CN110213813B - Intelligent management method for inertial sensor in indoor positioning technology - Google Patents
Intelligent management method for inertial sensor in indoor positioning technology Download PDFInfo
- Publication number
- CN110213813B CN110213813B CN201910555406.9A CN201910555406A CN110213813B CN 110213813 B CN110213813 B CN 110213813B CN 201910555406 A CN201910555406 A CN 201910555406A CN 110213813 B CN110213813 B CN 110213813B
- Authority
- CN
- China
- Prior art keywords
- state
- communication
- inertial sensor
- hidden
- observation value
- 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.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
-
- 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
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
- H04W52/0245—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal according to signal strength
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
The invention relates to an intelligent management method of an inertial sensor in an indoor positioning technology, which comprises the steps of obtaining an environment observation value of a label of a node to be positioned; inputting the obtained environment observation value into a hidden Markov model to obtain a hidden state of the hidden Markov model; judging whether to start the inertial sensor according to the acquired hidden state; the hidden state includes: a communication good state, a first communication attenuation state, a second communication attenuation state, a communication partial interruption state and a communication complete interruption state; when the environment observation value of the positioning node label is in any one of a second communication attenuation state, a communication partial interruption state and a communication complete interruption state, simultaneously starting an inertial sensor and a pedestrian navigation algorithm; the management method provided by the invention can accurately estimate the positioning environment of the label, and intelligently manage the start or the close of the inertial sensor so as to achieve the aim of low power consumption, thereby greatly increasing the cruising ability of the label.
Description
Technical Field
The invention belongs to the technical field of indoor positioning, and particularly relates to an intelligent management method of an inertial sensor in an indoor positioning technology.
Background
With the development of intelligent manufacturing and internet of things systems, the indoor positioning technology in extreme environments starts to be introduced in a large range, wherein the combination of ultra-wideband positioning and a pedestrian navigation algorithm is one of the best prospect schemes. The two methods are combined, so that the defects of each algorithm can be avoided, and the stronger anti-interference capability is achieved. However, the pedestrian navigation algorithm needs the inertial sensor to operate all the time to complete the positioning, which greatly increases the power consumption of the tag. Firstly, the algorithm requires that the sensor always keeps running, and the sensor can increase the power consumption of the tag; secondly, the control chip also needs to maintain communication with the sensor and store data, which causes the control chip to be frequently awakened from a low power consumption mode and also increases a large amount of power consumption; finally, the pedestrian navigation algorithm is complex, and the calculation process occupies a large amount of time, which also increases the power consumption.
Disclosure of Invention
Technical problem to be solved
Aiming at the existing technical problems, the invention provides an intelligent management method of an inertial sensor in an indoor positioning technology, which can accurately estimate the positioning environment of a label and intelligently manage the start or the close of the inertial sensor so as to achieve the aim of low power consumption, thereby greatly increasing the cruising ability of the label.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
an intelligent management method for an inertial sensor in an indoor positioning technology comprises the following steps:
acquiring an environment observation value of a label of a node to be positioned;
inputting the obtained environment observation value into a hidden Markov model to obtain a hidden state of the hidden Markov model;
judging whether to start the inertial sensor according to the acquired hidden state;
the hidden state includes: a communication good state, a first communication attenuation state, a second communication attenuation state, a communication partial interruption state and a communication complete interruption state;
when the environment observation value of the positioning node label is in any one of a second communication attenuation state, a communication partial interruption state and a communication complete interruption state, simultaneously starting an inertial sensor and a pedestrian navigation algorithm;
when the environment observation value of the positioning node label is in a first communication attenuation state, starting the inertial sensor, and not starting the pedestrian navigation algorithm;
and when the environment observation value of the positioning node label is in a good communication state, the inertial sensor is closed, and the pedestrian navigation algorithm is not started.
Preferably, the environmental observations comprise: the signal strength of the tag information received by the base station and the number of base stations receiving the tag information.
Preferably, the time-series state of the environmental observations is a markov model.
Preferably, the signal strength and the distance between the base station and the tag information correspond to the following expression:
where d is the distance, RSSI represents the signal strength, a is the signal strength when the base station and the tag are 1 meter apart, and n represents the environmental attenuation factor.
