CN116471661A - Method for positioning opportunistic signal analysis method of underground pipe gallery based on ray tracking - Google Patents
Method for positioning opportunistic signal analysis method of underground pipe gallery based on ray tracking Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
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- 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
- G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
- G01S5/0252—Radio frequency fingerprinting
- G01S5/02521—Radio frequency fingerprinting using a radio-map
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
- H04B17/318—Received signal strength
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
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- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The utility model provides a method for analyzing and positioning a signal of opportunity of an underground pipe gallery based on ray tracing, which comprises an underground pipe gallery map module, a signal of opportunity based on ray tracing module and an improved algorithm positioning output module, wherein the underground pipe gallery map module comprises a pipe network topology map, a region to be determined gridding map and a signal intensity distribution map; the opportunistic signals comprise Wi-Fi wireless signals, bluetooth wireless signals, ultra-wideband wireless signals and statistical transmission models thereof; the wireless signal module based on ray tracking is used for generating wireless signal information in a simulation mode; finally, map constraint, PDR and opportunistic signal weight are fused for positioning analysis. The invention integrates the existing various radio frequency positioning information, realizes the capabilities of model simulation analysis and algorithm fusion processing, and lays a foundation for the follow-up actual verification and test of various positioning technologies.
Description
Technical Field
The invention relates to ray tracing simulation and algorithm fusion of an underground pipe gallery opportunistic signal, in particular to a positioning method of an underground pipe gallery opportunistic signal analysis method based on ray tracing.
Background
At present, the global navigation satellite system (Global Navigation Satellite System, GNSS) has become the most commonly used navigation technology by virtue of its high accuracy and high stability, and has irreplaceable advantages. However, because of limitations of GNSS signals themselves, such as low power of signals reaching the earth's surface, susceptibility to interference and shielding during transmission, etc., navigation by GNSS alone often presents a certain risk.
With the rapid development of the underground comprehensive pipe rack, the environment information of the underground pipe rack of the foreign large-scale city is generally monitored by adopting a wired/wireless internet of things mode, the environment information in the pipe rack is fed back to an operation and maintenance management platform for analysis and treatment, and corresponding maintenance strategies are arranged according to diagnosis conclusions, so that the cost is high, the effect is not obvious, and prevention equipment is difficult to effectively play a role.
The current wireless channel modeling method can be divided into statistical modeling and deterministic modeling methods. In the statistical modeling method, the wireless channel is regarded as a random process, and the wireless channel can be simulated into different random distributions by counting measured data, and the fading characteristics of the channel are described by using a random process model. Because the randomization thought is adopted in the generation of the channel parameters, the generation is simpler, and the statistical modeling method can not accurately reflect the channel characteristics in an actual scene. The finite difference time domain method (FDTD) in deterministic modeling needs to consider the size of the propagation environment and the dielectric material, and in practical situations, the propagation environment of the signal is generally a complex environment with various obstacles, and when the FDTD is used for calculation, a lot of time is consumed. Therefore, the simulation of Radio Frequency (RF) opportunistic signals (signal of opportunity, SOP) is difficult to achieve by adopting a traditional mathematical model or an FDTD method, and reasonable simulation of indoor positioning signals is more beneficial to early scheme design, infrastructure deployment, algorithm method improvement verification, more realistic test evaluation and the like of indoor positioning technology.
The prior art is as follows:
application number: 202111494690.7, name: the utility model provides an auxiliary operation and maintenance system of utility tunnel based on remove end and method, provides an auxiliary operation and maintenance system of utility tunnel based on remove end, including utility tunnel equipment, utility tunnel server and remove end. The utility model also provides an auxiliary operation and maintenance method of the underground utility tunnel based on the mobile terminal, which comprises the following steps: s1, carrying out video monitoring in the pipe gallery by adopting underground comprehensive pipe gallery equipment, S2, acquiring monitoring data of the underground comprehensive pipe gallery equipment in real time by an underground comprehensive pipe gallery server through a data acquisition module of the underground comprehensive pipe gallery equipment, S3, and after data acquisition, pushing data to a mobile terminal by the underground comprehensive pipe gallery server. The invention can provide accurate positioning for the equipment by utilizing the portable characteristic of the mobile terminal and the positioning function, and does not need to spend a great deal of time to position the equipment when the equipment fails, thereby facilitating the equipment maintainer to carry out subsequent maintenance work and greatly improving the equipment maintenance efficiency.
