CN110233667A - VLC dynamic positioning method and system based on average drifting and Unscented kalman filtering - Google Patents
VLC dynamic positioning method and system based on average drifting and Unscented kalman filtering Download PDFInfo
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Abstract
The invention discloses a kind of VLC dynamic positioning method and system based on average drifting and Unscented kalman filtering, method includes the following steps: first, the light on and off of LED light high frequency are controlled by LED drive circuit, region existing for LED is found by LED-ID identification, identifies the ID of LED, and obtains the initial position of positioning terminal.Secondly, the LED in Mean-shift algorithm and Unscented kalman filtering dynamically track image sequence, calculates the relative position of present frame LED and initial frame LED pixel coordinate.Then, in conjunction with the initial position of positioning terminal and its relative positional relationship in subsequent frames, positioning terminal is obtained in position in the real world, realizes real-time positioning.The present invention has the ability of tracking high-speed target, improves positioning accuracy when LED is blocked, even if the LED of half is shielding, precision can also be kept.It additionally has good robustness and real-time, positioning field has broad application prospects indoors.
Description
Technical field
The present invention relates to technical field of visible light communication, and in particular to one kind is based on average drifting and Unscented kalman filtering
VLC dynamic positioning method and system.
Background technique
In commercial center, large public building (subway, airport, library etc.), high risk industrial park, hospital, endowment
The GPS booster action such as institute is less, there is an urgent need to the indoor spaces of navigator fix service, and indoor positioning technologies are with wide application
Prospect.
Common indoor positioning technologies have infrared ray (IR), ultrasonic wave, radio frequency identification (RFID), WLAN
(WLAN), bluetooth and ultra wide band (UWB) etc..Based on these technologies, different implementations is developed.But, it is contemplated that positioning
The ability or hardware device of precision or electromagnetism interference are at high cost, these all have the shortcomings that obvious, it is difficult to promote.
Compared with above-mentioned indoor positioning technologies, it is seen that light-seeking (VLP) technology abundant, electromagnetism interference with bandwidth resources
The outstanding advantages such as ability is strong, positioning accuracy is high, light capabilities are strong.In addition, supporting the hardware device cost of VLP system relatively
It is small, because not needing the complex device of progress precise measurement.Therefore, positioning field has wide application to VLP technology indoors
Prospect.
There are both of which for indoor locating system based on VLP: being based on the positioning of photodiode (PD) and is based on image
The positioning of sensor (IS).And since photodiode is to the sensibility of beam direction, the mobility of positioning terminal receives sternly
The limitation of weight, there is also another significant drawback, i.e. robustness are poor for it.Even if duplicate measurements can also generate in same position
The undulating value of light intensity.In addition, the VLP system based on PD is easy the interference by environment light and reflected light.Meanwhile it needing to reception
The angle and signal strength arrived carries out precise measurement.Otherwise positioning result has apparent error.
Theoretically, IS the field VLP performance be better than PD, but it is existing research positioning accuracy, real-time and
It does not all achieve satisfactory results in terms of robustness.When target is mobile with faster speed, the image of LED can become mould
Paste, this may result in positioning failure.In addition when the optical path between LED and positioning terminal is blocked, because of most of positioning
Algorithm is all based on two or more LED, and in the case where a LED is lacked in image, algorithm probably be will fail.In addition,
Screen effect is a fatal problem in the field VLP, and such as solving the problems, such as this again using optical flow method can make calculation amount become very big.
On the other hand, smart phone has been equipped with high-resolution complementary metal oxide semiconductor (CMOS) sensing at present
Device camera can be combined easily with the VLP method based on IS, have huge commercial value.
Summary of the invention
The purpose of the invention is to the deficiencies for existing method and the performance of VLP system, and cater to currently to room
The requirement of interior location technology provides a kind of VLC dynamic positioning method and system based on average drifting and Unscented kalman filtering.
First technical purpose of the invention can be reached by adopting the following technical scheme that:
A kind of VLC dynamic positioning method based on average drifting and Unscented kalman filtering, the dynamic positioning side VLC
Method includes the following steps；
S1, by LED drive circuit control the light on and off of LED light high frequency, by LED-ID identification find region existing for LED,
It identifies the ID of LED, and obtains the initial position of positioning terminal；
S2, worked as with the LED light in Mean-shift algorithm and Unscented kalman filtering dynamically track image sequence and calculating
The relative positional relationship of previous frame and initial frame LED light pixel coordinate；
S3, by the initial position of the positioning terminal in conjunction with relative positional relationship, obtain positioning terminal the world sit
Current location in mark system.
