CN112462332A - Intelligent terminal user indoor and outdoor positioning method based on encounter mechanism and group intelligence - Google Patents

Intelligent terminal user indoor and outdoor positioning method based on encounter mechanism and group intelligence Download PDF

Info

Publication number
CN112462332A
CN112462332A CN202011252622.5A CN202011252622A CN112462332A CN 112462332 A CN112462332 A CN 112462332A CN 202011252622 A CN202011252622 A CN 202011252622A CN 112462332 A CN112462332 A CN 112462332A
Authority
CN
China
Prior art keywords
firefly
node
target node
value
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011252622.5A
Other languages
Chinese (zh)
Other versions
CN112462332B (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

Images

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 intelligent terminal user indoor and outdoor positioning method based on an encounter mechanism and swarm intelligence, which comprises the following steps: 1) acoustic ranging based on an encounter mechanism; 2) group intelligence based acoustic anchor selection; 3) quadratic clustering based acoustic receive point localization. The method does not depend on infrastructure arrangement of an application environment, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in practical application scenes, and accurately and effectively realizes seamless positioning of indoor and outdoor intelligent terminal users.

Description

Intelligent terminal user indoor and outdoor positioning method based on encounter mechanism and group intelligence
Technical Field
The invention relates to the research field of location services, in particular to an intelligent terminal user indoor and outdoor positioning method based on an encounter mechanism and swarm intelligence.
Background
Indoor and outdoor seamless positioning of an intelligent terminal user belongs to a research technology of position service, and has important application value in scenes such as pedestrian path planning in tourist attractions, personnel positioning in industrial manufacturing workshops, position safety of solitary old people and the like.
The intelligent terminals comprise intelligent products which can be carried about by users, such as intelligent mobile phones, tablet computers, intelligent watches and the like, and in the terminals, the intelligent mobile phones become necessary products for the life of the terminal users. The smart phone has high storage and calculation capacity, is embedded with abundant sensors, has the capacity of distinguishing the sound intensity, distance and direction as two standard microphones of a person are arranged on the two ears of the person, and particularly is an integrated device integrating sound receiving and transmitting, so that the smart phone becomes an acoustic network node which is most easily acquired and arranged.
How to utilize the crowd wisdom of smart phone users and through the conscious or unconscious participation mode of the smart phone users, the perception task depending on professionals and professional equipment in the traditional mode is completed, so that more manpower and material resources are released, and the method becomes a research hotspot for front-end perception of various intelligent systems. The Crowd-Sensing (Crowd-Sensing) is a new data acquisition mode combining crowdsourcing thought and mobile equipment Sensing capability, is an expression form of the Internet of things, can effectively utilize mobile equipment carried by people to form an interactive and participatory Sensing network, and issues a Sensing task to be distributed to individuals or groups in the network through a cloud server to complete, so that professionals or the public are helped to collect data, analyze information and share knowledge, and barriers for participation of the professionals are broken through. The crowd sensing has the advantages of flexible and economical deployment, multi-source heterogeneous sensing data, wide and uniform coverage range, high-expansion multifunction and the like.
In an actual application scenario, the walking state of the intelligent terminal users is the same as a random walk mode, and the smart phones carried by the intelligent terminal users can form a dynamically-changing acoustic node network. Although the network has stronger dynamic change characteristics, each network node (a smart phone carried by a smart terminal user) can still sense a neighbor node within the visual distance range of the network node by adopting a crowd sensing mode, and the phenomenon is the same as that in a tide, everyone can exchange information with people in close distance around the network node. The mobile acoustic perception mode of multiple smart phones based on crowd sensing is undoubtedly the optimal choice for front-end perception of the intelligent terminal user position estimation system. However, how to select effective neighbor nodes based on an encounter mechanism in such a dynamic network to achieve effective interaction of node information, so that each node can obtain necessary information required for self position estimation is a considerable problem.
