CN116233741A - High-rise building fire rescue information system based on bluetooth indoor positioning - Google Patents

High-rise building fire rescue information system based on bluetooth indoor positioning Download PDF

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CN116233741A
CN116233741A CN202211618399.0A CN202211618399A CN116233741A CN 116233741 A CN116233741 A CN 116233741A CN 202211618399 A CN202211618399 A CN 202211618399A CN 116233741 A CN116233741 A CN 116233741A
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bluetooth
intelligent terminal
point
beacon
matrix
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宋宇廷
朱志伟
赵新跃
戴胜
高文昀
闵学智
刘明
陈颖
李诗芸
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Nanjing LES Information Technology Co. Ltd
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    • 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
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • 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
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
    • 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
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Abstract

The invention discloses a high-rise building fire rescue information system based on Bluetooth indoor positioning, which comprises: the Bluetooth beacon is used for sending Bluetooth signals to the intelligent terminals in the communication range in the building; the intelligent terminal is used for collecting real-time position information of rescue workers, receiving Bluetooth signals sent by the Bluetooth beacons and sending the received Bluetooth signals to the background server; the background server is used for analyzing the Bluetooth signal through a signal attenuation model to obtain a signal intensity indication value of the intelligent terminal, analyzing the signal intensity indication value through a filtering algorithm to obtain a stable signal intensity indication value, calculating to obtain the distance between the Bluetooth beacon and the intelligent terminal, further calculating to obtain the position of the intelligent terminal, and transmitting the obtained position information to the command terminal; and the command terminal is used for receiving the position information of the intelligent terminal sent by the background server. The invention can accurately acquire the position of the firefighter, improve rescue efficiency and reduce the probability of casualties.

Description

High-rise building fire rescue information system based on bluetooth indoor positioning
Technical Field
The invention belongs to the technical field of Bluetooth positioning, and particularly relates to a high-rise building fire rescue information system based on Bluetooth indoor positioning.
Background
With the continuous improvement of the urban level in China, high-rise buildings are increasingly increased. In order to meet the development demands of society, a modern rescue command system for high-rise complex building fire disaster is created, and the modern rescue command system is an important issue faced by the current fire control community.
Aiming at the complexity and the diversity of disaster environments, a specific rescue command scheme needs to be formulated by depending on real-time feedback information of a fire scene, but the traditional technology cannot finish the work of capturing the real-time position, monitoring the behavior gesture and the like of fire fighters in a modern high-rise fire rescue environment without electricity, high temperature, smoke and multiple shielding walls. When an emergency such as a fire occurs, a necessary condition for rescue is to quickly determine the position of firefighters, and particularly when a building is changed due to an emergency layout, it is difficult to quickly locate the positions of the firefighters empirically.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a high-rise building fire-fighting rescue information system based on Bluetooth indoor positioning, so as to solve the problems that the prior art is difficult to capture the real-time position of firefighters in combat, monitor the behavior gesture and the like.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention relates to a high-rise building fire rescue information system based on Bluetooth indoor positioning, which comprises the following components:
the Bluetooth beacons are deployed in the building, the ground clearance is 2.5-3 meters, and the beacon interval is controlled to be 6-8 meters; the intelligent terminal is used for sending Bluetooth signals to the intelligent terminal in the communication range in the building;
the intelligent terminal is worn on the body of the rescue personnel and used for collecting real-time position information of the rescue personnel, receiving Bluetooth signals sent by the Bluetooth beacons and sending the received Bluetooth signals to the background server;
the background server is used for analyzing the Bluetooth signal through a signal attenuation model to obtain a signal strength indication value (RSSI) of the intelligent terminal, analyzing the signal strength indication value through a filtering algorithm to obtain a stable signal strength indication value, calculating to obtain the distance between the Bluetooth beacon and the intelligent terminal, further calculating to obtain the position of the intelligent terminal, and transmitting the obtained position information to the command terminal;
and the command terminal is used for receiving the position information of the intelligent terminal sent by the background server.