The application adopts the underground comprehensive pipe gallery equipment to carry out video monitoring on the inside of the pipe gallery, the mobile terminal is accessed into an underground comprehensive pipe gallery local area network through WIFI or 4G/5G mobile data, and is communicated with an underground comprehensive pipe gallery server through a network communication module; this patent establishes the fingerprint storehouse through gathering signal of opportunity rssi and fixes a position.
Application number: 201921164985.6, name: the utility tunnel is interior work auxiliary system, it gathers the image of each subregion through image acquisition device, then confirm the present subregion that the staff is located according to the image, and then pertinently carry out environmental parameter's regulation or preconditioning, thereby need not use RFID wireless radio frequency identification location technique or iBeacon bluetooth location technique etc, therefore need not additionally increase equipment, reduce engineering cost, convenient operation management, in utility tunnel construction and operation maintenance, security and work efficiency when improving maintainer's inspection, the intelligent level of improvement utility tunnel engineering project.
The application adopts an image acquisition device to acquire images of all the subareas, then determines the subareas where the staff are currently positioned according to the images, and further carries out targeted adjustment or pre-adjustment of environmental parameters; the method and the device need to acquire rssi data for positioning by using Bluetooth, ultra-wideband and WIFI wireless opportunistic signals, and need to perform ray simulation on an opportunistic signal channel model.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method for analyzing an opportunity signal of an underground pipe gallery based on ray tracing, which comprises the steps of building different map models by utilizing the topography characteristics of the underground pipe gallery, building a signal simulation model by utilizing partial statistical characteristics of a radio frequency opportunity signal channel model in a corresponding pipe network map, analyzing the opportunity signal propagation model and channel characteristic parameters, carrying out modeling analysis on the long and narrow pipe gallery by adopting simulation software, and providing a positioning algorithm applying radio frequency opportunity signals.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a method for positioning an underground pipe gallery opportunistic signal analysis method based on ray tracing, which comprises the following specific steps of:
s1: determining a simulation scene, making a corresponding underground pipe network topological map through map drawing software, and making an underground pipe gallery area map to be positioned through a grid map module; collecting signal intensity and real positions of all fingerprint points, and establishing an underground pipe gallery opportunistic signal fingerprint distribution map; the cylindrical underground pipe gallery environment is simplified to a smooth three-dimensional figure;
s2: the map obtained in the step S1 is combined with a ray tracking simulation module to simulate and obtain the characteristic performance of a deterministic channel model of three radio frequency opportunistic signals, namely WIFI, UWB and Bluetooth;
s3: obtaining main energy contribution paths in three radio frequency opportunistic signal propagation paths by utilizing ray tracing simulation;
s4: according to the time delay power delay distribution, calculating the average additional time delay and the root mean square time delay of the channel, wherein the specific calculation mode is as follows:
1) Average additional delay:
2) Root mean square time delay:
s5: according to a common logarithmic path loss model, the attenuation degree of the average received power and the distance of the wireless signal in the ray tracing simulation is obtained, and the specific calculation formula is as follows:
wherein PL (d) 0 ) Represents the reference distance d 0 Received power at; n represents a path loss index; x is X σ Represents a normal random variable with a mean value of 0 and a standard deviation of sigma;
s6: and according to all simulation data obtained under different opportunistic signals, obtaining a cumulative distribution function of the ranging error, and comparing the cumulative distribution function with data obtained by fusing and positioning different opportunistic signals through a particle filter.