At present, indoor and outdoor seamless positioning systems have completely different positioning systems (GPS or Beidou) indoors and outdoors, and the problems of incompatibility of system conversion and the like exist between the systems, so that the practical application performance of the system is seriously influenced. The basic principle of outdoor GPS/Beidou positioning is considered, and the three-point positioning principle is not only suitable for outdoor GPS/Beidou positioning, but also suitable for indoor sensor network node positioning by combining related research results of the conventional indoor multi-section cooperative positioning system.
Disclosure of Invention
The invention aims to provide an intelligent terminal user indoor and outdoor positioning method based on an encounter mechanism and group intelligence aiming at the defects of the prior art. The method does not depend on infrastructure arrangement of an application environment, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in practical application scenes, and accurately and effectively realizes seamless positioning of indoor and outdoor intelligent terminal users.
The technical scheme for realizing the purpose of the invention is as follows:
an intelligent terminal user indoor and outdoor positioning method 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 a sight distance range, the intelligent terminals carried by the users call a loudspeaker and a microphone of the intelligent terminal to transmit and record Chirp sound signals through a Chirp sound APP, the transmitted sound signals comprise two items of information of ID and sounding time Te of the transmitting terminal, the recorded sound signals imply receiving time Tr corresponding to each sound event, the time Tr is easily obtained by adopting a time domain generalized cross-correlation algorithm, for any node, the ID and sounding time Te of the transmitting terminal and the time Tr for 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 IDb respectively, the sounding time is Tae and Tbe, the time for receiving the sound signals sent by the other party is Tbr and Tar respectively, and all the terminals are in the same network, with consistent network synchronization time, nodes a and B can directly calculate the one-way flight times etaob and etaaa of the Chirp acoustic signals as shown in equation (1):
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d ═ eta + eta) · c/2, where c is the speed of sound, and the acoustic signals received by any target node, often the acoustic signals emitted by multiple nodes in the scene, are mixed with environmental noise, so the distance values calculated by the target node from the received signals are usually collected in an aggregate manner
Figure BDA0002772071750000023
Represents:
Figure BDA0002772071750000021
v denotes the total number of nodes in the scene, if known
Figure BDA0002772071750000022
The optimal line-of-sight distance value in the set, i.e., the anchor point distance corresponding to the group-intelligence-based acoustic anchor point selection result, is the target node position P obtained by a Time Difference of Arrival (TDOA) algorithm, how to derive the target node position P from the TDOA
Figure BDA0002772071750000031
The optimal sight distance value is selected in an aggregation mode and is the key of the position of the target node;
2) group intelligence-based acoustic anchor selection: using firefly algorithm to accomplish from
Figure BDA0002772071750000032
Selecting nodes corresponding to each set elementThe superior node is selected as the acoustic anchor point to perform the P value estimation of the target node position based on the TDOA algorithm, wherein the position of each firefly represents
Figure BDA0002772071750000033
The node position corresponding to each distance value in the set represents a feasible solution of a problem to be solved, namely a sight distance value, the brightness of the firefly represents the fitness of the firefly position, namely the node position, the position of the firefly individual with higher brightness in a solution space is better, namely the probability of serving as a sight distance node is higher, each firefly adopts a roulette rule and moves towards all fireflies with higher brightness than the firefly, and the transition probability of the ith firefly moving to the jth firefly is shown in a formula (2):
Figure BDA0002772071750000034
in the formula (2), lj(q)、li(q) and lk(q) respectively represents the fluorescein values of the jth firefly, the ith firefly and the kth firefly in the q generation, and is jointly determined by the fluorescein value of the q-1 generation and the current position fitness:
li(q)＝(1-ρ)li(q-1)+γJ(xi(q)) (3)，
in the formula (3), rho is in [0,1 ]]Represents the volatilization factor of fluorescein, gamma is from [0,1 ]]Indicates the renewal rate of fluorescein, xi(q) represents the location of the ith firefly in the qth generation, since the location of the firefly determines the firefly's fluorescein value, i.e., the brightness of the firefly, which in turn directly reflects the fitness of the firefly's location, which corresponds to the stadia distance value involved,
Figure BDA00027720717500000311
in the q-th generation of firefly, which is a v number of nodes in the set, the apparent distance corresponding to each node has the same reference as the firefly position, and is represented by X ═ X (X ═ X)1(q),…,xv(q)), the individual with the highest brightness among the firefly population corresponds to the fitnessThe individuals with better response, i.e. the individuals with better sight distance, are shown in formula (4):
Figure BDA0002772071750000035
in the formula (4), α represents a step-size factor of disturbance, and the value range is [0,1 ]]And | represents the Euclidean norm operator, the fitness value of a new position where the firefly arrives after flying to all other individuals with higher brightness than the firefly is calculated by adopting an updated equation formula (4), if the new position is superior to the position before flying, the firefly flies to the new position, otherwise, the firefly stays in the original position, and when the maximum iteration number is reached, the searched optimal firefly position is used as a solution set
Figure BDA0002772071750000036
And outputting, otherwise returning to the formula (3), and continuing to perform another round of iterative updating, obviously,
Figure BDA0002772071750000037
is that
Figure BDA0002772071750000038
Is selected from the group consisting of (a) a subset of,
Figure BDA0002772071750000039
3) acoustic receive point localization based on quadratic clustering: to pair
Figure BDA00027720717500000310
The distance values in the target node are sorted 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 for obtaining the position of the target node is that the relative position of the target node and the sight distance range neighbor node of the target node is kept unchanged, and the application assumption is that under an actual scene, the application is often difficult to ensure because: the movement of a node has randomness, and the position of a target node changes along with time, and the positions of neighbor nodes of the target node also change along with time, which is assumed to beTime period t [ Delta t ]1,…ΔtmWithin the period, the target node moves by n steps, wherein the value of n is obtained by reading of an accelerometer arranged in the smart phone and is considered as a known value, and the target node is located at each unit time delta tmIntra-location estimation
Figure BDA0002772071750000042
Obtained by 4 nodes which are adjacent to the optimal sight distance and provided by the step 1) and the step 2) and a three-point positioning model based on sound arrival time difference, namely TDOA algorithm, and finally, for each unit time delta tmIntra-location estimation
Figure BDA0002772071750000041
After secondary clustering is carried out based on a K mean (Kmeans) algorithm, the optimal position estimated value P of the target node at the current moment t can be obtainedt。
The Chirp sound APP in the step 1) is soft typewriting No. 2653065 named Chirp sound recording software, and Chirp sound signals transmitted by the Chirp sound recording software are located in the upper limit frequency range of human ear hearing, so that hearing interference is not caused to a terminal user.
In the process of resolving TDOA, the selection of the neighbor nodes in the sight distance range is key, the selection is based on the inverse square law of the distance of sound propagating in the 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 attenuation of sound signals propagating in the air is less, and based on the principle, the firefly algorithm in the swarm intelligence can be the first choice for screening the neighbor nodes in the sight distance range of the target node. More importantly, the firefly algorithm is relatively more intelligent than other mainstream group algorithms, such as: for genetic algorithm, ant colony algorithm, flora algorithm, frog leaping algorithm or artificial bee colony algorithm and the like, the method has less optimization parameters and lower algorithm complexity, and can ensure timeliness, accuracy and stability of real-time operation of the algorithm, so that 4 network nodes more suitable for serving as acoustic anchor points are quickly and accurately screened out from the neighbor nodes in a plurality of visual range and serve as the optimal visual range neighbor nodes for determining the position of the target node. Considering that the moving mode of each terminal user has randomness and the adjacent nodes in the view distance range of the target node have strong time dynamics, in order to further reduce the estimation error of the position of the target node, the technical scheme utilizes the secondary clustering idea to cluster the estimation set of the position of the target node and takes the position of the center cluster head as the optimal estimation value of the position of the target node.