Further, the step of analyzing the stable signal strength indication value by the background server through a filtering algorithm specifically includes:
(11) The background server receives Bluetooth signals sent by the intelligent terminal through a 4G/5G network, predicts and updates the current state by utilizing an improved Kalman filtering algorithm, reduces system errors caused by noise superposition, and improves system stability;
(12) Carrying out regression fitting by adopting a control variable method under different attenuation conditions of signal intensity in different environments to obtain target parameters A and n in a signal attenuation model;
(13) And determining the distance d between the beacon base station and the intelligent terminal through a signal attenuation model.
Further, the improved kalman filtering algorithm in the step (11) specifically includes:
assume that the noise state and metrology model of the system is expressed as:
X k ＝A k,k-1 X k-1 +B k,k-1 u k +G k,k-1 w k-1 (1)
Z k ＝H k X k +v k (2)
in the formula ,Xk Is an n 1 vector; x is X k-1 Is a (n-1) 1 vector; z is Z k Is m 1 vector; a is that k,k-1 For the state transition matrix of the system, A k,k-1 Is an n-by-n vector; b (B) k,k-1 A control matrix n; g k,k-1 A vector n x n; h k An m x n matrix is observed for the system; w (w) k Is the state noise of the system at the moment k-1, v k The observation sound of the system at the moment k; wherein E (X) 0 )＝mx 0 ，Var(X 0 )＝p 0 ，P 0 The initial value of the covariance of the posterior error; then H k and p0 Direct influence of the observation vector X 0 For state vector X k Thus by adjusting H k and p0 The correction function of the new and old measurement vectors to the estimated value is realized by the values of (1), and the specific settings are as follows:
Figure BDA0004001019490000021
Figure BDA0004001019490000022
Figure BDA0004001019490000023
in the formula ,
Figure BDA0004001019490000024
the initial value is the initial value of the posterior error covariance matrix; s is an evanescent factor term; n is the width of the filtering window; />
Figure BDA0004001019490000025
A posterior error covariance matrix at the moment k; />
Figure BDA0004001019490000026
A noise covariance matrix at the moment k-1;
according to the Kalman filtering method, the equation for obtaining the fading memory filter is shown in formulas (6) - (9):
Figure BDA0004001019490000027
Figure BDA0004001019490000028
Figure BDA0004001019490000029
Figure BDA00040010194900000210
in the formula ,
Figure BDA00040010194900000211
is a Kalman gain coefficient; q (Q) k-1 A noise covariance matrix at the moment k-1;
in the calculation
Figure BDA00040010194900000212
The fading factors are added in the process; s is the evanescent factor term, due to s>1, therefore
Figure BDA00040010194900000213
Always greater than P k,k-1 Thus->
Figure BDA00040010194900000214
wherein Kk The new measurement value is weighted higher than the Kalman filtering method in the filtering gain matrix, that is, in the filtering process; thus, formula (9) is further expressed as:
Figure BDA0004001019490000031
wherein ,
Figure BDA0004001019490000032
the weight of the obtained old measured value is reduced, and the influence of the old measured value on the filtering process is reduced; when the filter diverges, the gain time of the filter approaches zero, and the estimation error is larger than the theoretical estimation error; therefore, the divergence determination condition is selected as:
Figure BDA0004001019490000033
when λ=1, the judgment condition is expressed as:
Figure BDA0004001019490000034
wherein lambda is the white noise figure;
Figure BDA0004001019490000035
is a white noise identity matrix; />
Figure BDA0004001019490000036
The covariance matrix is white noise:
Figure BDA0004001019490000037
in the formula ,Sk A fading factor matrix at the moment k; r is R k The measured noise covariance at time k;
when the filtering process is stable, the mean covariance of the news at the first N times can be used as the covariance of the news at the current time, namely:
Figure BDA0004001019490000038
thus, the equation for the evanescent adaptive Kalman filter is:
Figure BDA0004001019490000039
Figure BDA00040010194900000310
Figure BDA00040010194900000311
Figure BDA00040010194900000312
Figure BDA00040010194900000313
wherein m is the dimension of the measurement sequence;
Figure BDA00040010194900000314
to update the predicted value; />
Figure BDA00040010194900000315
Updating a posterior error covariance matrix for the k moment; is->
Figure BDA00040010194900000316
To update the prediction matrix; />
Figure BDA00040010194900000317
To update the gain matrix.
