CN112462332B - Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence - Google Patents

Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence Download PDF

Info

Publication number
CN112462332B
CN112462332B CN202011252622.5A CN202011252622A CN112462332B CN 112462332 B CN112462332 B CN 112462332B CN 202011252622 A CN202011252622 A CN 202011252622A CN 112462332 B CN112462332 B CN 112462332B
Authority
CN
China
Prior art keywords
firefly
time
node
sound
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
Application number
CN202011252622.5A
Other languages
Chinese (zh)
Other versions
CN112462332A (en
Inventor
宋浠瑜
王玫
仇洪冰
刘争红
罗丽燕
周陬
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.)
Guilin University of Electronic Technology
Original Assignee
Guilin University of Electronic Technology
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 Guilin University of Electronic Technology filed Critical Guilin University of Electronic Technology
Priority to CN202011252622.5A priority Critical patent/CN112462332B/en
Publication of CN112462332A publication Critical patent/CN112462332A/en
Application granted granted Critical
Publication of CN112462332B publication Critical patent/CN112462332B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Remote Sensing (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Measurement Of Velocity Or Position Using Acoustic Or Ultrasonic Waves (AREA)
  • Circuit For Audible Band Transducer (AREA)

Abstract

The invention discloses an indoor and outdoor positioning method of intelligent terminal users based on an encounter mechanism and group intelligence, which comprises the following steps: 1) Acoustic ranging based on an encounter mechanism; 2) Selecting an acoustic anchor point based on group intelligence; 3) Acoustic receiving point localization based on secondary clustering. The method is independent of infrastructure arrangement of application environments, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in actual application scenes, and can accurately and effectively realize seamless positioning of indoor and outdoor intelligent terminal users.