The technical scheme provides an intelligent terminal user positioning optimization method based on an encounter mechanism and swarm intelligence aiming at the problem that the actual performance of an indoor and outdoor seamless positioning system facing an intelligent terminal user is difficult to meet the position service requirement due to incompatibility of high dynamics of indoor and outdoor environments and system conversion of a positioning system. The method does not depend on infrastructure arrangement of an application environment, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in practical application scenes, and accurately and effectively realizes seamless positioning of indoor and outdoor intelligent terminal users. The technical scheme is not only suitable for indoor environment, but also suitable for outdoor environment, is particularly suitable for places where people gather, such as airports, museums, large exhibition halls and the like, and can meet the application requirements of current position sensing service on indoor and outdoor seamless positioning.
The method does not depend on infrastructure arrangement of an application environment, is not limited to indoor or outdoor single application scenes, can fully utilize the dynamic advantages of multiple users in practical application scenes, and accurately and effectively realizes seamless positioning of indoor and outdoor intelligent terminal users.
Drawings
FIG. 1 is a schematic flow chart of an exemplary method;
FIG. 2 is a schematic diagram illustrating the acoustic ranging principle based on the encounter mechanism in the embodiment;
FIG. 3 is a schematic diagram of a calculation flow of intelligent terminal user location optimization in the embodiment.
Detailed Description
The invention will be further elucidated with reference to the drawings and examples, without however being limited thereto.
Example (b):
referring to fig. 1, an intelligent terminal user indoor and outdoor positioning method based on an encounter mechanism and swarm intelligence comprises the following steps:
1) acoustic ranging based on encounter mechanism: the distance measurement principle of acoustic distance measurement based on an encounter mechanism is shown in fig. 2, in an actual application scenario, when two intelligent terminal users are in a line-of-sight range, the intelligent terminals carried by the users call the speakers and microphones of the intelligent terminals to transmit and record Chirp acoustic signals through Chirp acoustic APP, the transmitted acoustic signals comprise two items of information, namely ID of the transmitting terminal and sounding time Te, the recorded acoustic signals imply receiving time Tr corresponding to each acoustic event, the time Tr is easily obtained by adopting a time domain generalized cross-correlation algorithm, for any node, the ID and sounding time Te of the transmitting terminal and the time Tr for receiving the ID acoustic 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 IDb, the sounding times are Tae and Tbe, the times for receiving the acoustic signals sent by the other party are Tbr and Tar respectively, because all terminals are in the same network and adopt consistent network synchronization time, the nodes a and B can directly calculate the one-way flight time ETOAb and ETOAa of the Chirp acoustic signal as shown in formula (1):
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d ═ eta + eta) · c/2, where c is the speed of sound, and the acoustic signals received by any target node, often the acoustic signals emitted by multiple nodes in the scene, are mixed with environmental noise, so the distance values calculated by the target node from the received signals are usually collected in an aggregate manner
Figure BDA0002772071750000061
Represents:
Figure BDA0002772071750000062
v denotes the total number of nodes in the scene, if known
Figure BDA0002772071750000063
Preferred apparent distance value in set, namely acoustic anchor point selection node based on group intelligenceIf the corresponding anchor point distance is obtained, the target node position P is obtained by the TDOA algorithm, how to obtain the target node position P from the TDOA algorithm
Figure BDA0002772071750000064
The optimal sight distance value is selected in an aggregation mode and is the key of the position of the target node;
2) group intelligence-based acoustic anchor selection: using firefly algorithm to accomplish from