Further, the method for calculating the position of the intelligent terminal by the background server comprises the following steps: single point positioning, two point positioning and triangular positioning;
(21) Single point positioning: the method is used for detecting the existence, and by setting the effective identification distance of the Bluetooth beacon (by setting a signal intensity threshold), when the intelligent terminal enters the coverage range of the beacon, the signal intensity of the Bluetooth beacon is sensed to reach the threshold, so that whether the positioning is in a certain area or not is judged;
(22) Two-point positioning: the method is used for a one-dimensional scene, marks Bluetooth beacons, and lays one Bluetooth beacon at intervals in a linear area (such as a corridor) to ensure that signals of at least two Bluetooth beacons can be received at any position in a special area; obtaining distances between the intelligent terminal and different Bluetooth beacons through a ranging model, so as to obtain the position of the intelligent terminal;
(23) Triangular positioning: the method is used for a general open space scene, the intelligent terminal receives Bluetooth signals sent by surrounding Bluetooth beacons, the distance between the Bluetooth beacons and the intelligent terminal is obtained according to a ranging model of signal intensity, and when the intelligent terminal receives the Bluetooth signals of more than three different Bluetooth beacons and the coordinates of the Bluetooth beacons are known, the intelligent terminal can be positioned.
Further, the step (21) specifically includes:
(211) Screening out the set beacons for single-point positioning (presence detection) according to the MAC address, indirectly setting a detection range by setting an RSSI threshold value (according to a signal attenuation model, the distance between the mobile phone and the beacons measured by the signal strength is the detection radius);
(212) When the received signal strength is within the RSSI threshold, the intelligent terminal is judged to be within the Bluetooth beacon range (the specific position cannot be determined and only the range cannot be determined), and the output coordinate point is the coordinate (x 1 ,y 1 )；
Bluetooth beacon point coordinates A (x 1 ,y 1 ) The distance between the intelligent terminal and the plane where the Bluetooth beacon is located is h, the distance between the Bluetooth beacon and the intelligent terminal is d as measured by a signal attenuation model, and the radius r of the detection range is as follows:
Figure BDA0004001019490000041
further, the step (22) specifically includes:
(221) Projecting the intelligent terminal G onto the straight line where the Bluetooth beacons A and B are located, wherein the projection point is G', and the vertical distance h between the intelligent terminal and the straight line determined by the Bluetooth beacons A and B is the same
Figure BDA0004001019490000042
Figure BDA0004001019490000043
(222)A(x 1 ,y 1 ),B(x 2 ,y 2 )，AF＝d 1 ,BE＝d 2 Wherein the target point G is the midpoint of EF; the F point coordinates are
Figure BDA0004001019490000044
Similarly, the E point coordinate may be calculated, and the G point coordinate may be calculated.
Further, the step (23) specifically includes:
(231) The point A is a Bluetooth beacon point, the point G is an intelligent terminal, the point G is not in the plane of the Bluetooth beacon, the AG is the real distance of ranging, the target is to acquire the distance of AG', and the point G is projected into the plane of the beacon, wherein the formula is as follows:
Figure BDA0004001019490000045
(232) Three-point positioning method is used, three circles are solved in a pairwise simultaneous manner, and the coordinate of the intersection point M, E, F is obtained as M (x) 4 ,y 4 )、E(x 5 ,y 5 )、F(x 6 ,y 6 ) The coordinates (x, y) of the target point G are:
Figure BDA0004001019490000051
the invention has the beneficial effects that:
according to the invention, a positioning algorithm suitable for different scenes is established, so that on one hand, the calculated amount can be reduced, on the other hand, the coupling between subsystems can be reduced, and the stability of the whole system is improved. For example, a single-point positioning technology is adopted in a room to perform presence detection, a two-point positioning technology is adopted in a linear area of a corridor, and a three-point positioning technology is adopted in a large-bay area with a lower floor, so that positioning accuracy is improved. The influence of multipath effect on indoor signal transmission is considered in the system, so that the accuracy of the ranging model is improved to a certain extent; secondly, the interference of various distributed noises is considered, and improved Kalman filtering is adopted, so that the stability of signals is improved; through the high-rise building fire rescue information system based on bluetooth indoor location, can be more accurate obtain firefighter's position, improve rescue efficiency, reduce the probability of casualties.