Description

Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence
Technical Field
The invention relates to the field of research of location services, in particular to an indoor and outdoor positioning method of intelligent terminal users based on meeting mechanism and group intelligence.
Background
The indoor and outdoor seamless positioning of the intelligent terminal user belongs to a research technology of location service, and has important application value in scenes such as tourist attraction pedestrian path planning, industrial manufacturing workshop personnel positioning, solitary old man location safety and the like.
Smart terminals include smart products that users such as smartphones, tablet computers, smartwatches, etc. can carry around, and in these terminals smartphones have become a necessity for life for the end users. The intelligent mobile phone has high storage and high calculation power, and is embedded with abundant sensors, the standard double microphones of the intelligent mobile phone are like human ears, and the intelligent mobile phone has the capability of distinguishing sound intensity, distance and direction, and particularly is an integrated device integrating sound receiving and transmitting, so that the intelligent mobile phone becomes an acoustic network node which is most easy to acquire and arrange.
How to utilize the group wisdom of smart mobile phone users, through the conscious or unconscious participation mode of the smart mobile phone users, the perception task depending on professional staff and professional equipment in the traditional mode is completed, so that more manpower and material resources are released, and the smart mobile phone users become research hotspots for front-end perception of various intelligent systems. Crowd-Sensing (Crowd-Sensing) is a new data acquisition mode combining Crowd-sourced ideas and Sensing capability of mobile equipment, is a representation form of the Internet of things, can effectively utilize mobile equipment carried by people to form an interactive and participatory Sensing network, and distributes Sensing tasks to individuals or groups in the network through a cloud server to finish, thereby helping professionals or public collect data, analyze information and share knowledge, and breaking through barriers participated by professionals. The crowd sensing has the advantages of flexible and economical deployment, multi-source heterogeneous sensing data, wide and uniform coverage range, high expansion and multiple functions and the like.
In an actual application scene, the walking state of the intelligent terminal user is like a random walk mode, and the intelligent mobile phone carried by the intelligent terminal user can form a dynamically-changed acoustic node network. Although the network has stronger dynamic change characteristic, each network node (smart phone carried by the intelligent terminal user) can still "sense" to the neighbor nodes in the sight distance range by adopting a crowd sensing mode, and the phenomenon is the same as that of the phenomenon that each person can exchange information with the nearby close-range people in the tide. The mobile acoustic sensing mode of a plurality of intelligent mobile phones based on crowd sensing is certainly the optimal choice for the front end sensing of the intelligent terminal user position estimation system. However, how to select effective neighbor nodes based on the meeting mechanism in such a dynamic network to realize effective interaction of node information, so that each node can obtain necessary information required for self-position estimation is a considerable problem.
At present, an indoor and outdoor seamless positioning system has completely different indoor and outdoor (GPS or Beidou) positioning systems, and the problems of incompatibility in system conversion and the like exist among the systems, so that the practical application performance of the system is seriously affected. The basic principle of outdoor GPS/Beidou positioning is considered, and the related research results of the current indoor multisection cooperative positioning system are combined, so that the three-point positioning principle is not only suitable for outdoor GPS/Beidou positioning, but also suitable for indoor sensor network node positioning.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides an indoor and outdoor positioning method for intelligent terminal users based on an encounter mechanism and crowd intelligence. The method is independent of infrastructure arrangement of application environments, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in actual application scenes, and can accurately and effectively realize seamless positioning of indoor and outdoor intelligent terminal users.
The technical scheme for realizing the aim of the invention is as follows:
an indoor and outdoor positioning method of intelligent terminal users based on an encounter mechanism and group intelligence comprises the following steps:
1) Acoustic ranging based on encounter mechanism: when two intelligent terminal users are in the sight distance range, the carried intelligent terminals call a loudspeaker and a microphone of the intelligent terminal to transmit and record Chirp sound signals through Chirp sound APP, the transmitted sound signals comprise two information of an ID and sound producing time Te of the transmitting terminal, the recorded sound signals implicitly correspond to receiving time Tr of each sound event, a time domain generalized cross correlation algorithm is adopted to easily obtain time Tr, for any node, the ID and sound producing time Te of the transmitting terminal are extracted from signals received by any node, and the time Tr of receiving the ID sound signals is set to be IDa and IDb respectively, sound producing time is Tae and Tbe respectively, and the time of receiving the sound signals sent by the other side is Tbr and Tar respectively, and as all the terminals are in the same network, the nodes A and B can directly calculate single pass time ETb and OAETa formula (1) of the Chirp sound signals by adopting consistent network synchronization time, as shown in the formula (1):
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d= (etoaa+etoab) ·c/2, where c is the speed of sound, the acoustic signal received by any target node is often the acoustic signal emitted by multiple nodes in the scene, and mixed with environmental noise, so the distance value calculated by the target node from the received signal is usually calculated asAggregationThe representation is: />v represents the total number of nodes in the scene, if know +.>The preferred line-of-sight distance value in the set, namely the anchor point distance corresponding to the group intelligent-based anchor point selection result, is obtained by the arrival time difference (Time Difference of Arrival, TDOA) algorithm, how to select from ∈>The screening of the optimal line-of-sight distance value is integrated and is the key of the target node position;
2) Group intelligence-based selection of acoustic anchor points: the firefly algorithm is adopted to complete the slaveSelecting a better node from nodes corresponding to each set element as an acoustic anchor point to execute P value estimation of the target node position based on a TDOA algorithm, wherein the position of each firefly represents +.>The node position corresponding to each distance value in the set represents a feasible solution of the to-be-solved problem, namely the apparent distance value, the brightness of the firefly represents the fitness of the position of the firefly, namely the node position, the higher the brightness is, the better the position of a firefly individual in a solution space is, namely the higher the possibility of being used as an apparent distance node is, each firefly adopts a roulette rule, moves towards all fireflies with higher brightness than the firefly, and the transition probability of the ith firefly to the jth firefly is shown in a formula (2):
in the formula (2), l j (q)、l i (q) and l k (q) represents the luciferin values of the jth firefly, the ith firefly and the kth firefly at the qth generation, respectively, and is determined by the q-1 th generation luciferin value and the current position fitness together:
l i (q)＝(1-ρ)l i (q-1)+γJ(x i (q)) (3)，
in the formula (3), ρ ε [0,1 ]]Represents the volatilization factor of fluorescein, gamma E [0,1 ]]Representing the update rate of fluorescein, x i (q) represents the position of the ith firefly at the q-th generation, since the position of the firefly determines the luciferin value of the firefly, that is, the brightness of the firefly, which directly reflects the fitness of the position of the firefly, the fitness corresponds to the viewing distance value involved, and therefore,when fireflies are in the q-th generation, the sight distance corresponding to each node has the same reference of firefly positions, and is expressed as X= (X) 1 (q),…,x v (q)) the individual with the greatest brightness in the firefly population corresponds to the individual with the better fitness, namely the individual with the better line-of-sight distance, and the firefly position updating formula (4) shows:
in the formula (4), alpha represents the step factor of disturbance, and the value range is [0,1 ]]And II represents Euclidean norm operator, an updated equation formula (4) is adopted, the fitness value of a new position of the firefly after flying to all other individuals with higher brightness than the firefly is calculated, if the new position is better than the position before flying, the firefly flies to the new position, otherwise, the firefly stays in place, and when the maximum iteration number is reached, the searched optimal position of the firefly is used as a solution setOutputting, otherwise returning to formula (3), continuing with a further iteration of the update, obviously +.>Is->Subset of->
3) Acoustic receiving point positioning based on secondary clustering: for a pair ofThe inner distance values are ordered from small to large, 4 minimum distance values are selected, and the position P of the target node is obtained by adopting a TDOA algorithm, however, the application premise of obtaining the position of the target node is that the relative position of the target node and the neighbor nodes in the sight distance range is kept unchanged, and the application assumption is that the relative position is difficult to be ensured in an actual scene, and the reasons are that: the node's movements are random, not only the target node's position changes over time, but its neighbor node's position also changes over time, assuming that it is within the time period t { Δt 1 ,…Δt m Within the process, the target node moves n steps, wherein n is obtained by the reading of the accelerometer arranged in the smart phone and is considered as a known value, and the target node performs deltat in each unit time m Position estimation in +.>The optimal line-of-sight neighbor 4 nodes provided by the step 1) and the step 2) are obtained by a three-point positioning model based on the acoustic arrival time difference, namely TDOA algorithm, and finally, the time delta t of each unit time is calculated m Position estimation in +.>After secondary clustering based on K means (Kmeans) algorithm, the optimal position estimated value P of the target node at the current time t can be obtained t 。