Figure BDA0002772071750000069
The superior node is selected as the acoustic anchor point from the nodes corresponding to each set element to execute the P value estimation of the target node position based on the TDOA algorithm, wherein the position of each firefly represents
Figure BDA0002772071750000065
The node position corresponding to each distance value in the set represents a feasible solution of a problem to be solved, namely a sight distance value, the brightness of the firefly represents the fitness of the firefly position, namely the node position, the position of the firefly individual with higher brightness in a solution space is better, namely the probability of serving as a sight distance node is higher, each firefly adopts a roulette rule and moves towards all fireflies with higher brightness than the firefly, and the transition probability of the ith firefly moving to the jth firefly is shown in a formula (2):
Figure BDA0002772071750000066
in the formula (2), lj(q)、li(q) and lk(q) respectively represents the fluorescein values of the jth firefly, the ith firefly and the kth firefly in the q generation, and is jointly determined by the fluorescein value of the q-1 generation and the current position fitness:
li(q)＝(1-ρ)li(q-1)+γJ(xi(q)) (3)，
in the formula (3), rho is in [0,1 ]]Represents the volatilization factor of fluorescein, gamma is from [0,1 ]]Indicates the renewal rate of fluorescein, xi(q) represents the q-th generationThe ith firefly is located, because the firefly location determines the firefly fluorescein value, i.e., the firefly brightness, which directly reflects the firefly location fitness corresponding to the stadia distance value of the present example,
Figure BDA0002772071750000067
in the q-th generation of firefly, which is a v number of nodes in the set, the apparent distance corresponding to each node has the same reference as the firefly position, and is represented by X ═ X (X ═ X)1(q),…,xv(q)), the individuals with the highest brightness in the firefly population correspond to the individuals with better fitness, i.e., the individuals with better stadia distance, and the firefly location update formula (4) shows:
Figure BDA0002772071750000068
in the formula (4), α represents a step-size factor of disturbance, and the value range is [0,1 ]]And | represents the Euclidean norm operator, the fitness value of a new position where the firefly arrives after flying to all other individuals with higher brightness than the firefly is calculated by adopting an updated equation formula (4), if the new position is superior to the position before flying, the firefly flies to the new position, otherwise, the firefly stays in the original position, and when the maximum iteration number is reached, the searched optimal firefly position is used as a solution set
Figure BDA0002772071750000071
And outputting, otherwise returning to the formula (3), and continuing to perform another round of iterative updating, obviously,
Figure BDA0002772071750000072
is that
Figure BDA0002772071750000073
Is selected from the group consisting of (a) a subset of,
Figure BDA0002772071750000074
3) sound receiving point based on secondary clusteringPositioning: to pair
Figure BDA0002772071750000075
The distance values in the target node are sorted 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 for obtaining the position of the target node is that the relative position of the target node and the sight distance range neighbor node of the target node is kept unchanged, and the application assumption is that under an actual scene, the application is often difficult to ensure because: the movement of a node is random, and not only the target node position changes with time, but also its neighbor node positions change with time, as shown in FIG. 3, assuming that it is in a time period t { Δ t }1,…ΔtmWithin the period, the target node moves by n steps, wherein the value of n is obtained by reading of an accelerometer arranged in the smart phone and is considered as a known value, and the target node is located at each unit time delta tmIntra-location estimation
Figure BDA0002772071750000077
Obtained by 4 nodes which are adjacent to the optimal sight distance and provided by the step 1) and the step 2) and a three-point positioning model based on sound arrival time difference, namely TDOA algorithm, and finally, for each unit time delta tmIntra-location estimation
Figure BDA0002772071750000076
After secondary clustering is carried out based on a K mean (Kmeans) algorithm, the optimal position estimated value P of the target node at the current moment t can be obtainedt。
The Chirp sound APP in the step 1) is soft typewriting No. 2653065 named Chirp sound recording software, and Chirp sound signals transmitted by the Chirp sound recording software are located in the upper limit frequency range of human ear hearing, so that hearing interference is not caused to a terminal user.