Drawings
Fig. 1 is a schematic block diagram of the system of the present invention.
Fig. 2 is a three-point positioning schematic diagram.
Detailed Description
The invention will be further described with reference to examples and drawings, to which reference is made, but which are not intended to limit the scope of the invention.
Referring to fig. 1, the invention provides a high-rise building fire rescue information system based on bluetooth indoor positioning, which comprises:
the Bluetooth beacons are deployed in the building, the ground clearance is 2.5-3 meters, and the beacon interval is controlled to be 6-8 meters; the intelligent terminal is used for sending Bluetooth signals to the intelligent terminal in the communication range in the building;
the intelligent terminal is worn on the body of the rescue personnel and used for collecting real-time position information of the rescue personnel, receiving Bluetooth signals sent by the Bluetooth beacons and sending the received Bluetooth signals to the background server;
the background server is used for analyzing the Bluetooth signal through a signal attenuation model to obtain a signal strength indication value (RSSI) of the intelligent terminal, analyzing the signal strength indication value through a filtering algorithm to obtain a stable signal strength indication value, calculating to obtain the distance between the Bluetooth beacon and the intelligent terminal, further calculating to obtain the position of the intelligent terminal, and transmitting the obtained position information to the command terminal;
and the command terminal is used for receiving the position information of the intelligent terminal sent by the background server.
The background server analyzes and obtains a stable signal strength indication value through a filtering algorithm specifically comprises the following steps:
(11) The background server receives Bluetooth signals sent by the intelligent terminal through a 4G/5G network, predicts and updates the current state by utilizing an improved Kalman filtering algorithm, reduces system errors caused by noise superposition, and improves system stability;
(12) Carrying out regression fitting by adopting a control variable method under different attenuation conditions of signal intensity in different environments to obtain target parameters A and n in a signal attenuation model;
(13) And determining the distance d between the beacon base station and the intelligent terminal through a signal attenuation model.
Preferably, the improved kalman filtering algorithm in the step (11) specifically includes:
the traditional Kalman filtering calculation process may diverge due to inaccurate model, so the correction effect of the new and old measurement vectors on the filtering estimation value is adjusted by introducing the fading memory factor;
assume that the noise state and metrology model of the system is expressed as:
X k ＝A k,k-1 X k-1 +B k,k-1 u k +G k,k-1 w k-1 (1)
Z k ＝H k X k +v k (2)
in the formula ,Xk Is an n 1 vector; x is X k-1 Is a (n-1) 1 vector; z is Z k Is m 1 vector; a is that k,k-1 For the state transition matrix of the system, A k,k-1 Is an n-by-n vector; b (B) k,k-1 A control matrix n; g k,k-1 A vector n x n; h k An m x n matrix is observed for the system; w (w) k Is the state noise of the system at the moment k-1, v k The observation sound of the system at the moment k; wherein E (X) 0 )＝mx 0 ，Var(X 0 )＝p 0 ，P 0 The initial value of the covariance of the posterior error; then H k and p0 Direct influence of the observation vector X 0 For state vector X k Thus by adjusting H k and p0 The correction function of the new and old measurement vectors to the estimated value is realized by the values of (1), and the specific settings are as follows:
Figure BDA0004001019490000061
Figure BDA0004001019490000062
Figure BDA0004001019490000063
in the formula ,
Figure BDA0004001019490000064
the initial value is the initial value of the posterior error covariance matrix; s is an evanescent factor term; n is the width of the filtering window; />
Figure BDA0004001019490000065