Preferably, the signal strength of the tag information received by the base station in the environment observation value is:
abs(dr-dt)*k
wherein d isrAnd dtThe distances are respectively calculated according to the signal intensity and the signal flight time, and k is an undetermined coefficient.
Preferably, the number of base stations in the environment observation is n.
Preferably, the hidden markov model requires a training data set to calculate a state transition matrix, the formula for calculating the state transition matrix being:
A＝[aij]wherein
State qiTransfer to qjHas a probability ofijThe frequency of transition in the training set is Aij。
Preferably, the initial hidden state of the hidden markov model is a communication good state.
Preferably, the hidden state determination and prediction in the hidden markov model is performed using the viterbi algorithm.
(III) advantageous effects
The invention has the beneficial effects that: the intelligent management method of the inertial sensor in the indoor positioning technology has the following beneficial effects:
the invention aims at the indoor positioning technology integrating high-precision ultra-wideband positioning and pedestrian navigation algorithm, and intelligently starts the inertial sensor to achieve the purpose of low power consumption.
The invention uses hidden Markov model to judge whether to start the inertial sensor; the hidden Markov model divides the environment of a node to be positioned (hereinafter referred to as a label) into five environments; the observation value of the hidden Markov model comprises the number of each base station for receiving the tag ultra-wideband signal and the signal strength; and the hidden Markov model estimates the environment of the label according to the observation value so as to determine whether to start the inertial sensor.
The method can accurately estimate the positioning environment of the label, intelligently manage the start or the close of the inertial sensor, and achieve the aim of low power consumption, thereby greatly increasing the cruising ability of the label.
Drawings
Fig. 1 is a schematic structural diagram of a positioning technology aimed at in an intelligent management method of an inertial sensor in an indoor positioning technology provided by the present invention.
Description of reference numerals:
1: positioning a base station; 2: ultra-wideband communication; 3: a label; 4: communication; 5: and (4) an upper computer.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
The embodiment provides an intelligent management method of an inertial sensor in an indoor positioning technology, which comprises the following steps:
acquiring an environment observation value of a label of a node to be positioned;
inputting the obtained environment observation value into a hidden Markov model to obtain a hidden state of the hidden Markov model;
judging whether to start the inertial sensor according to the acquired hidden state;
the hidden state includes: a communication good state, a first communication attenuation state, a second communication attenuation state, a communication partial interruption state and a communication complete interruption state;
when the environment observation value of the positioning node label is in any one of a second communication attenuation state, a communication partial interruption state and a communication complete interruption state, simultaneously starting an inertial sensor and a pedestrian navigation algorithm;
when the environment observation value of the positioning node label is in a first communication attenuation state, starting the inertial sensor, and not starting the pedestrian navigation algorithm;
and when the environment observation value of the positioning node label is in a good communication state, the inertial sensor is closed, and the pedestrian navigation algorithm is not started.
It should be noted that: the environment observation value described in this embodiment includes: the signal strength of the tag information received by the base station and the number of base stations receiving the tag information.
In this embodiment, the time-series state of the environment observation value is a markov model.
It should be noted that: the signal strength and the distance of the tag information received by the base station conform to the following expression:
where d is the distance, RSSI represents the signal strength, a is the signal strength when the base station and the tag are 1 meter apart, and n represents the environmental attenuation factor.
The signal strength is greatly influenced by the environment, and the larger the distance difference between the distance d and the ultra-wideband measurement is, the worse the environment is.
Next, in this embodiment, the signal strength of the tag information received by the base station in the environment observation value is:
abs(dr-dt)*k
wherein d isrAnd dtThe distances are respectively calculated according to the signal intensity and the signal flight time, and k is an undetermined coefficient.
It should be noted that: the number of base stations in the environment observation is n.
In this embodiment, the hidden markov model requires a training data set to calculate a state transition matrix, and the formula for calculating the state transition matrix is:
A＝[aij]wherein
State qiTransfer to qjHas a probability ofijThe frequency of transition in the training set is Aij。
The initial hidden state of the hidden markov model in this embodiment is a communication good state.
Finally, it should be said that: in the embodiment, the judgment and prediction of the hidden state in the hidden Markov model are completed by using a Viterbi algorithm.