As a further improvement of the present invention, the step S1 specifically includes the following steps:
s11: and carrying out region division according to the information in the underground pipe gallery map, marking the region which can not be reached by the signal as an unreachable region according to the signal propagation characteristics, marking the region which can be reached by the signal as a reachable region, connecting different regions in the map through connection points, abstracting the underground pipe gallery map into a topological map, and enabling the topological path of the whole map to be a path component.
S12: assuming that a path planning field is square, dividing the field into a plurality of small square grids according to the side length of the grids, calculating according to one grid when the boundary is less than one grid, obtaining a grid map of row and col columns, dividing the grid map into an obstacle grid and a transmissible grid according to whether obstacle barriers exist in an underground pipe gallery, and representing the grid map containing obstacle information by using a row×col array matrix, namely a grid association matrix, wherein 0 represents that obstacles exist on the grid and cannot be transmitted; the grid transferable signal is denoted by 1;
s13: determining fingerprint points, acquiring signal intensity from WIFI, UWB and Bluetooth opportunistic signals at each fingerprint point, recording position information of each fingerprint point, storing data of the positioning fingerprint library in a gridding form through a last step of grid map, wherein each row of data represents corresponding multi-source signal intensity at a grid point and position information of the grid.
As a further improvement of the present invention, the step S2 specifically includes the following steps:
s21: firstly, initializing simulation parameters of a ray tracing simulation module, and setting electromagnetic parameters, positions of a transmitter and a receiver, waveforms of transmitted signals, frequencies, direct incidence, reflection times and reflection coefficient related parameters of room materials because an incident rebound method SBR model is used;
s22: tracking each ray one by one, firstly judging whether the signal intensity of the ray is attenuated below a threshold value, if the signal intensity of the ray is attenuated below the threshold value, not tracking the ray, if the signal intensity of the ray is not attenuated below the threshold value, judging whether the ray intersects an obstacle, if the signal intensity of the ray does not intersect the obstacle, judging whether the ray reaches the vicinity of a receiving sphere, and if the signal intensity of the ray reaches the receiving sphere, recording the signal intensity of the ray; if the signal intensity intersects with the obstacle, calculating the mathematical expression of the intersecting plane or edge, judging whether the signal intensity intersects with the plane or edge to calculate the signal intensity obtained by reflection and diffraction respectively, and returning to the loop judgment before starting execution to continue tracking until the signal intensity of the ray is reduced to be abandoned;
s23: for a narrowband signal such as a WIFI signal, the reflection coefficient can be directly multiplied in the frequency domain, while for a wideband signal such as a UWB signal, the bandwidth is particularly wide, and the center frequency cannot be used to replace the whole spectrum, so that the impulse response of the channel is directly obtained by convolution using the time domain reflection coefficient, the horizontal polarization and vertical polarization frequency domain coefficients are converted into the time domain, and the time domain form of the reflection coefficient can be obtained by laplace inverse change as follows:
wherein, l is defined as:
at the same time, K= (1- κ)/(1+κ)
Wherein, the liquid crystal display device comprises a liquid crystal display device,psi is glancing angle;
for solving the reflection coefficient of the multiple reflection problem, the reflection characteristics of a plurality of reflection interfaces need to be realized by convolution, the reflection coefficients on each reflection interface are respectively obtained, and then convolution is carried out to obtain the final reflection coefficient, and the method is concretely realized as follows:
as a further improvement of the invention, the S6 particle filter fusion positioning method comprises the following implementation steps:
s1: inputting an initial position of a target, wherein the unit distance of the target movement is used as prior information;
s2: the target initial position is obtained using the received RSSI information input deep confidence network. Assuming that N particles are randomly generated near the initial coordinates according to a set distribution;
s3: constructing a target motion equation by utilizing target motion information measured by the movable terminal, and predicting a new position by the particle set according to the motion equation;
s4: taking a fingerprint positioning result obtained by the deep confidence network as an observation value, and calculating the corresponding weight of each particle according to the distance between the particle set and the observation value;
s5: carrying out weighted summation on the particle set to obtain a final positioning result;
s6: and (3) screening particles according to a resampling principle, and returning the resampled particle set to the step (2). The positioning target of the target at the current moment can be obtained through the steps.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a method for researching and analyzing a opportunistic signal simulation and positioning algorithm of an underground pipe gallery based on ray tracing. Map analysis and simulation analysis are provided for a subsequent underground pipe gallery robot positioning system method by utilizing topology established for an underground pipe network, an area to be positioned, radio frequency distribution map modeling and space-time correlation simulation modeling of pipe gallery positioning opportunistic signal positioning, and meanwhile, the influence of factors such as different data information, environment interference basic arrangement and the like on radio frequency opportunistic signals can be analyzed according to a simulation result, so that a basis is provided for subsequent improvement of a positioning algorithm and simulation of mobile object navigation.