A posterior error covariance matrix at the moment k; />
Figure BDA0004001019490000066
A noise covariance matrix at the moment k-1;
according to the Kalman filtering method, the equation for obtaining the fading memory filter is shown in formulas (6) - (9):
Figure BDA0004001019490000067
/>
Figure BDA0004001019490000068
Figure BDA0004001019490000069
Figure BDA00040010194900000610
in the formula ,
Figure BDA0004001019490000071
is a Kalman gain coefficient; q (Q) k-1 A noise covariance matrix at the moment k-1;
in the calculation
Figure BDA0004001019490000072
The fading factors are added in the process; s is the evanescent factor term, due to s>1, therefore
Figure BDA0004001019490000073
Always greater than P k,k-1 Thus->
Figure BDA0004001019490000074
wherein Kk The new measurement value is weighted higher than the Kalman filtering method in the filtering gain matrix, that is, in the filtering process; thus, formula (9) is further expressed as:
Figure BDA0004001019490000075
wherein ,Kk N >K k ，X k N ,k-1 The weight of the obtained old measured value is reduced, and the influence of the old measured value on the filtering process is reduced; when the filter diverges, the gain time of the filter approaches zero, and the estimation error is larger than the theoretical estimation error; therefore, the divergence determination condition is selected as:
Figure BDA0004001019490000076
when λ=1, the judgment condition is expressed as:
Figure BDA0004001019490000077
wherein lambda is the white noise figure;
Figure BDA0004001019490000078
is a white noise identity matrix; />
Figure BDA0004001019490000079
The covariance matrix is white noise:
Figure BDA00040010194900000710
in the formula ,Sk A fading factor matrix at the moment k; r is R k The measured noise covariance at time k;
when the filtering process is stable, the mean covariance of the news at the first N times can be used as the covariance of the news at the current time, namely:
Figure BDA00040010194900000711
thus, the equation for the evanescent adaptive Kalman filter is:
Figure BDA00040010194900000712
Figure BDA00040010194900000713
Figure BDA00040010194900000714
Figure BDA00040010194900000715
/>
Figure BDA00040010194900000716
wherein m is the dimension of the measurement sequence;
Figure BDA0004001019490000081
to update the predicted value; />
Figure BDA0004001019490000082
Updating a posterior error covariance matrix for the k moment; is->
Figure BDA0004001019490000083
To update the prediction matrix; />
Figure BDA0004001019490000084
To update the gain matrix.
Preferably, the method for calculating the position of the intelligent terminal by the background server includes: single point positioning, two point positioning and triangular positioning;
(21) Single point positioning: the method is used for detecting the existence, and by setting the effective identification distance of the Bluetooth beacon (by setting a signal intensity threshold), when the intelligent terminal enters the coverage range of the beacon, the signal intensity of the Bluetooth beacon is sensed to reach the threshold, so that whether the positioning is in a certain area or not is judged;
(22) Two-point positioning: the method is used for a one-dimensional scene, marks Bluetooth beacons, and lays one Bluetooth beacon at intervals in a linear area (such as a corridor) to ensure that signals of at least two Bluetooth beacons can be received at any position in a special area; obtaining distances between the intelligent terminal and different Bluetooth beacons through a ranging model, so as to obtain the position of the intelligent terminal;
(23) Triangular positioning: the method is used for a general open space scene, the intelligent terminal receives Bluetooth signals sent by surrounding Bluetooth beacons, the distance between the Bluetooth beacons and the intelligent terminal is obtained according to a ranging model of signal intensity, and when the intelligent terminal receives the Bluetooth signals of more than three different Bluetooth beacons and the coordinates of the Bluetooth beacons are known, the intelligent terminal can be positioned.