The Chirp sound APP in the step 1) is soft-written character 2653065 and named as Chirp sound recording software, and Chirp sound signals transmitted by the Chirp sound recording software are located in the upper limit frequency band of human ear hearing, so that hearing interference is not caused to an end user.
In the technical scheme, in the process of resolving TDOA, the selection of the neighbor nodes in the sight distance range is key, and the selection is based on the inverse square law of the distance of sound propagation in an air medium, namely, the sound propagation path between the target node and the neighbor nodes in the sight distance range is shorter than that of the non-sight distance neighbor nodes, so that the sound energy of sound signals propagated in the air is less attenuated, and based on the fact, a firefly algorithm in group intelligence can be the first choice for screening the neighbor nodes in the sight distance range of the target node. More importantly, firefly algorithms are relatively intelligent to other mainstream populations such as: the genetic algorithm, the ant colony algorithm, the flora algorithm, the frog-leaping algorithm or the artificial bee colony algorithm and the like have fewer optimization parameters and lower algorithm complexity, and can ensure timeliness, accuracy and stability of algorithm running in real time, so that the technical scheme can rapidly and accurately screen 4 network nodes which are more suitable to be used as acoustic anchor points from a plurality of view-range neighbor nodes and serve as optimal view-range neighbor nodes for determining the position of a target node. In order to further reduce the estimation error of the target node position, the technical scheme utilizes the secondary clustering idea to carry out clustering on the target node position estimation set, and takes the position of the central cluster head as the optimal position estimation of the target node.
Aiming at the problem that the actual performance of an indoor and outdoor seamless positioning system facing intelligent terminal users is difficult to meet the position service requirement due to incompatibility of high dynamic property of indoor and outdoor environments and system conversion of a positioning system, the intelligent terminal user positioning optimization method based on an meeting mechanism and group intelligence is provided. The method is independent of infrastructure arrangement of application environments, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in actual application scenes, and can accurately and effectively realize seamless positioning of indoor and outdoor intelligent terminal users. The technical scheme is not only suitable for indoor environments, but also suitable for outdoor environments, is particularly suitable for places with crowd gathering, such as airports, museums, large-scale exhibition halls and the like, and can meet the application requirements of current position sensing services on indoor and outdoor seamless positioning.
The method is independent of infrastructure arrangement of application environments, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in actual application scenes, and can accurately and effectively realize seamless positioning of indoor and outdoor intelligent terminal users.
Drawings
FIG. 1 is a schematic flow chart of a method of an embodiment;
FIG. 2 is a schematic diagram of an acoustic ranging principle based on an encounter mechanism in an embodiment;
FIG. 3 is a flow chart diagram of an intelligent end user location optimization calculation in an embodiment.
Detailed Description
The present invention will now be further illustrated, but not limited, by the following figures and examples.
Examples:
referring to fig. 1, an indoor and outdoor positioning method for intelligent terminal users based on an encounter mechanism and group intelligence comprises the following steps:
1) Acoustic ranging based on encounter mechanism: in the practical application scenario, when two intelligent terminal users are in the sight distance range, the carried intelligent terminal calls a loudspeaker and a microphone of the intelligent terminal to transmit and record a Chirp sound signal through a Chirp sound APP, the transmitted sound signal comprises two information of an ID of the transmitting terminal and sound producing time Te, the recorded sound signal implies a receiving time Tr corresponding to each sound event, a time domain generalized cross correlation algorithm is adopted to easily obtain the time Tr, for any node, the ID and sound producing time Te of the transmitting terminal and the time Tr for receiving the ID sound signal are extracted from the signal received by any node, any two neighbor nodes A and node B are set, the terminal IDs corresponding to the nodes A and the node B are respectively IDa and IDb, the sound producing time is Tae and Tbe, the time for receiving the sound signal sent by the other party is respectively Tbr and Tar, and as all the terminals are in the same network, the nodes A and B can directly calculate the flight time of the Chirp sound signal (OAETa single pass formula 1) as shown in the single pass formula:
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d= (etoaa+etoab) ·c/2, where c is the speed of sound, the acoustic signal received by any target node is often the acoustic signal emitted by multiple nodes in the scene and mixed with environmental noise, so the distance values that the target node has solved from the received signal are usually in a setThe representation is: />v represents the total number of nodes in the scene, if know +.>The preferred line-of-sight distance value in the set, namely the anchor point distance corresponding to the group intelligent-based anchor point selection result, then the target node position P is obtained by the arrival time difference TDOA algorithm, how to select from +.>The screening of the optimal line-of-sight distance value is integrated and is the key of the target node position;
2) Group intelligence-based selection of acoustic anchor points: the firefly algorithm is adopted to complete the slaveSelecting a better node from nodes corresponding to each set element as an acoustic anchor point to execute P value estimation of the target node position based on a TDOA algorithm, wherein the position of each firefly represents +.>The node position corresponding to each distance value in the set represents a feasible solution of the to-be-solved problem, namely the apparent distance value, the brightness of the firefly represents the fitness of the position of the firefly, namely the node position, the higher the brightness is, the better the position of a firefly individual in a solution space is, namely the higher the possibility of being used as an apparent distance node is, each firefly adopts a roulette rule, moves towards all fireflies with higher brightness than the firefly, and the transition probability of the ith firefly to the jth firefly is shown in a formula (2):
in the formula (2), l j (q)、l i (q) and l k (q) represents the luciferin values of the jth firefly, the ith firefly and the kth firefly at the qth generation, respectively, and is determined by the q-1 th generation luciferin value and the current position fitness together:
l i (q)＝(1-ρ)l i (q-1)+γJ(x i (q)) (3)，
in the formula (3), ρ ε [0,1 ]]Represents the volatilization factor of fluorescein, gamma E [0,1 ]]Representing the update rate of fluorescein, x i (q) represents the position of the ith firefly in the q-th generation, since the position of the firefly determines the luciferin value of the firefly, that is, the brightness of the firefly, which directly reflects the fitness of the position of the firefly, the fitness corresponds to the line-of-sight distance value according to the present example, and therefore,when fireflies are in the q-th generation, the sight distance corresponding to each node has the same reference of firefly positions, and is expressed as X= (X) 1 (q),…,x v (q)) the individual with the greatest brightness in the firefly population corresponds to the individual with the better fitness, namely the individual with the better line-of-sight distance, and the firefly position updating formula (4) shows:
in the formula (4), alpha represents the step factor of disturbance, and the value range is [0,1 ]]And II represents Euclidean norm operator, an updated equation formula (4) is adopted, the fitness value of a new position of the firefly after flying to all other individuals with higher brightness than the firefly is calculated, if the new position is better than the position before flying, the firefly flies to the new position, otherwise, the firefly stays in place, and when the maximum iteration number is reached, the searched optimal position of the firefly is used as a solution setOutputting, otherwise returning to formula (3), continuing with a further iteration of the update, obviously +.>Is->Subset of->
3) Acoustic receiving point positioning based on secondary clustering: for a pair ofThe inner distance values are ordered from small to large, 4 minimum distance values are selected, and the position P of the target node is obtained by adopting a TDOA algorithm, however, the application premise of obtaining the position of the target node is that the relative position of the target node and the neighbor nodes in the sight distance range is kept unchanged, and the application assumption is that the relative position is difficult to be ensured in an actual scene, and the reasons are that: the node's movements are random, not only the target node's position changes over time, but its neighbor node's position also changes over time, as shown in FIG. 3, assuming that during period t { Δt } 1 ,…Δt m Within the process, the target node moves n steps, wherein n is obtained by the reading of the accelerometer arranged in the smart phone and is considered as a known value, and the target node performs deltat in each unit time m Position estimation in +.>The optimal line-of-sight neighbor 4 nodes provided by the step 1) and the step 2) are obtained by a three-point positioning model based on the acoustic arrival time difference, namely TDOA algorithm, and finally, the time delta t of each unit time is calculated m Position estimation in +.>After secondary clustering based on K means (Kmeans) algorithm, the optimal position estimated value P of the target node at the current time t can be obtained t 。
The Chirp sound APP in the step 1) is soft-written character 2653065 and named as Chirp sound recording software, and Chirp sound signals transmitted by the Chirp sound recording software are located in the upper limit frequency band of human ear hearing, so that hearing interference is not caused to an end user.

