving terminal and transmitting terminal, ψ indicate the Gaussian distributed random variable that mean value is zero；
Using A and N as the environmental parameter of localization region, utmostly met LoRa path attenuations mould in the environment of localization region
Type：Z (d)=A-10N × log10d。
4. a kind of outdoor positioning method based on LoRa technologies according to claim 1, which is characterized in that the step S3
Specially：
Linear orientation model based on RSSI is：In conjunction with the LoRa path attenuation models optimized, it is based on
The LoRa of RSSI positions linear model L3M：Calculate the Primary Location results set R of destination node；
Wherein WithIndicate the position coordinates set of anchor node,WithIndicate the Primary Location results set R of destination node,Indicate the
H anchor node received signal intensity value, RHThe H anchor node is indicated to the distance for positioning origin, whereinH tables
Show that the number of anchor node, N indicate that the parameter that signal propagation characteristics change with the change of environment in localization region, A indicate to receive
The intensity value and undulating value of reception signal of the end at 1m.
5. a kind of outdoor positioning method based on LoRa technologies according to claim 1, which is characterized in that the step S4
Specially：
S41：All estimated locations of destination node are divided into k cluster using K-means clustering algorithms；
S42：Calculate the estimation RSSI errors of each cluster；
S43：The cluster for finding out estimation RSSI error minimums, is denoted as：k*；
S44：Calculate the total degree that each anchor node occurs in all clusters of residue in addition to * cluster of kth；
S45：The most anchor node of occurrence number is found out, it is removed from location Calculation；
S46：Remaining H-1 anchor node reuses L3M and is positioned；
S47：Export the positioning result collection R of destination node1。
6. a kind of outdoor positioning method based on LoRa technologies according to claim 1, which is characterized in that the step S5
Specially：
S51：To the Primary Location results set R of destination node, m-th of estimated location that destination node in R is arranged is (xm,ym),
Find out (xm,ym) averaged power spectrum RSSI errors：WhereinIndicate (xm,ym) arrive all anchors
The averaged power spectrum RSSI errors of node,Indicate i-th of anchor node received signal strength value set,Indicate (xm,ym) arrive
The received signal strength value set of i-th of anchor node, H indicate the number of anchor node；
S52：For m all in set R, utilizeFormula finds out estimation RSSI error minimumsThe corresponding estimated locations of m* are the positioning result collection R2 of destination node；It indicatesIn minimum value set.
7. a kind of outdoor positioning method based on LoRa technologies according to claim 6, which is characterized in that the step S6
Specially：
Positioning result collection R is found out respectively1And R2Estimated location to all anchor nodes averaged power spectrum RSSI errors, it is assumed that (a, b)
It is R1And R2In some estimated position points, have：
Wherein,Indicate the estimation RSSI errors of (a, b),Indicate that (a, b) believes to the reception of i-th of anchor node
Number intensity set；
It utilizesFormula finds out averaged power spectrum RSSI error minimums
Corresponding (x*,y*), the as best estimate position of destination node；Wherein (X1,Y1) and (X2,Y2) R1 and R2 set is indicated respectively
The estimated location coordinate of middle destination node；Wherein,It indicatesWithMinimum value in set.