Claims (2)

1. An intelligent terminal user indoor and outdoor positioning method based on an encounter mechanism and swarm intelligence is characterized by comprising the following steps:
1) acoustic ranging based on encounter mechanism: when two intelligent terminal users are in the sight distance range, the intelligent terminals carried by the two intelligent terminal users call the loudspeakers and the microphones of the intelligent terminals to transmit and record Chirp sound signals through the Chirp sound APP, the transmitted sound signal comprises two items of information of ID and sounding time Te of the transmitting terminal, the recorded sound signal implies receiving time Tr corresponding to each sound event, time Tr is obtained by adopting time domain generalized cross-correlation algorithm, for any node, extracting the ID and the sounding time Te of the transmitting terminal from the signal received by any node, and time Tr for receiving the ID sound signal, setting any two neighbor nodes A and node B, wherein the terminal IDs corresponding to the node A and the node B are IDa and IDb respectively, the sounding time is Tae and Tbe, the time for receiving the sound signal sent by the other side is Tbr and Tar respectively, and the nodes A and B can directly calculate the one-way flight time ETOAb and ETOAa of the Chirp sound signal as shown in formula (1):
ETOAb＝Tbr-Tbe,ETOAa＝Tar-Tae (1)，
the distance between nodes a and B is: d ═ c/2 (etaaa + etaob) · c/2, where c is the speed of sound, and the distance values that the target node calculates from the received signal are aggregated as a set
Figure FDA0002772071740000011
Represents:
Figure FDA0002772071740000012
v denotes the total number of nodes in the scene, knowing
Figure FDA0002772071740000013
The optimal apparent distance value in the set is the anchor point distance corresponding to the group-intelligence-based acoustic anchor point selection result, and then the target node position P is obtained by a time difference of arrival (TDOA) algorithm;
2) group intelligence-based acoustic anchor selection: using firefly algorithm to accomplish from
Figure FDA0002772071740000014
The superior node is selected as the acoustic anchor point from the nodes corresponding to each set element to execute the P value estimation of the target node position based on the TDOA algorithm, wherein the position of each firefly represents
Figure FDA0002772071740000015
The node position corresponding to each distance value in the set represents a feasible solution of the problem to be solved, namely the sight distance value, the brightness of the fireflies the fitness of the firefly position, namely the node position, each firefly adopts the roulette rule and moves towards all fireflies with higher brightness than the firefly, and the transition probability of the ith firefly moving to the jth firefly is shown in a formula (2):
Figure FDA0002772071740000016
in the formula (2), lj(q)、li(q) and lk(q) respectively represents the fluorescein values of the jth firefly, the ith firefly and the kth firefly in the q generation, and is jointly determined by the fluorescein value of the q-1 generation and the current position fitness:
li(q)＝(1-ρ)li(q-1)+γJ(xi(q)) (3)，
in the formula (3), rho is in [0,1 ]]Represents the volatilization factor of fluorescein, gamma is from [0,1 ]]Indicates the renewal rate of fluorescein, xi(q) represents the location of the ith firefly in the qth generation, which determines the firefly's fluorescein value, i.e., the brightness of the firefly, which in turn directly reflects the fitness of the firefly's location, which corresponds to the stadia distance value involved, and therefore,
Figure FDA0002772071740000027
in the q-th generation of firefly, which is a v number of nodes in the set, the apparent distance corresponding to each node has the same reference as the firefly position, and is represented by X ═ X (X ═ X)1(q),…,xv(q)), the individuals with the highest brightness in the firefly population correspond to the individuals with better fitness, i.e., the individuals with better stadia distance, and the firefly location update formula (4) shows:
Figure FDA0002772071740000021
in the formula (4), α represents a step-size factor of disturbance, and the value range is [0,1 ]]And | represents the Euclidean norm operator, the fitness value of a new position where the firefly arrives after flying to all other individuals with higher brightness than the firefly is calculated by adopting an updated equation formula (4), if the new position is superior to the position before flying, the firefly flies to the new position, otherwise, the firefly stays in the original position, and when the maximum iteration number is reached, the searched optimal firefly position is used as a solution set
Figure FDA0002772071740000022
Outputting, otherwise returning to the formula (3), continuing to perform another round of iterative updating,
Figure FDA0002772071740000023
is that
Figure FDA0002772071740000024
Is selected from the group consisting of (a) a subset of,
Figure FDA0002772071740000025
3) acoustic receive point localization based on quadratic clustering: to pair
Figure FDA0002772071740000026
The distance values in the target node are sorted from small to large, 4 minimum distance values are selected, 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 adjacent node in the sight distance range of the target node is kept unchanged, and the assumption is made that the time period t { delta t [ [ delta ] t ] is in a time period1,…ΔtmWithin the period, the target node moves by n steps, wherein the value of n is obtained by reading of an accelerometer arranged in the smart phone and is considered as a known value, and the target node is located at each unit time delta tmIntra-location estimation
Figure FDA0002772071740000029
Obtained by 4 nodes which are adjacent to the optimal sight distance and provided by the step 1) and the step 2) and a three-point positioning model based on sound arrival time difference, namely TDOA algorithm, and finally, for each unit time delta tmIntra-location estimation
Figure FDA0002772071740000028
After secondary clustering is carried out based on a K mean (Kmeans) algorithm, the optimal position estimated value P of the target node at the current moment t can be obtainedt。
2. The intelligent end user indoor and outdoor positioning method based on the encounter mechanism and the swarm intelligence as claimed in claim 1, wherein the Chirp APP in step 1) is soft typewriter 2653065 named Chirp sound recording software, and Chirp sound signals emitted by the Chirp sound recording software are located in the upper limit frequency band of human ear hearing, so that no hearing interference is caused to the end user.
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 true CN112462332A (en) 2021-03-09
CN112462332B 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 (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030228035A1 (en) * 2002-06-06 2003-12-11 Parunak H. Van Dyke Decentralized detection, localization, and tracking utilizing distributed sensors
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