The embodiment aims at the problem of low power consumption in the indoor positioning technology integrating ultra-wideband and pedestrian navigation, the required indoor positioning technology simultaneously applies high-precision indoor positioning based on ultra-wideband and positioning based on a pedestrian navigation algorithm, and integrates two positioning results. In this positioning technique, it is required to arrange positioning base stations 1 in an environment, the coordinates of which are known. The object to be located is a person and wears a positioning tag 3, which tag 3 is capable of wireless communication 4 with a base station via ultra wideband, as shown in fig. 1. While inside the tag 3 are mounted 9-axis inertial sensors similar to mpu9250, including a three-axis accelerometer, a three-axis gyroscope, a three-axis magnetometer. The fusion of the two algorithms can improve the positioning precision and the anti-interference capability, but can increase the energy consumption and reduce the endurance capability of the tag 3. The intelligent management method for the inertial sensor in the indoor positioning technology, which is realized by the embodiment aiming at the problem, can effectively solve the problem, and the scheme is as follows:
the tag 3 is provided with an inertial sensor and realizes a pedestrian navigation algorithm, but whether the algorithm or the sensor is started is determined by the upper computer 5. The upper computer 5 judges whether to start an inertial sensor and a pedestrian navigation algorithm by using a hidden Markov model, in the model, the environment where the label 3 is located is divided into five intermediate states as hidden states, and correspondingly, the number of each base station and the signal intensity of the signal of the label ultra-wideband communication 2 are received as observation input. The activation of the algorithms and sensors is then managed according to the environmental conditions. When the sensor needs to be started or shut down, the upper computer 5 transmits a message to the tag 3 through the base station.
The time sequence state chain of the environment where the tag 3 (positioning tag) is located is regarded as a markov process in the present embodiment, that is, the probability of the environment state at the current time t is only related to the environment state at the last time t-1. Accordingly, it is divided into five states: a good communication state; a first communication attenuation state; a second communication attenuation state; a communication section interrupt state; a communication complete interruption state. And activation of the pedestrian navigation algorithm and the inertial sensor is associated with the environmental conditions. When the environment of the tag 3 is in a second communication attenuation state, a communication partial interruption state and a communication complete interruption state, the tag 3 starts an inertial sensor and starts a pedestrian navigation algorithm; when the environment of the tag 3 is in a first communication attenuation state, starting an inertial sensor, but not starting a pedestrian navigation algorithm; and when the environment of the tag 3 is in a good communication state, the inertial sensor is closed, and the pedestrian navigation algorithm is not started.
In terms of observation value input of the hidden markov model, the present embodiment mainly refers to two aspects: signal strength, number of base stations receiving tag 3 information.
The following relationship exists between the signal strength and the distance of the tag information received by the base station:
where d is the distance, RSSI represents the signal strength, a is the signal strength when the base station and the tag 3 are 1 meter apart, and n represents the environmental attenuation factor.
The signal strength is greatly influenced by the environment, and the larger the distance difference between the distance d and the ultra-wideband measurement is, the worse the environment is.
One parameter of the environmental condition observation (environmental observation) is:
abs(dr-dt)*k
wherein d isrAnd dtThe distances are respectively calculated according to the signal intensity and the signal flight time, and k is an undetermined coefficient.
Another parameter of the environmental state observation is the number of base stations n, where a larger n indicates a better handover state.
After the model is established, a certain number of training data sets are required to calculate the state transition matrix, and the calculation method comprises the following steps:
A＝[aij]wherein
In the algorithm, the present invention assumes a state qiTransfer to qjHas a probability ofijThe frequency of transition in the training set is Aij。
The initial state of the hidden Markov model is a good communication state, and then the prediction and judgment of the hidden state are completed through a Viterbi algorithm, so that the starting management of a pedestrian navigation algorithm and an inertial sensor is completed.
The technical principles of the present invention have been described above in connection with specific embodiments, which are intended to explain the principles of the present invention and should not be construed as limiting the scope of the present invention in any way. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive efforts, which shall fall within the scope of the present invention.