Drawings
FIG. 1 is a diagram of an analysis process of a simulation and positioning algorithm of a signal of opportunity of an underground pipe gallery;
FIG. 2 is a ray tracing flow chart;
FIG. 3 is a diagram of a opportunistic signal simulation based on ray tracing;
fig. 4 is a fusion localization map based on a particle filter algorithm.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and detailed description:
as shown in FIG. 1, the simulation analysis method for the opportunity signal of the underground pipe gallery based on ray tracing comprises the following specific steps:
s1: determining a simulation scene, making a corresponding underground pipe network topological map through map drawing software, and making an underground pipe gallery area map to be positioned through a grid map module; collecting signal intensity and real positions of all fingerprint points, and establishing an underground pipe gallery opportunistic signal fingerprint distribution map; the cylindrical underground piping environment is simplified here to a smooth solid.
Further, the specific operation of the step S1 is as follows:
s11: and carrying out region division according to the information in the underground pipe gallery map, and marking the region which can not be reached by the signal as an unreachable region according to the signal propagation characteristics, and marking the region which can be reached by the signal as a reachable region. Different areas in the map are connected by connection points. The map of the underground pipe gallery is abstracted into a topological map, and the topological path of the whole map is a path component.
S12: assuming that the path planning field is square, dividing the field into a plurality of small square grids (when the boundary is less than one grid, calculating according to one grid) according to the grid side length, and obtaining the grid map of row and col columns. The grid map may be divided into an obstacle grid and a transmissible grid according to the presence or absence of obstacle barriers within the underground piping lane. The grid map containing the obstacle information may be represented by a row×col array matrix, which is called a grid correlation matrix (abbreviated as correlation matrix). 0 indicates that the barrier is on the grid and cannot spread; the grid is denoted by 1 to which signals can be transferred.
S13: and determining fingerprint points, acquiring the signal intensity of the WIFI, UWB and Bluetooth opportunistic signals at each fingerprint point, and recording the position information of each fingerprint point. And storing the data of the positioning fingerprint library in a gridding form through a grid map of the last step, wherein each row of data represents the corresponding multi-source signal intensity at a certain grid point and the position information of the grid.
S2: and (3) simulating the map obtained in the step (1) by combining a ray tracking simulation module to obtain the characteristic performance of the deterministic channel model of the three radio frequency opportunistic signals of WIFI, UWB and Bluetooth.