Further, the step S1 process is as follows:
S101, the on-off that output electric current is controlled by LED drive circuit, make LED light according to specified frequency scintillation；
S102, LED light is shot using video camera, is that camera takes the LED on image by the frequency translation of LED light light on and off
Lamp bar line quantity information；
S103, camera pass through USB connecting line for image transmitting to positioning terminal, then pass through local network transport to server
LED image is converted into grayscale image by end, server end, is then carried out gaussian filtering and binary conversion treatment, is obtained binaryzation LED strip
Print image, then feature extraction and feature detection are carried out to binaryzation LED strip print image, obtain the ID of LED light；
S104, positioning terminal is calculated i.e. using the geometrical relationship in the world coordinates and world coordinate system of multiple LED light
The practical initial position of robot.
Further, the step S2 the following steps are included:
S201, the model that tracking target is described using Epanechikov kernel function, it is assumed that current frame image tracks target
Initial position is positioned at target in the central point b of previous frame picture position first0, to target area current image frame probability
Density is estimated, central point b is calculated0Bhattacharyya coefficient ρ (b0) with other point Bhattacharyya coefficient ρ
(b), if ρ (b) > ρ (b0), then by the central transference of region of search to point b, such as fruit dot b and central point b0Distance is less than arbitrarily small
Constant ε then terminates this circulation, and point b is exactly the central point of the tracking target area in current frame image, otherwise repeats above-mentioned meter
It calculates；
Above is the process of Mean-shift algorithm；
S202, Unscented kalman filtering optimization algorithm is introduced, ifFor the state vector of target,
Wherein x and y indicates the coordinate of target's center's point；WithRespectively indicate the derivative of x and y, the i.e. speed of target；hk|k-1Indicate mesh
Mark the kernel function bandwidth variation from the k-1 moment to the k moment, YkFor the observational variable of target, Unscented kalman filtering is initialized:
For state vector XkMean value, PkFor state vector XkVariance；
If the state vector of target is in k-1 moment corresponding i-th of Sigma pointThe quantity of Sigma point is 2n+1
It is a.
Calculate Sigma point:
In formulaα is parameter to be selected, 0 < α≤10-4；
WithIt indicates to bring Sigma point into following shape to the observation at target's center in i-th of Sigma point of k moment
State transfer equation F and observational equation H:
Average value before calculating this moment state vector and observational variable update:
For observational variable YkAverage value,For the weight coefficient of mean value,
Calculate kalman gain K:
In formula β >=0 and herein value are zero.
And the mean value and variance for updating state vector are brought in following two formula into；
S203, Unscented kalman filtering is introduced into Mean-shift algorithm, predicts present frame mesh using Unscented kalman filtering
Most likely location is marked, then as the prior information of Mean-shift algorithm, Mean-shift algorithm is allowed to search for the region, when
When LED light is blocked, using mean shift algorithm to the tracking result of target as the observation model of Unscented kalman filtering, in conjunction with
Noise matrix obtains final output.
Further, the step S3 is closed using the relative position of present frame LED light and initial frame LED light pixel coordinate
System establishes the Linear Mapping from pixel coordinate to world coordinates by the coordinate transform between different coordinate systems, then utilizes
Geometry site between LED light and positioning terminal calculates present frame and initial frame positioning terminal by similar triangles
Positioning terminal is calculated in world coordinate system in conjunction with the initial position of positioning terminal in the relative positional relationship of pixel coordinate
In position.
Further, the estimation procedure that probability density is carried out in the step S201 is as follows:
In kth frame image, it is assumed that have nkA pixelIn target area, kernel function bandwidth is h, by feature
Space uniform is divided into m subinterval, then object model feature value u=1 ..., the Multilayer networks of m are as follows:
WhereinFor normaliztion constant, function k () is the profile function of kernel function, by distance
Heart point (i.e. a in formula0) distance measure the weight of each pixel, t (ai) it is point aiThe characteristic value at place, δ (t (ai)-u) and work
With being to judge whether the characteristic value of pixel belongs to u-th of section, initial frame central point is a0, Multilayer networks are
Further, the accounting equation of the Bhattacharyya coefficient is as follows:
WhereinFor i-th point of weight.