Claims (5)
1. An intelligent management method for an inertial sensor in an indoor positioning technology is characterized by comprising the following steps:
acquiring an environment observation value of a label of a node to be positioned;
inputting the obtained environment observation value into a hidden Markov model to obtain a hidden state of the hidden Markov model;
judging whether to start the inertial sensor according to the acquired hidden state;
the hidden state includes: a communication good state, a first communication attenuation state, a second communication attenuation state, a communication partial interruption state and a communication complete interruption state;
when the environment observation value of the positioning node label is in any one of a second communication attenuation state, a communication partial interruption state and a communication complete interruption state, simultaneously starting an inertial sensor and a pedestrian navigation algorithm;
when the environment observation value of the positioning node label is in a first communication attenuation state, starting the inertial sensor, and not starting the pedestrian navigation algorithm;
when the environment observation value of the positioning node label is in a good communication state, the inertial sensor is closed, and the pedestrian navigation algorithm is not started;
the environmental observations comprise: the base station receives the signal intensity of the label information and the number of the base stations receiving the label information;
the time sequence state of the environment observation value is a Markov model;
the signal strength and the distance of the tag information received by the base station conform to the following expression:
wherein d is the distance, RSSI represents the signal strength, A is the signal strength when the base station and the tag are at a distance of 1 meter, and n represents the environmental attenuation factor;
the signal strength of the tag information received by the base station in the environment observation value is as follows:
abs(dr-dt)*k
wherein，drAnd dtThe distances are respectively calculated according to the signal intensity and the signal flight time, and k is an undetermined coefficient.
2. The management method according to claim 1,
the number of base stations in the environment observation is n.
3. The method of claim 2, wherein the hidden markov model requires a training data set to compute a state transition matrix, the formula for computing the state transition matrix being:
A＝[aij]wherein
State qiTransfer to qjHas a probability ofijThe frequency of transition in the training set is Aij。
4. The method of claim 3, wherein the initial hidden state of the hidden Markov model is a good communication state.
5. The method of claim 4, wherein the hidden state estimation and prediction in the hidden Markov model is performed using a Viterbi algorithm.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910555406.9A CN110213813B (en) | 2019-06-25 | 2019-06-25 | Intelligent management method for inertial sensor in indoor positioning technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910555406.9A CN110213813B (en) | 2019-06-25 | 2019-06-25 | Intelligent management method for inertial sensor in indoor positioning technology |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110213813A CN110213813A (en) | 2019-09-06 |
| CN110213813B true CN110213813B (en) | 2021-05-14 |
Family
ID=67794641
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910555406.9A Active CN110213813B (en) | 2019-06-25 | 2019-06-25 | Intelligent management method for inertial sensor in indoor positioning technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110213813B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110986952B (en) * | 2019-12-11 | 2023-06-02 | 东北大学 | High-precision anti-interference indoor positioning method for pedestrians |
| CN111174784B (en) * | 2020-01-03 | 2022-10-14 | 重庆邮电大学 | Visible light and inertial navigation fusion positioning method for indoor parking lot |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105445776A (en) * | 2015-12-28 | 2016-03-30 | 天津大学 | Indoor and outdoor seamless positioning system |
| CN105682022A (en) * | 2015-12-30 | 2016-06-15 | 华东师范大学 | Indoor and outdoor seamless positioning system based on Android device and positioning method thereof |
| CN108761514A (en) * | 2018-08-03 | 2018-11-06 | 北斗国信智能科技（北京）有限公司 | A kind of positioning system and localization method merging the Big Dipper or GPS and sensor |
| EP3454073A1 (en) * | 2017-09-08 | 2019-03-13 | SPIE SAG GmbH | System and method for locating within buildings |