Further, the specific operation of the step S2 is as follows:
s21: firstly, initializing simulation parameters of a ray tracing simulation module, and setting relevant parameters such as electromagnetic parameters of room materials, positions of a transmitter and a receiver, waveforms of transmitted signals, frequencies, direct incidence, reflection times, reflection coefficients and the like because an incident rebound method SBR model is used;
s22: as shown in fig. 2, tracking each ray one by one, firstly judging whether the signal intensity of the ray is attenuated below a threshold value, if the signal intensity is attenuated below the threshold value, not tracking the ray, if the signal intensity is attenuated below the threshold value, judging whether the ray intersects with an obstacle, if the signal intensity is not attenuated below the threshold value, judging whether the ray reaches the vicinity of a receiving sphere, and if the signal intensity is reached, recording the signal intensity of the ray; if the signal intensity intersects with the obstacle, calculating the mathematical expression of the intersecting plane or edge, judging whether the signal intensity intersects with the plane or edge to calculate the signal intensity obtained by reflection and diffraction respectively, and returning to the loop judgment before starting execution to continue tracking until the signal intensity of the ray is reduced to be abandoned;
s23: while a narrowband signal such as a WIFI signal can be directly multiplied by a reflection coefficient in the frequency domain, a wideband signal such as a UWB signal, which has a particularly wide bandwidth, cannot be replaced by a center frequency, and therefore the impulse response of a channel can be directly obtained by convolution using a time domain reflection coefficient. The frequency domain coefficients of horizontal polarization and vertical polarization are converted into the time domain, and the time domain form of the reflection coefficient can be obtained by utilizing the inverse change of Laplace as follows:
wherein, l can be defined as:
at the same time, K= (1- κ)/(1+κ)
Wherein, the liquid crystal display device comprises a liquid crystal display device,psi is glancing angle.
For solving the reflection coefficient of the multiple reflection problem, the reflection characteristics of a plurality of reflection interfaces need to be realized by convolution, the reflection coefficients on each reflection interface are respectively obtained, and then convolution is carried out to obtain the final reflection coefficient, and the method is concretely realized as follows:
s3: and obtaining main energy contribution paths in three radio frequency opportunistic signal propagation paths by utilizing ray tracing simulation.
S4: according to the time delay power delay distribution, calculating the average additional time delay and the root mean square time delay of the channel, wherein the specific calculation mode is as follows:
1) Average additional delay:
2) Root mean square time delay:
s5: according to a common logarithmic path loss model, the attenuation degree of the average received power and the distance of the wireless signal in the ray tracing simulation is obtained, and the specific calculation formula is as follows:
wherein PL (d) 0 ) Represents the reference distance d 0 Received power at; n represents a path loss index; x is X σ Represents a normal random variable with a mean value of 0 and a standard deviation sigma.
S6: and obtaining a cumulative distribution function of the ranging error according to all simulation data obtained under different opportunistic signal conditions.
As shown in FIG. 3, in the opportunistic signal fusion positioning method based on particle filtering, an RSSI fingerprint positioning method and a pedestrian navigation position measuring and calculating technology are realized by using a deep confidence network to respectively obtain rough positioning information, and then a particle filter is used for fusion filtering of two rough positioning results.
The motion model of any target device in the positioning area is assumed to be:
wherein k represents the time of day, the random variable X k For the target position prediction value, Y k For target position observations, Y in the present method k I.e. the rough positioning result obtained by the deep belief network, f k And h k As a nonlinear function. Delta k System noise, gamma, representing pedestrian space-time measurement technique k Representing ambient noise, independent of each other.
Constructing a collection of N particlesIn (1) the->Indicating the state of the ith particle at time k,then the weight of the particle at this time is indicated and +.>f(X k ) Representing the true coordinates of the target at time k, p (X k |Y 1∶k ) Indicating at this time X k Is a posterior probability density of (c). The final obtained positioning result is expressed as:
E[f(X k )]＝∫f(X k )p(X k |Y 1：k )dX k (2)
in practical application, it is very difficult to directly extract effective samples from posterior probability distribution, so that importance sampling method is introduced to improve sampling efficiency, and the known importance sampling density q (X k |Y k ) Sample extraction is performed to avoid direct extraction from p (X k |Y k ) Samples are drawn. Then formula (1) can be expressed as:
decomposing the importance density function as shown in formula (5):
q(X 1：k |Y 1：k )＝q(X 1：k-1 |Y 1：k-1 )q(X k |X 1：k-1 ，Y 1：k ) (5)
according to an importance sampling theory, selecting proper importance sampling density as follows:
q(X k |X 1：k-1 ，Y 1：k )＝q(X k |X 1：k-1 ，Y k )＝p(X k |X 1：k-1 ) (6)
posterior probability Density function p (X) k |X 1∶k ) Is in the form of a recurrence:
the particle weight represented by equation (3) can be expressed as an iterative form:
expressed in recursive form:
the expression (2) using the monte carlo sampling method is:
the formula (10) is as follows:
therefore, there are:
utilizing a set of weighted particlesThe position output result, which approximately represents the particle filtering, is:
in the particle weightThe method comprises the following steps:
at this time, as the iteration number increases, only a few particles are left to approach the real sample, and the weights of most of the "invalid" particles are extremely small, which causes serious waste of computing resources. Resampling means are therefore added to reduce the adverse consequences of particle degradation phenomena.