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030228035A1 (en) * 2002-06-06 2003-12-11 Parunak H. Van Dyke Decentralized detection, localization, and tracking utilizing distributed sensors
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
林峰;张磊;李贵楠;王智;: "基于智能手机声信号的自标定室内定位系统", 计算机研究与发展, no. 12 *

Also Published As

Publication number Publication date
CN112462332B (en) 2023-07-18

Similar Documents

Publication Publication Date Title
Polese et al. Machine learning at the edge: A data-driven architecture with applications to 5G cellular networks
Abualsaud et al. A survey on mobile crowd-sensing and its applications in the IoT era
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
Bian et al. Using sound source localization in a home environment
CN107920355A (en) The wireless network that machine learning is coordinated
US10340996B1 (en) System and method for antenna array control and coverage mapping
WO2015062409A1 (en) Method and device for predicting position of mobile user
WO2014096861A2 (en) Methods and apparatus for forming image using, and finding positions of, plural pixel devices
Kouyoumdjieva et al. Survey of non-image-based approaches for counting people
CN108960785A (en) A kind of information cuing method and device
CN107959936A (en) Recognition methods, system, terminal and the computer-readable storage medium of 4G pseudo-base stations
US20210021962A1 (en) System and method for passive tracking of objects
CN107624249A (en) Method and system for the number in the aggregated data real-time counting crowd by communication network
Higuchi et al. Context-supported local crowd mapping via collaborative sensing with mobile phones
Higuchi et al. Mobile devices as an infrastructure: A survey of opportunistic sensing technology
CN112462332B (en) Indoor and outdoor positioning method for intelligent terminal users based on meeting mechanism and group intelligence
Guillen-Perez et al. Pedestrian characterisation in urban environments combining WiFi and AI
US11570613B2 (en) Methods and apparatus for determining and using enclosure state information
Park et al. Improving acoustic localization accuracy by applying interaural level difference and support vector machine for AoA outlier removal
Mantoro et al. Mobile user location determination using extreme learning machine
Ng et al. Footfall count estimation techniques using mobile data
Wong et al. Relative accuracy based location estimation in wireless ad hoc sensor networks
Koshiba et al. Group detection based on user-to-user distance in everyday life for office lunch group recommendation
WO2023032076A1 (en) Determination system, determination method, and computer program
CN115623531B (en) Hidden monitoring equipment discovering and positioning method using wireless radio frequency signal

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