| CN109743680A (en) * | 2019-02-28 | 2019-05-10 | 电子科技大学 | A kind of indoor tuning on-line method based on PDR combination hidden Markov model |
| CN109883429A (en) * | 2019-04-15 | 2019-06-14 | 山东建筑大学 | Zero-speed detection method and indoor pedestrian's inertial navigation system based on Hidden Markov Model |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10145934B2 (en) * | 2015-09-14 | 2018-12-04 | Samsung Electronics Co., Ltd. | Terminal and method for measuring location thereof |
-
2019
- 2019-06-25 CN CN201910555406.9A patent/CN110213813B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105445776A (en) * | 2015-12-28 | 2016-03-30 | 天津大学 | Indoor and outdoor seamless positioning system |
| CN105682022A (en) * | 2015-12-30 | 2016-06-15 | 华东师范大学 | Indoor and outdoor seamless positioning system based on Android device and positioning method thereof |
| EP3454073A1 (en) * | 2017-09-08 | 2019-03-13 | SPIE SAG GmbH | System and method for locating within buildings |
| CN108761514A (en) * | 2018-08-03 | 2018-11-06 | 北斗国信智能科技（北京）有限公司 | A kind of positioning system and localization method merging the Big Dipper or GPS and sensor |
| CN109743680A (en) * | 2019-02-28 | 2019-05-10 | 电子科技大学 | A kind of indoor tuning on-line method based on PDR combination hidden Markov model |
| CN109883429A (en) * | 2019-04-15 | 2019-06-14 | 山东建筑大学 | Zero-speed detection method and indoor pedestrian's inertial navigation system based on Hidden Markov Model |
Non-Patent Citations (6)
| Title |
|---|
| A hidden Markov model for indoor user tracking based on WiFi fingerprinting and step detection;M.K. Hoang等;《IEEE》;20140508;全文 * |
| An energy-accuracy tradeoff scheme based on optimal communication policy for HetNet-based indoor localization framework;Jun Xia等;《IEEE》;20160407;全文 * |
| Localization in Wireless Sensor Networks by Hidden Markov Model;R. Arthi等;《IEEE》;20110307;全文 * |
| 基于UWB/PDR的组合室内定位算法;陈隆亮等;《信息技术与网络安全》;20190510;全文 * |
| 基于WIFI和隐马尔可夫模型的室内定位算法研究;王克会;《计算机时代》;20180115;全文 * |
| 基于反馈校正的WLAN与PDR融合定位方法研究;姚团结等;《仪器仪表学报》;20160215;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110213813A (en) | 2019-09-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107659989B (en) | Distributed measurement dormancy and target tracking method for wireless sensor network nodes | |
| CN101458325B (en) | Wireless sensor network tracking method based on self-adapting prediction | |
| CN110213813B (en) | Intelligent management method for inertial sensor in indoor positioning technology | |
| CN101820676B (en) | Sensor node positioning method | |
| CN109212476B (en) | RFID indoor positioning algorithm based on DDPG | |
| CN109597864A (en) | Instant positioning and map constructing method and the system of ellipsoid boundary Kalman filtering | |
| CN106851820B (en) | Positioning method of underground wireless sensor network | |
| CN107576330A (en) | A kind of localization method of the indoor dynamic sensing strategy based on WLAN fingerprints | |
| CN103052128A (en) | Wireless sensor network-based energy-efficient collaborative scheduling method | |
| CN112469115B (en) | Improved wireless sensor network positioning method based on fast clustering multidimensional scaling FC-MDS | |
| CN105898789B (en) | A kind of radio sensor network data convergence method | |
| CN104684081A (en) | Wireless sensor network node localization algorithm based on distance clustering selected anchor nodes | |
| CN101610567A (en) | A kind of dynamic group scheduling method based on wireless sensor network | |
| CN105263149A (en) | Adaptive distributed node re-clustering method in mobile wireless sensor network | |
| EP3143814B1 (en) | Running location provider processes | |
| CN103648164A (en) | Time difference of arrival and Gossip algorithm based wireless sensor network distributed positioning method | |
| CN112556689B (en) | Positioning method integrating accelerometer and ultra-wideband ranging | |
| CN116980834B (en) | Low-power consumption control method and system for Bluetooth beacon | |
| CN112833876A (en) | Multi-robot cooperative positioning method integrating odometer and UWB | |
| Wang et al. | Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering | |
| CN106851800A (en) | A kind of anchor node dispatching method in wireless sensor network positioning | |
| Xiang et al. | Crowdsourcing-based indoor localization with knowledge-aided fingerprint transfer | |
| CN109905846B (en) | Underwater wireless sensor network positioning method based on autonomous underwater vehicle | |
| CN116047410A (en) | UWB indoor pedestrian positioning method based on graph convolution neural network | |
| CN109902744A (en) | A method of it is modified based on exceptional value of the Markov transition matrix to matrix |
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 |