Preferably, the step (21) specifically includes:
(211) Screening out the set beacons for single-point positioning (presence detection) according to the MAC address, indirectly setting a detection range by setting an RSSI threshold value (according to a signal attenuation model, the distance between the mobile phone and the beacons measured by the signal strength is the detection radius);
(212) When the received signal strength is within the RSSI threshold, the intelligent terminal is judged to be within the Bluetooth beacon range (the specific position cannot be determined and only the range cannot be determined), and the output coordinate point is the coordinate (x 1 ,y 1 )；
Bluetooth beacon point coordinates A (x 1 ,y 1 ) The distance between the intelligent terminal and the plane where the Bluetooth beacon is located is h, the distance between the Bluetooth beacon and the intelligent terminal is d as measured by a signal attenuation model, and the radius r of the detection range is as follows:
Figure BDA0004001019490000085
preferably, the step (22) specifically includes:
(221) Projecting the intelligent terminal G onto the straight line where the Bluetooth beacons A and B are located, wherein the projection point is G', and the vertical distance h between the intelligent terminal and the straight line determined by the Bluetooth beacons A and B is the same
Figure BDA0004001019490000086
Figure BDA0004001019490000087
(222)A(x 1 ,y 1 ),B(x 2 ,y 2 )，AF＝d 1 ,BE＝d 2 Wherein the target point G is the midpoint of EF; the F point coordinates are
Figure BDA0004001019490000088
Similarly, the E point coordinate may be calculated, and the G point coordinate may be calculated.
Preferably, referring to fig. 2, the step (23) specifically includes:
(231) The point A is a Bluetooth beacon point, the point G is an intelligent terminal, the point G is not in the plane of the Bluetooth beacon, the AG is the real distance of ranging, the target is to acquire the distance of AG', and the point G is projected into the plane of the beacon, wherein the formula is as follows:
Figure BDA0004001019490000091
/>
(232) Three-point positioning method is used, three circles are solved in a pairwise simultaneous manner, and the coordinate of the intersection point M, E, F is obtained as M (x) 4 ,y 4 )、E(x 5 ,y 5 )、F(x 6 ,y 6 ) The coordinates (x, y) of the target point G are:
Figure BDA0004001019490000092
Figure BDA0004001019490000093
the present invention has been described in terms of the preferred embodiments thereof, and it should be understood by those skilled in the art that various modifications can be made without departing from the principles of the invention, and such modifications should also be considered as being within the scope of the invention.

Claims (7)

1. High-rise building fire rescue information system based on bluetooth indoor location, its characterized in that includes:
the Bluetooth beacon is deployed in the building and is used for sending Bluetooth signals to the intelligent terminals in the communication range in the building;
the intelligent terminal is worn on the body of the rescue personnel and used for collecting real-time position information of the rescue personnel, receiving Bluetooth signals sent by the Bluetooth beacons and sending the received Bluetooth signals to the background server;
the background server is used for analyzing the Bluetooth signal through a signal attenuation model to obtain a signal intensity indication value of the intelligent terminal, analyzing the signal intensity indication value through a filtering algorithm to obtain a stable signal intensity indication value, calculating to obtain the distance between the Bluetooth beacon and the intelligent terminal, further calculating to obtain the position of the intelligent terminal, and transmitting the obtained position information to the command terminal;
and the command terminal is used for receiving the position information of the intelligent terminal sent by the background server.
2. The bluetooth indoor positioning-based high-rise building fire rescue information system according to claim 1, wherein the background server analyzing the stable signal strength indication value through a filtering algorithm specifically comprises:
(11) The background server receives Bluetooth signals sent by the intelligent terminal through a 4G/5G network, predicts and updates the current state by utilizing an improved Kalman filtering algorithm, and reduces system errors caused by noise superposition;
(12) Carrying out regression fitting by adopting a control variable method under different attenuation conditions of signal intensity in different environments to obtain target parameters A and n in a signal attenuation model;
(13) And determining the distance d between the beacon base station and the intelligent terminal through a signal attenuation model.