The complete opportunistic signal fusion positioning method based on particle filtering is implemented as follows, wherein a particle filtering algorithm fusion positioning chart is shown in fig. 4:
s1: inputting an initial position of a target, wherein the unit distance of the target movement is used as prior information;
s2: the target initial position is obtained using the received RSSI information input deep confidence network. Assuming that N particles are randomly generated near the initial coordinates according to a set distribution;
s3: constructing a target motion equation by utilizing target motion information measured by the movable terminal, and predicting a new position by the particle set according to the motion equation;
s4: taking a fingerprint positioning result obtained by the deep confidence network as an observation value, and calculating the corresponding weight of each particle according to the distance between the particle set and the observation value;
s5: carrying out weighted summation on the particle set to obtain a final positioning result;
s6: and (3) screening particles according to a resampling principle (the total number of the particles is unchanged), and returning the resampled particle set to the step (2). The positioning target of the target at the current moment can be obtained through the steps.
The above description is only of the preferred embodiment of the present invention, and is not intended to limit the present invention in any other way, but is intended to cover any modifications or equivalent variations according to the technical spirit of the present invention, which fall within the scope of the present invention as defined by the appended claims.
Claims (4)
1. The utility model provides a method for locating a signal-on-opportunity analysis method of an underground pipe gallery based on ray tracing, which comprises the following specific steps:
s1: determining a simulation scene, making a corresponding underground pipe network topological map through map drawing software, and making an underground pipe gallery area map to be positioned through a grid map module; collecting signal intensity and real positions of all fingerprint points, and establishing an underground pipe gallery opportunistic signal fingerprint distribution map; the cylindrical underground pipe gallery environment is simplified to a smooth three-dimensional figure;
s2: the map obtained in the step S1 is combined with a ray tracking simulation module to simulate and obtain the characteristic performance of a deterministic channel model of three radio frequency opportunistic signals, namely WIFI, UWB and Bluetooth;
s3: obtaining main energy contribution paths in three radio frequency opportunistic signal propagation paths by utilizing ray tracing simulation;
s4: according to the time delay power delay distribution, calculating the average additional time delay and the root mean square time delay of the channel, wherein the specific calculation mode is as follows:
1) Average additional delay:
2) Root mean square time delay:
s5: according to a common logarithmic path loss model, the attenuation degree of the average received power and the distance of the wireless signal in the ray tracing simulation is obtained, and the specific calculation formula is as follows:
wherein PL (d) 0 ) Represents the reference distance d 0 Received power at; n represents a path loss index; x is X σ Represents a normal random variable with a mean value of 0 and a standard deviation of sigma;
s6: and according to all simulation data obtained under different opportunistic signals, obtaining a cumulative distribution function of the ranging error, and comparing the cumulative distribution function with data obtained by fusing and positioning different opportunistic signals through a particle filter.
2. The method for locating a signal-of-opportunity analysis method of an underground pipe gallery based on ray tracing according to claim 1, wherein the method comprises the following steps:
the step S1 specifically comprises the following steps:
s11: and carrying out region division according to the information in the underground pipe gallery map, marking the region which can not be reached by the signal as an unreachable region according to the signal propagation characteristics, marking the region which can be reached by the signal as a reachable region, connecting different regions in the map through connection points, abstracting the underground pipe gallery map into a topological map, and enabling the topological path of the whole map to be a path component.