Claims (2)

1. An indoor and outdoor positioning method of intelligent terminal users based on an encounter mechanism and group intelligence is characterized by comprising the following steps:
1) Acoustic ranging based on encounter mechanism: when two intelligent terminal users are in a viewing distance range, the carried intelligent terminals call a loudspeaker and a microphone of the intelligent terminal to transmit and record Chirp sound signals through Chirp sound APP, the transmitted sound signals comprise two information of an ID of the transmitting terminal and sound producing time Te, the recorded sound signals implicitly correspond to receiving time Tr of each sound event, a time domain generalized cross correlation algorithm is adopted to obtain time Tr, for any node, the ID and sound producing time Te of the transmitting terminal and the time Tr of the receiving the ID sound signals are extracted from signals received by any node, any two neighbor nodes A and B are set, the terminal IDs corresponding to the nodes A and B are respectively IDa and IDb, the sound producing time is Tae and Tbe, the time of receiving the sound signals sent by the other party is Tbr and Tar, and the nodes A and B can directly calculate single-pass flight time OAb and OAa of the Chirp sound signals as shown in formula (1):
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d= (etoaa+etoab) ·c/2, where c is the speed of sound, the distance values the target node solves out from the received signal are aggregatedThe representation is: />v represents the total number of nodes in the scene, knowing +.>The optimal line-of-sight distance value in the set, namely the anchor point distance corresponding to the group intelligent-based acoustic anchor point selection result, is obtained by the arrival time difference TDOA algorithm;
2) Group intelligence-based selection of acoustic anchor points: the firefly algorithm is adopted to complete the slaveSelecting a better node from nodes corresponding to each set element as an acoustic anchor point to execute P value estimation of the target node position based on a TDOA algorithm, wherein the position of each firefly represents +.>The node position corresponding to each distance value in the set represents one feasible solution of the to-be-solved problem, namely, the apparent distance value, and the brightness of fireflies represents the fitness of the firefly position, namely, the node position, each firefly adopts a roulette rule, moves towards all fireflies with brightness higher than that of the firefly, and the transition probability of the ith firefly to the jth firefly is shown in a formula (2):
in the formula (2), l j (q)、l i (q) andl k (q) represents the luciferin values of the jth firefly, the ith firefly and the kth firefly at the qth generation, respectively, and is determined by the q-1 th generation luciferin value and the current position fitness together:
l i (q)＝(1-ρ)l i (q-1)+γJ(x i (q)) (3)，
in the formula (3), ρ ε [0,1 ]]Represents the volatilization factor of fluorescein, gamma E [0,1 ]]Representing the update rate of fluorescein, x i (q) represents the position of the ith firefly in the q-th generation, the position of the firefly determines the luciferin value of the firefly, namely the brightness of the firefly, and the brightness directly reflects the adaptability of the position of the firefly, and the adaptability corresponds to the related viewing distance value, therefore,when fireflies are in the q-th generation, the sight distance corresponding to each node has the same reference of firefly positions, and is expressed as X= (X) 1 (q),…,x v (q)) the individual with the greatest brightness in the firefly population corresponds to the individual with the better fitness, namely the individual with the better line-of-sight distance, and the firefly position updating formula (4) shows:
in the formula (4), alpha represents the step factor of disturbance, and the value range is [0,1 ]]And II represents Euclidean norm operator, an updated equation formula (4) is adopted, the fitness value of a new position of the firefly after flying to all other individuals with higher brightness than the firefly is calculated, if the new position is better than the position before flying, the firefly flies to the new position, otherwise, the firefly stays in place, and when the maximum iteration number is reached, the searched optimal position of the firefly is used as a solution setOutput, otherwise return to equation (3), continue with another round of iterative update, +.>Is->Subset of->
3) Acoustic receiving point positioning based on secondary clustering: for a pair ofThe distance values in the range are sequenced from small to large, 4 minimum distance values are selected, and the TDOA algorithm is adopted to obtain the position P of the target node, however, the application premise of obtaining the position of the target node is that the relative position of the target node and the neighbor nodes in the sight distance range is kept unchanged, and the assumption is that in the period t { delta t 1 ,…Δt m Within the process, the target node moves n steps, wherein n is obtained by the reading of the accelerometer arranged in the smart phone and is considered as a known value, and the target node performs deltat in each unit time m Position estimation in +.>The optimal line-of-sight neighbor 4 nodes provided by the step 1) and the step 2) are obtained by a three-point positioning model based on the acoustic arrival time difference, namely TDOA algorithm, and finally, the time delta t of each unit time is calculated m Position estimation in +.>After secondary clustering based on K means (Kmeans) algorithm, the optimal position estimated value P of the target node at the current time t can be obtained t 。
2. The indoor and outdoor positioning method for intelligent terminal users based on an encounter mechanism and group intelligence according to claim 1, wherein the Chirp sound APP in step 1) is soft-written dendriform 2653065 and named as Chirp sound recording software, and Chirp sound signals transmitted by the Chirp sound recording software are located in an upper limit frequency band of human ear hearing and do not cause hearing interference to the terminal users.
CN202011252622.5A 2020-11-11 2020-11-11 Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence Active CN112462332B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011252622.5A CN112462332B (en) 2020-11-11 2020-11-11 Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011252622.5A CN112462332B (en) 2020-11-11 2020-11-11 Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence

Publications (2)

Publication Number Publication Date
CN112462332A CN112462332A (en) 2021-03-09
CN112462332B true CN112462332B (en) 2023-07-18

Family

ID=74825426

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011252622.5A Active CN112462332B (en) 2020-11-11 2020-11-11 Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence

Country Status (1)

Country Link
CN (1) CN112462332B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110456815A (en) * 2019-07-04 2019-11-15 北京航空航天大学 It is a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7480395B2 (en) * 2002-06-06 2009-01-20 Techteam Government Solutions, Inc. Decentralized detection, localization, and tracking utilizing distributed sensors

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110456815A (en) * 2019-07-04 2019-11-15 北京航空航天大学 It is a kind of based on the heuristic intelligent unmanned plane cluster co-located method of army antenna

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于智能手机声信号的自标定室内定位系统;林峰;张磊;李贵楠;王智;;计算机研究与发展(12);全文 *

Also Published As

Publication number Publication date
CN112462332A (en) 2021-03-09

Similar Documents

Publication Publication Date Title
JP7096343B2 (en) A method for obtaining positioning information in a wireless communication system and a device for that purpose.
US20210084607A1 (en) Locating a mobile station and applications therefor
Zhang et al. Mobility prediction: A survey on state-of-the-art schemes and future applications
Abualsaud et al. A survey on mobile crowd-sensing and its applications in the IoT era
Bonné et al. WiFiPi: Involuntary tracking of visitors at mass events
Lorincz et al. Motetrack: A robust, decentralized approach to rf-based location tracking
Lorincz et al. MoteTrack: a robust, decentralized approach to RF-based location tracking
Savvides et al. Localization in sensor networks
US10952041B2 (en) Control device and method for processing a service using a plurality of sensor devices in a distributed manner while suppressing a communication load
Fahmy WSNs applications
Kouyoumdjieva et al. Survey of non-image-based approaches for counting people
WO2014096861A2 (en) Methods and apparatus for forming image using, and finding positions of, plural pixel devices
Li et al. Cooperative positioning and tracking in disruption tolerant networks
CN107624249A (en) Method and system for the number in the aggregated data real-time counting crowd by communication network
WO2021088970A1 (en) Sounding reference signal transmission setting method, information configuration method, positioning method, and related device
CN106416319A (en) Deriving relationships from overlapping location data
CN108919328A (en) A kind of indoor and outdoor seamless positioning system and localization method
JP6934623B2 (en) Communication control method, telepresence robot, and communication control program
CN112462332B (en) Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence
Chen et al. Locating in crowdsourcing-based dataspace: wireless indoor localization without special devices
Aboueleneen et al. Deep reinforcement learning for internet of drones networks: issues and research directions
Radu et al. Acoustic noise pollution monitoring in an urban environment using a VANET network
US11688178B1 (en) System and method for calculating regional crowd movement
Abd Warif et al. IoT-based Smart Parking System using Android Application
Gayan et al. Sensor network based adaptable system architecture for emergency situations

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