3. The bluetooth indoor positioning-based high-rise building fire rescue information system according to claim 2, wherein the improved kalman filtering algorithm in step (11) specifically comprises:
assume that the noise state and metrology model of the system are expressed as:
X k ＝A k,k-1 X k-1 +B k,k-1 u k +G k,k-1 w k-1 (1)
Z k ＝H k X k +v k (2)
in the formula ,Xk Is an n 1 vector; x is X k-1 Is a (n-1) 1 vector; z is Z k Is m 1 vector; a is that k,k-1 For the state transition matrix of the system, A k,k-1 Is an n-by-n vector; b (B) k,k-1 A control matrix n; g k,k-1 A vector n x n; h k An m x n matrix is observed for the system; w (w) k Is the state noise of the system at the moment k-1, v k The observation sound of the system at the moment k; wherein E (X) 0 )＝mx 0 ，Var(X 0 )＝p 0 ，P 0 The initial value of the covariance of the posterior error; then H k and p0 Direct influence of the observation vector X 0 For state vector X k Thus by adjusting H k and p0 The correction function of the new and old measurement vectors to the estimated value is realized by the values of (1), and the specific settings are as follows:
Figure FDA0004001019480000011
Figure FDA0004001019480000012
Figure FDA0004001019480000013
in the formula ,
Figure FDA0004001019480000021
the initial value is the initial value of the posterior error covariance matrix; s is an evanescent factor term; n is the width of the filtering window; />
Figure FDA0004001019480000022
Posterior error at time kA difference covariance matrix; />
Figure FDA0004001019480000023
A noise covariance matrix at the moment k-1;
according to the Kalman filtering method, the equation for obtaining the fading memory filter is shown in formulas (6) - (9):
Figure FDA0004001019480000024
Figure FDA0004001019480000025
Figure FDA0004001019480000026
Figure FDA0004001019480000027
/>
in the formula ,
Figure FDA0004001019480000028
is a Kalman gain coefficient; q (Q) k-1 A noise covariance matrix at the moment k-1;
in the calculation
Figure FDA0004001019480000029
The fading factors are added in the process; s is the evanescent factor term, due to s>1, therefore->
Figure FDA00040010194800000210
Always greater than P k,k-1 Thus->
Figure FDA00040010194800000211
wherein Kk To increase the filteringThe benefit matrix, that is, the weight of the new measurement value is higher than that of the Kalman filtering method in the filtering process; thus, formula (9) is further expressed as:
Figure FDA00040010194800000212
wherein ,
Figure FDA00040010194800000219
the weight of the obtained old measured value is reduced, and the influence of the old measured value on the filtering process is reduced; when the filter diverges, the gain time of the filter approaches zero, and the estimation error is larger than the theoretical estimation error; therefore, the divergence determination condition is selected as:
Figure FDA00040010194800000213
when λ=1, the judgment condition is expressed as:
Figure FDA00040010194800000214
wherein lambda is the white noise figure;
Figure FDA00040010194800000215
is a white noise identity matrix; />
Figure FDA00040010194800000216
The covariance matrix is white noise:
Figure FDA00040010194800000217
in the formula ,Sk A fading factor matrix at the moment k; r is R k The measured noise covariance at time k;
when the filtering process is stable, the mean covariance of the news at the first N times can be used as the covariance of the news at the current time, namely:
Figure FDA00040010194800000218
thus, the equation for the evanescent adaptive Kalman filter is:
Figure FDA0004001019480000031
Figure FDA0004001019480000032
Figure FDA0004001019480000033
Figure FDA0004001019480000034
Figure FDA0004001019480000035
wherein m is the dimension of the measurement sequence;
Figure FDA0004001019480000036
to update the predicted value; />
Figure FDA0004001019480000037
Updating a posterior error covariance matrix for the k moment; is that
Figure FDA0004001019480000038
To update the prediction matrix; />
Figure FDA0004001019480000039
To update the gain matrix. />
4. The high-rise building fire rescue information system based on Bluetooth indoor positioning according to claim 1, wherein the method adopted by the background server for calculating the position of the intelligent terminal comprises the following steps: single point positioning, two point positioning and triangular positioning;
(21) Single point positioning: the method is used for detecting existence, and by setting the effective identification distance of the Bluetooth beacon, when the intelligent terminal enters the coverage range of the beacon, the signal strength of the Bluetooth beacon is sensed to reach a threshold value, so that whether the intelligent terminal is positioned in a certain area or not is judged;
(22) Two-point positioning: the method is used for marking the Bluetooth beacons in a one-dimensional scene, and the Bluetooth beacons are distributed at intervals in a linear area, so that signals of at least two Bluetooth beacons can be received at any position in a special area; obtaining distances between the intelligent terminal and different Bluetooth beacons through a ranging model, so as to obtain the position of the intelligent terminal;
(23) Triangular positioning: the method is used for a general open space scene, the intelligent terminal receives Bluetooth signals sent by surrounding Bluetooth beacons, the distance between the Bluetooth beacons and the intelligent terminal is obtained according to a ranging model of signal intensity, and when the intelligent terminal receives the Bluetooth signals of more than three different Bluetooth beacons and the coordinates of the Bluetooth beacons are known, the intelligent terminal can be positioned.