Further, the white Gaussian noise matrix W of the state transfer equation F and observational equation HkAnd VkSatisfaction with
Lower statistical property:
Wk~N (0, Qk)Vk~N (0, Rk)
Wherein, QkAnd RkThe covariance matrix of respectively two noise matrixes.
Another technical purpose of the invention can be reached by adopting the following technical scheme that:
A kind of VLC dynamic positioning system based on average drifting and Unscented kalman filtering, the VLC dynamic positioning system
System includes VLC transmitting terminal, the receiving end VLC and server end；
Wherein, the VLC transmitting terminal includes 28V constant pressure source, LED drive circuit, LED light and power supply adaptor；Wherein,
The LED light is powered by 28V constant pressure source；The LED drive circuit control LED light generates high frequency light on and off；The power supply
Adapter converts the voltage of input to after 5V voltage and individually powers to LED drive circuit；
The receiving end VLC includes positioning terminal and camera subsystem；Wherein, the camera subsystem includes
CMOS camera, CMOS camera parameter setting module；The CMOS camera is for persistently shooting the real-time figure of LED light
Picture；The CMOS camera is connected with positioning terminal, by the image transmitting of acquisition to positioning terminal；The positioning terminal packet
Include WIFI module and liquid crystal display, the WIFI module is by image transmitting to server end；
The server end includes image processing subsystem, image trace module and Unscented kalman filtering device；It is described
Image processing subsystem include color image converting gradation image module, image filtering module, image binaryzation module and LED
Lamp identification module, each module, which is sequentially connected, receives and processes image realization LED light identification, and the ID of image and LED light is transferred to
Unscented kalman filtering device and image trace module；The Unscented kalman filtering device include LED light position prediction module,
Current LED prediction result is transferred to image trace module by LED light position optimization module, Kalman filtering parameter updating module；
The image trace module is transferred to positioning terminal by WIFI module to object real-time tracking and positioning, by location information,
Apply control voltage eventually by the liquid crystal display to positioning terminal and shows location information.
Further, the LED drive circuit uses one piece of STM32 system board as chip, while using DD311 high
The on-off of the electric current of frequency switch control LED light makes LED light generate high frequency light on and off.
Further, when the CMOS camera parameter setting module is used to be arranged the focal length of CMOS camera, exposure
Length, exposure compensating, sensitivity, so that CMOS camera be enable clearly to capture LED image.
The present invention has the following advantages and effects with respect to the prior art:
1, the method that uses of the present invention is it is only necessary to know that the initial position of LED and the relative positional relationship with positioning terminal,
Avoid the VLP location algorithm of repetition complexity.
2, the present invention tracks the movement LED in imaging sensor using Mean-shift algorithm, solves fuzzy effect
Fruit problem, ensure that real-time.
3, the present invention uses Unscented kalman filtering, improves the maximum allowable movement velocity of positioning terminal, reduces calculation
The runing time of method.
4, the present invention takes the Mean-shift algorithm having good robustness, under simple background, even if big in image
Part LED is blocked, and VLP method will not fail.
5, the present invention will be seen that the result of light-seeking method is combined with the noise matrix of Unscented kalman filtering, obtain
Output when LED is blocked reduces the error of positioning result.
Detailed description of the invention
Fig. 1 is showing for the VLC dynamic positioning method based on average drifting and Unscented kalman filtering disclosed in the present invention
It is intended to；
Fig. 2 is industrial camera rolling screen door effect operation principle schematic diagram in the embodiment of the present invention；
Fig. 3 is in the present invention without mark conversion process schematic diagram；
Fig. 4 is the schematic diagram of world coordinate system in the present invention, image coordinate system and camera coordinates system；
Fig. 5 is the schematic diagram of LED and imaging sensor geometrical relationship in the embodiment of the present invention；
Fig. 6 is setting for the VLC dynamic positioning system based on average drifting and Unscented kalman filtering disclosed in the present invention
Standby composite structural diagram.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without making creative work, shall fall within the protection scope of the present invention.