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| CN110493720A (en) * | 2019-09-11 | 2019-11-22 | 深圳市名通科技股份有限公司 | Localization method, device and the storage medium of terminal |
| CN110927666A (en) * | 2019-11-08 | 2020-03-27 | 西北大学 | Non-contact detection positioning method and system based on LoRa signal |
| CN110990500A (en) * | 2019-03-29 | 2020-04-10 | 天维讯达(湖南)科技有限公司 | Propagation path model map establishing method and path loss determining method |
| CN111065046A (en) * | 2019-11-21 | 2020-04-24 | 东南大学 | LoRa-based outdoor unmanned aerial vehicle positioning method and system |
| CN111555824A (en) * | 2020-04-26 | 2020-08-18 | 南京工业大学 | Bad anchor node detection and elimination method for LoRa positioning system |
| EP3764120A1 (en) * | 2019-07-10 | 2021-01-13 | Swisscom AG | Low power wide area network localization |
| US11503435B2 (en) | 2019-06-19 | 2022-11-15 | Beijing Boe Technology Development Co., Ltd. | Positioning method and apparatus of target node in wireless ad hoc network, electronic device, and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102413564A (en) * | 2011-11-25 | 2012-04-11 | 北京工业大学 | Indoor positioning method based on BP neural network and improved centroid algorithm |
| CN102883430A (en) * | 2012-09-12 | 2013-01-16 | 南京邮电大学 | Range-based wireless sensing network node positioning method |
| CN105654151A (en) * | 2015-12-23 | 2016-06-08 | 华中科技大学 | Workpiece positioning method and positioning system |
| CN105676178A (en) * | 2016-01-13 | 2016-06-15 | 南京邮电大学 | Wireless sensor network positioning method based on compressed sensing and BP neural networks |
-
2018
- 2018-03-16 CN CN201810220076.3A patent/CN108459298A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102413564A (en) * | 2011-11-25 | 2012-04-11 | 北京工业大学 | Indoor positioning method based on BP neural network and improved centroid algorithm |
| CN102883430A (en) * | 2012-09-12 | 2013-01-16 | 南京邮电大学 | Range-based wireless sensing network node positioning method |
| CN105654151A (en) * | 2015-12-23 | 2016-06-08 | 华中科技大学 | Workpiece positioning method and positioning system |
| CN105676178A (en) * | 2016-01-13 | 2016-06-15 | 南京邮电大学 | Wireless sensor network positioning method based on compressed sensing and BP neural networks |
Non-Patent Citations (2)
| Title |
|---|
| KA-HO LAM 等: "Lora-based localization systems for noisy outdoor environment", 《2017 IEEE 13TH INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB)》 * |
| 彭宇倩 等: "基于BP神经网络的实时动态参数定位方法", 《计算机工程与设计》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109474886B (en) * | 2018-10-15 | 2021-03-12 | 广州中国科学院计算机网络信息中心 | Low-power-consumption remote positioning method and system based on LoRa |
| CN109474886A (en) * | 2018-10-15 | 2019-03-15 | 广州中国科学院计算机网络信息中心 | A kind of low-power consumption long distance positioning method and system based on LoRa |
| CN110990500B (en) * | 2019-03-29 | 2023-12-15 | 天维讯达(湖南)科技有限公司 | Propagation path model map establishment method and path loss determination method |
| CN110990500A (en) * | 2019-03-29 | 2020-04-10 | 天维讯达(湖南)科技有限公司 | Propagation path model map establishing method and path loss determining method |
| CN110136468A (en) * | 2019-04-18 | 2019-08-16 | 太原理工大学 | A kind of large parking lot car finding device and method |
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| US11503435B2 (en) | 2019-06-19 | 2022-11-15 | Beijing Boe Technology Development Co., Ltd. | Positioning method and apparatus of target node in wireless ad hoc network, electronic device, and medium |
| EP3764120A1 (en) * | 2019-07-10 | 2021-01-13 | Swisscom AG | Low power wide area network localization |
| US20210012180A1 (en) * | 2019-07-10 | 2021-01-14 | Swisscom Ag | Methods and systems for low power wide area network localization |
| US11782120B2 (en) * | 2019-07-10 | 2023-10-10 | Swisscom Ag | Methods and systems for low power wide area network localization |
| CN110493720A (en) * | 2019-09-11 | 2019-11-22 | 深圳市名通科技股份有限公司 | Localization method, device and the storage medium of terminal |
| CN110927666A (en) * | 2019-11-08 | 2020-03-27 | 西北大学 | Non-contact detection positioning method and system based on LoRa signal |
| CN110927666B (en) * | 2019-11-08 | 2023-03-28 | 西北大学 | Non-contact detection positioning method and system based on LoRa signal |
| CN111065046B (en) * | 2019-11-21 | 2020-12-11 | 东南大学 | LoRa-based outdoor unmanned aerial vehicle positioning method and system |
| CN111065046A (en) * | 2019-11-21 | 2020-04-24 | 东南大学 | LoRa-based outdoor unmanned aerial vehicle positioning method and system |
| CN111555824A (en) * | 2020-04-26 | 2020-08-18 | 南京工业大学 | Bad anchor node detection and elimination method for LoRa positioning system |
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