5. The bluetooth indoor positioning-based high-rise building fire rescue information system according to claim 4, wherein said step (21) specifically comprises:
(211) The set beacons for single point positioning are screened out according to the MAC address, and the detection range is indirectly set by setting the RSSI threshold;
(212) When the received signal strength is within the RSSI threshold range, the intelligent terminal is judged to be within the Bluetooth beacon range, and the coordinate point output at the moment is the coordinate (x 1 ,y 1 )；
Bluetooth beacon point coordinates A (x 1 ,y 1 ) The distance between the intelligent terminal and the plane where the Bluetooth beacon is located is h, the distance between the Bluetooth beacon and the intelligent terminal is d as measured by a signal attenuation model, and the radius r of the detection range is as follows:
Figure FDA00040010194800000310
6. the bluetooth indoor positioning-based high-rise building fire rescue information system according to claim 5, wherein said step (22) specifically comprises:
(221) Projecting the intelligent terminal G onto the straight line where the Bluetooth beacons A and B are located, wherein the projection point is G', and the vertical distance h between the intelligent terminal and the straight line determined by the Bluetooth beacons A and B is the same
Figure FDA0004001019480000041
Figure FDA0004001019480000042
(222)A(x 1 ,y 1 ),B(x 2 ,y 2 )，AF＝d 1 ,BE＝d 2 Wherein the target point G is the midpoint of EF; the F point coordinates are
Figure FDA0004001019480000043
Similarly, the E point coordinate may be calculated, and the G point coordinate may be calculated.
7. The bluetooth indoor positioning-based high-rise building fire rescue information system according to claim 6, wherein said step (23) specifically comprises:
(231) The point A is a Bluetooth beacon point, the point G is an intelligent terminal, the point G is not in the plane of the Bluetooth beacon, the AG is the real distance of ranging, the target is to acquire the distance of AG', and the point G is projected into the plane of the beacon, wherein the formula is as follows:
Figure FDA0004001019480000044
(232) Three-point positioning method is used, three circles are solved in a pairwise simultaneous manner, and the coordinate of the intersection point M, E, F is obtained as M (x) 4 ,y 4 )、E(x 5 ,y 5 )、F(x 6 ,y 6 ) The coordinates (x, y) of the target point G are:
Figure FDA0004001019480000045
/>
Figure FDA0004001019480000046
/>
CN202211618399.0A 2022-12-15 2022-12-15 High-rise building fire rescue information system based on bluetooth indoor positioning Pending CN116233741A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116819591A (en) * 2023-06-30 2023-09-29 常州市场景信息科技有限公司 Positioning method and system based on RTK and Bluetooth

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116819591A (en) * 2023-06-30 2023-09-29 常州市场景信息科技有限公司 Positioning method and system based on RTK and Bluetooth
CN116819591B (en) * 2023-06-30 2024-01-19 常州市场景信息科技有限公司 Positioning method and system based on RTK and Bluetooth

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