Embodiment one
As shown in Fig. 1, it is fixed based on the VLC of average drifting and Unscented kalman filtering dynamic that present embodiment discloses a kind of
Position method, including the following steps:
S1, each LED light of control are with different frequency light on and off.When the starting of dynamic visible light location algorithm, pass through
LED-ID recognizer finds the region where LED light, then identifies the ID of each LED, and obtains the initial bit of positioning terminal
It sets；
In specific embodiment, step S1 includes following sub-step:
S101, the pulse train by mcu programming, making its different pins output different frequency, by different frequency
Pulse train is input to LED drive circuit, the on-off of LED drive circuit control output electric current, to make LED according to specified frequency
Rate flashing；
S102, as shown in Fig. 2, it is fast using the rolling with complementary metal oxide semiconductor (CMOS) imaging sensor
The video camera of door machine structure shoots LED light, expose line by line using it, reads the characteristics of data line by line, by the frequency of LED light light on and off turn
It turns to camera and takes LED light striped quantity information on image；
S103, camera pass through USB connecting line for image transmitting to positioning terminal, then pass through local network transport to server
LED image is converted into grayscale image by end, server end, is then carried out gaussian filtering and binary conversion treatment, is obtained binaryzation LED strip
Print image, then feature extraction and feature detection are carried out to binary image, to obtain the ID of LED light；
S104, the ID for obtaining each LED can be obtained the world coordinates of each LED, utilize the world coordinates and generation of multiple LED light
Geometrical relationship in boundary's coordinate system can calculate the practical initial position of the i.e. robot of positioning terminal；
S2, with the LED light in Mean-shift algorithm and Unscented kalman filtering dynamically track image sequence, pass through the algorithm
Calculate the relative positional relationship of present frame and initial frame LED light pixel coordinate；
In specific embodiment, step S2 includes following sub-step:
S201, the model that tracking target is described using Epanechikov kernel function, in kth frame image, it is assumed that have nkIt is a
PixelIn target area, it is assumed that the initial position of current frame image tracking target is positioned at target upper one first
The central point b of frame picture position0, kernel function bandwidth is h, feature space is evenly dividing as m subinterval, then object module spy
Value indicative u=1 ..., the Multilayer networks of m are as follows:
WhereinFor normaliztion constant, function k () is the profile function of kernel function, by distance
Heart point (i.e. a in formula0) distance measure the weight of each pixel, t (ai) it is point aiThe characteristic value at place, δ (t (ai)-u) and work
With being to judge whether the characteristic value of pixel belongs to u-th of section.Initial frame central point is a0, Multilayer networks are
Using Density Estimator, the weight of each sample point is made to arrive the distance dependent of central point with them, can guaranteed
Value drift process converges on kernel function near the point of estimation density zero.
Recycle following equation calculation b0Bhattacharyya coefficient ρ (b0):
Then the Bhattacharyya coefficient ρ (b) of each point is calculated using following formula:
WhereinFor the weight of each point.
Bhattacharyya coefficient and Mean-shift algorithm have good fitness, therefore select Bhattacharyya
Coefficient carrys out the similitude between metric objective model and candidate family.
If ρ (b) > ρ (b0), then by the central transference of region of search to b point, if b and b0Distance is less than arbitrarily small constant
ε then terminates this circulation, and point b is exactly the central point of the tracking target area in current frame image, otherwise repeats above-mentioned calculating.
S202, Unscented kalman filtering optimization algorithm is introduced, ifFor the state vector of target,
Wherein x and y indicates the coordinate of target's center's point；WithRespectively indicate the derivative of x and y, the i.e. speed of target；hk|k-1Indicate mesh
Mark the kernel function bandwidth variation from the k-1 moment to the k moment.YkFor the observational variable of target.It is first initial to Unscented kalman filtering
Change:
For state vector XkMean value, PkFor state vector XkVariance；
If the state vector of target is in k-1 moment corresponding i-th of Sigma pointThe quantity of Sigma point is 2n+1
It is a.Then as shown in Fig. 3, with following three equation calculations Sigma point:
In formulaα is candidate parameter, 0 < α≤10-4。
WithIt indicates in i-th of Sigma point of k moment to the observation at target's center.Bring Sigma point into following shape
State transfer equation F and observational equation H:
Then the average value before this moment state vector and observational variable update can be calculated by following equation:
For observational variable YkAverage value,For the weight coefficient of mean value,
Kalman gain K is calculated by following formula again:
In formula β >=0 and herein value are zero.