S12: assuming that a path planning field is square, dividing the field into a plurality of small square grids according to the side length of the grids, calculating according to one grid when the boundary is less than one grid, obtaining a grid map of row and col columns, dividing the grid map into an obstacle grid and a transmissible grid according to whether obstacle barriers exist in an underground pipe gallery, and representing the grid map containing obstacle information by using a row×col array matrix, namely a grid association matrix, wherein 0 represents that obstacles exist on the grid and cannot be transmitted; the grid transferable signal is denoted by 1;
s13: determining fingerprint points, acquiring signal intensity from WIFI, UWB and Bluetooth opportunistic signals at each fingerprint point, recording position information of each fingerprint point, storing data of the positioning fingerprint library in a gridding form through a last step of grid map, wherein each row of data represents corresponding multi-source signal intensity at a grid point and position information of the grid.
3. The method for locating a signal-of-opportunity analysis method of an underground pipe gallery based on ray tracing according to claim 1, wherein the method comprises the following steps: the step S2 specifically includes the following steps:
s21: firstly, initializing simulation parameters of a ray tracing simulation module, and setting electromagnetic parameters, positions of a transmitter and a receiver, waveforms of transmitted signals, frequencies, direct incidence, reflection times and reflection coefficient related parameters of room materials because an incident rebound method SBR model is used;
s22: tracking each ray one by one, firstly judging whether the signal intensity of the ray is attenuated below a threshold value, if the signal intensity of the ray is attenuated below the threshold value, not tracking the ray, if the signal intensity of the ray is not attenuated below the threshold value, judging whether the ray intersects an obstacle, if the signal intensity of the ray does not intersect the obstacle, judging whether the ray reaches the vicinity of a receiving sphere, and if the signal intensity of the ray reaches the receiving sphere, recording the signal intensity of the ray; if the signal intensity intersects with the obstacle, calculating the mathematical expression of the intersecting plane or edge, judging whether the signal intensity intersects with the plane or edge to calculate the signal intensity obtained by reflection and diffraction respectively, and returning to the loop judgment before starting execution to continue tracking until the signal intensity of the ray is reduced to be abandoned;
s23: for a narrowband signal such as a WIFI signal, the reflection coefficient can be directly multiplied in the frequency domain, while for a wideband signal such as a UWB signal, the bandwidth is particularly wide, and the center frequency cannot be used to replace the whole spectrum, so that the impulse response of the channel is directly obtained by convolution using the time domain reflection coefficient, the horizontal polarization and vertical polarization frequency domain coefficients are converted into the time domain, and the time domain form of the reflection coefficient can be obtained by laplace inverse change as follows:
wherein, l is defined as:
at the same time, K= (1- κ)/(1+κ)
Wherein, the liquid crystal display device comprises a liquid crystal display device,psi is glancing angle;
for solving the reflection coefficient of the multiple reflection problem, the reflection characteristics of a plurality of reflection interfaces need to be realized by convolution, the reflection coefficients on each reflection interface are respectively obtained, and then convolution is carried out to obtain the final reflection coefficient, and the method is concretely realized as follows:
4. the method for locating a signal-of-opportunity analysis method of an underground pipe gallery based on ray tracing according to claim 1, wherein the method comprises the following steps: the S6 particle filter fusion positioning method comprises the following implementation steps:
s1: inputting an initial position of a target, wherein the unit distance of the target movement is used as prior information;
s2: the target initial position is obtained using the received RSSI information input deep confidence network. Assuming that N particles are randomly generated near the initial coordinates according to a set distribution;
s3: constructing a target motion equation by utilizing target motion information measured by the movable terminal, and predicting a new position by the particle set according to the motion equation;
s4: taking a fingerprint positioning result obtained by the deep confidence network as an observation value, and calculating the corresponding weight of each particle according to the distance between the particle set and the observation value;
s5: carrying out weighted summation on the particle set to obtain a final positioning result;
s6: and (3) screening particles according to a resampling principle, and returning the resampled particle set to the step (2). The positioning target of the target at the current moment can be obtained through the steps.
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