K is finally brought into following two formula to the mean value and variance for updating state vector:
The target position that Unscented kalman filtering algorithm calculates every time can be all compared with its a upper position, thus
Update and have modified the state model of Unscented kalman filtering；
S203, Unscented kalman filtering is introduced into Mean-shift algorithm, predicts present frame mesh using Unscented kalman filtering
Most likely location is marked, then as the prior information of Mean-shift algorithm, Mean-shift algorithm is allowed to search for the region.Pass through
The program can effectively reduce the quantity of iteration, improve the real-time of positioning, and improve the maximum allowable fortune of positioning terminal
Scanning frequency degree.
In addition, the white Gaussian noise matrix W of state transfer equation and observational equationkAnd VkMeet following statistical property:
Wk~N (0, Qk)Vk~N (0, Rk)
Wherein QkAnd RkThe covariance matrix of respectively two noise matrixes.When LED is blocked, by mean shift algorithm
Observation model to the tracking result of target as Unscented kalman filtering, obtains final output in conjunction with noise matrix, can be with
Effectively solve the case where LED is blocked.
By combining the Mean-shift algorithm of Unscented kalman filtering that present frame LED light and initial frame LED can be obtained above
The relative positional relationship of lamp pixel coordinate.
S3, using the relative positional relationship obtained in the initial position and S2 of the positioning terminal obtained in S1, positioned
Current location of the terminal in world coordinate system；
In specific embodiment, step S3 process is as follows:
Using the relative positional relationship of present frame LED light and initial frame LED light pixel coordinate, as shown in Fig. 4, by not
The Linear Mapping from pixel coordinate to world coordinates is established in coordinate transform between same coordinate system, then, as shown in Fig. 5,
Using the geometry site between LED light and positioning terminal, present frame can be calculated by similar triangles and is determined with initial frame
Positioning terminal is finally calculated in conjunction with the initial position of positioning terminal in the relative positional relationship of the pixel coordinate of position terminal
Position in world coordinate system.
Embodiment two
As shown in fig. 6, present embodiment discloses a kind of VLC dynamic positioning based on average drifting and Unscented kalman filtering
System, the VLC dynamic positioning system include: VLC transmitting terminal, the receiving end VLC and server end.
Wherein, VLC transmitting terminal includes 28V constant pressure source, LED drive circuit, LED light and power supply adaptor；
LED light is powered by 28V constant pressure source, and LED light can be made to have constant and suitable light intensity；LED drive circuit is by one piece
STM32 system board is as chip, using the on-off of the electric current of DD311 HF switch control LED light, make LED light generate human eye without
The light on and off frequency of the high frequency light on and off of method identification, each LED light is different, therefore above-mentioned LED can be used as the optical signal transmitter of unique ID；
The voltage that the voltage of input is converted into 5V or so is that LED drive circuit is individually powered by power supply adaptor.
The receiving end VLC includes positioning terminal and camera subsystem；
Camera subsystem is made of CMOS camera, CMOS camera parameter setting module, and CMOS camera parameter is set
Focal length, exposure time, the exposure compensating, sensitivity for setting module setting CMOS camera, to keep CMOS camera clear
Capture LED image；CMOS camera persistently shoots the realtime graphic of LED light, because of the rolling screen door effect of camera, i.e., exposes line by line
Light, the characteristic for reading data line by line can get the image with different light and shade fringe number purpose LED light；Camera and positioning terminal
It is connected directly, by the image transmitting of acquisition to positioning terminal；Positioning terminal includes WIFI module and liquid crystal display, passes through WIFI
Module is by image transmitting to server end；
Server end includes image processing subsystem, image trace module and Unscented kalman filtering device；
Image processing subsystem includes color image converting gradation image module, image filtering module, image binaryzation mould
Block and LED light identification module, each module, which is sequentially connected, receives and processes image realization LED identification, then the ID of image and LED are passed
It is defeated to arrive Unscented kalman filtering device and image trace module；Unscented kalman filtering device includes LED light position prediction module, LED
Lamp position sets optimization module, Kalman filtering parameter updating module；Current LED prediction result is transferred to by Unscented kalman filtering device
Image trace module；Real-time tracking and positioning of the image trace module to target, location information is transferred to using WIFI module
Positioning terminal applies control voltage eventually by the liquid crystal display to positioning terminal and shows location information.
The present invention is positioned using Unscented kalman filtering device assistant images tracking module, can effectively improve positioning
Real-time and robustness are suitable for mobile terminal device, such as smart phone, tablet computer, mobile robot.
The above embodiment is a preferred embodiment of the present invention, but embodiments of the present invention are not by above-described embodiment
Limitation, other any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present invention,
It should be equivalent substitute mode, be included within the scope of the present invention.
Claims (10)
1. a kind of VLC dynamic positioning method based on average drifting and Unscented kalman filtering, which is characterized in that the VLC
Dynamic positioning method includes the following steps；
S1, the light on and off of LED light high frequency are controlled by LED drive circuit, region existing for LED, identification is found by LED-ID identification
The ID of LED out, and obtain the initial position of positioning terminal；
S2, with the LED light in Mean-shift algorithm and Unscented kalman filtering dynamically track image sequence and present frame is calculated
With the relative positional relationship of initial frame LED light pixel coordinate；
S3, by the initial position of the positioning terminal in conjunction with relative positional relationship, obtain positioning terminal in world coordinate system
In current location.
2. the VLC dynamic positioning method according to claim 1 based on average drifting and Unscented kalman filtering, feature
It is, the step S1 process is as follows:
S101, the on-off that output electric current is controlled by LED drive circuit, make LED light according to specified frequency scintillation；
S102, LED light is shot using video camera, is that camera takes the LED light bar on image by the frequency translation of LED light light on and off
Line quantity information；
S103, camera by USB connecting line by image transmitting to positioning terminal, then pass through local network transport to server end,
LED image is converted into grayscale image by server end, is then carried out gaussian filtering and binary conversion treatment, is obtained binaryzation LED strip line
Image, then feature extraction and feature detection are carried out to binaryzation LED strip print image, obtain the ID of LED light；
S104, positioning terminal i.e. machine is calculated using the geometrical relationship in the world coordinates and world coordinate system of multiple LED light
The practical initial position of people.
3. the VLC dynamic positioning method according to claim 1 based on average drifting and Unscented kalman filtering, feature
Be, the step S2 the following steps are included:
S201, the model that tracking target is described using Epanechikov kernel function, it is assumed that current frame image tracks the initial of target
Position is positioned at target in the central point b of previous frame picture position first0, to target area current image frame probability density
Estimated, calculates central point b0Bhattacharyya coefficient ρ (b0) with other point Bhattacharyya coefficient ρ (b),
If ρ (b) > ρ (b0), then by the central transference of region of search to point b, such as fruit dot b and central point b0Distance is less than arbitrarily small constant
ε then terminates this circulation, and point b is exactly the central point of the tracking target area in current frame image, otherwise repeats above-mentioned calculating；
S202, it setsFor the state vector of k moment target, wherein x and y indicates the seat of target's center's point
Mark；WithRespectively indicate the derivative of x and y, the i.e. speed of target；hk|k-1Indicate target from the k-1 moment to the kernel function at k moment
Bandwidth variation, YkFor the observational variable of k moment target, Unscented kalman filtering is initialized:
For state vector XkMean value, PkFor state vector XkVariance；
If the state vector of target is in k-1 moment corresponding i-th of Sigma pointThe quantity of Sigma point is 2n+1；
Calculate Sigma point:
In formulaα is parameter to be selected, 0 < α≤10-4；
WithIt indicates that the observation at target's center is brought Sigma point into following state and converted in i-th of Sigma point of k moment
Equation F and observational equation H:
Average value before calculating this moment state vector and observational variable update:
For observational variable YkAverage value,For the weight coefficient of mean value,
Calculate kalman gain K:
In formula And value is zero herein；
And the mean value and variance for updating state vector are brought in following two formula into:
S203, present frame target most likely location is predicted using Unscented kalman filtering, then as Mean-shift algorithm
Prior information, allow Mean-shift algorithm to search for the region, when LED light is blocked, by mean shift algorithm to target with
Observation model of the track result as Unscented kalman filtering obtains final output in conjunction with noise matrix.
4. the VLC dynamic positioning method according to claim 1 based on average drifting and Unscented kalman filtering, feature
It is, the step S3 utilizes the relative positional relationship of present frame LED light and initial frame LED light pixel coordinate, passes through difference
Coordinate system between coordinate transform establish Linear Mapping from pixel coordinate to world coordinates, then utilize LED light and positioning
Geometry site between terminal is calculated the phase of present frame with the pixel coordinate of initial frame positioning terminal by similar triangles
Position of the positioning terminal in world coordinate system is calculated in conjunction with the initial position of positioning terminal to positional relationship.
5. the VLC dynamic positioning method according to claim 3 based on average drifting and Unscented kalman filtering, feature
It is, the estimation procedure that probability density is carried out in the step S201 is as follows:
In kth frame image, it is assumed that have nkA pixelIn target area, kernel function bandwidth is h, by feature space
It is evenly dividing as m subinterval, then object model feature value u=1 ..., the Multilayer networks of m are as follows:
WhereinFor normaliztion constant, function k () is the profile function of kernel function, passes through distance center point b0
Distance measure the weight of each pixel, t (ai) it is point aiThe characteristic value at place, δ (t (ai)-u) and effect be to judge pixel
Whether characteristic value belongs to u-th of section, and initial frame central point is a0, Multilayer networks are
6. the VLC dynamic positioning method according to claim 3 based on average drifting and Unscented kalman filtering, feature
It is, the accounting equation of the Bhattacharyya coefficient is as follows:
WhereinFor i-th point of weight.
7. the VLC dynamic positioning method according to claim 3 based on average drifting and Unscented kalman filtering, feature
It is, the white Gaussian noise matrix W of the state transfer equation F and observational equation HkAnd VkMeet following statistical property:
Wk~N (0, Qk)Vk~N (0, Rk)
Wherein, QkAnd RkThe covariance matrix of respectively two noise matrixes.
8. a kind of VLC dynamic positioning system based on average drifting and Unscented kalman filtering, which is characterized in that the VLC
Dynamic positioning system includes VLC transmitting terminal, the receiving end VLC and server end；
Wherein, the VLC transmitting terminal includes 28V constant pressure source, LED drive circuit, LED light and power supply adaptor；Wherein, described
LED light powered by 28V constant pressure source；The LED drive circuit control LED light generates high frequency light on and off；The power adaptation
Device converts the voltage of input to after 5V voltage and individually powers to LED drive circuit；
The receiving end VLC includes positioning terminal and camera subsystem；Wherein, the camera subsystem includes CMOS
Camera, CMOS camera parameter setting module；The CMOS camera is for persistently shooting the realtime graphic of LED light；Institute
The CMOS camera stated is connected with positioning terminal, by the image transmitting of acquisition to positioning terminal；The positioning terminal includes
WIFI module and liquid crystal display, the WIFI module is by image transmitting to server end；
The server end includes image processing subsystem, image trace module and Unscented kalman filtering device；The figure
As processing subsystem includes that color image converting gradation image module, image filtering module, image binaryzation module and LED light are known
Other module, each module, which is sequentially connected, receives and processes image realization LED light identification, and the ID of image and LED light is transferred to no mark
Kalman filter and image trace module；The Unscented kalman filtering device includes LED light position prediction module, LED light
Current LED prediction result is transferred to image trace module by position optimization module, Kalman filtering parameter updating module；It is described
Image trace module to object real-time tracking and positioning, location information is transferred to positioning terminal by WIFI module, finally
Apply control voltage by the liquid crystal display to positioning terminal and shows location information.
9. the VLC dynamic positioning system according to claim 8 based on average drifting and Unscented kalman filtering, feature
It is, the LED drive circuit is controlled using one piece of STM32 system board as chip, while using DD311 HF switch
The on-off of the electric current of LED light makes LED light generate high frequency light on and off.
10. the VLC dynamic positioning system according to claim 8 based on average drifting and Unscented kalman filtering, special
Sign is that the focal length, exposure time, exposure that the CMOS camera parameter setting module is used to be arranged CMOS camera are mended
It repays, sensitivity, so that CMOS camera be enable clearly to capture LED image.
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