CN114362823A - Indoor environment modeling method based on visible light communication and positioning - Google Patents
Indoor environment modeling method based on visible light communication and positioning Download PDFInfo
- Publication number
- CN114362823A CN114362823A CN202210027519.3A CN202210027519A CN114362823A CN 114362823 A CN114362823 A CN 114362823A CN 202210027519 A CN202210027519 A CN 202210027519A CN 114362823 A CN114362823 A CN 114362823A
- Authority
- CN
- China
- Prior art keywords
- led
- obstacle
- algorithm
- leds
- light signal
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000004891 communication Methods 0.000 title claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 52
- 230000003287 optical effect Effects 0.000 claims abstract description 30
- 239000011159 matrix material Substances 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000005540 biological transmission Effects 0.000 claims description 3
- 230000005855 radiation Effects 0.000 claims description 3
- 238000001514 detection method Methods 0.000 abstract description 7
- 238000005562 fading Methods 0.000 abstract description 3
- 230000008447 perception Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 2
- 230000004888 barrier function Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
Images
Landscapes
- Optical Communication System (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The invention discloses an indoor environment modeling method based on visible light communication and positioning, which comprises the following steps: arranging a plurality of LEDs as optical signal transmitters in a room, and enabling the PD to receive optical signals of the LEDs at different positions in the room; if the intensity of the light signal of a certain LED received by the PD at a certain position is significantly smaller than the theoretical value calculated by the lambert model, there is a high possibility that the current position between the LED and the PD is blocked by an obstacle. Two algorithms are adopted to detect indoor obstacles according to the characteristic that the optical signal propagates along a straight line. The first algorithm realizes two-dimensional detection of obstacles by identifying the blocked optical link; the second algorithm achieves three-dimensional perception of the indoor environment by identifying unobstructed optical links. The invention expands the service types of the optical communication system, can sense the three-dimensional information of the environment on the premise of not using equipment such as laser radar and the like, and provides data support for path planning and wireless channel fading prediction.
Description
Technical Field
The invention relates to the technical field of visible light positioning, in particular to an indoor environment modeling method based on visible light communication and positioning.
Background
Visible Light Communication (VLC) has received attention because of its advantages such as high transmission rate, no need for spectrum application, no electromagnetic interference, and high security. Because the propagation of the optical signal has strong directivity, the VLC system may also support a high-precision Visible Light Positioning (VLP) service. In addition to localization, the light signal may also be used for indoor environment modeling, i.e. detection of indoor obstacles.
Most of the existing indoor obstacle detection technologies rely on smart phones, cameras or laser radars, and not only high hardware cost and complex algorithm design are needed, but also high labor cost is needed to complete the collection of environmental information. Therefore, the existing technology cannot support low-cost indoor environment modeling, and is difficult to be applied to some systems with limited budgets.
Disclosure of Invention
In view of this, the present invention aims to provide an indoor environment modeling method based on visible light communication and positioning, so as to solve the problems of high cost and relatively high algorithm complexity of the existing indoor environment modeling algorithm. Meanwhile, whether the obstacle exists on the current link is judged only according to the light signals received by the PD during movement, and the judgment is realized without depending on a complex algorithm.
In order to achieve the purpose, the invention adopts the following technical scheme:
an indoor environment modeling method based on visible light communication and positioning, the modeling method comprising the following steps:
step S1, aiming at an indoor scene, arranging n LEDs on the top of the indoor scene, wherein the n LEDs are used for emitting optical signals, and an obstacle is also arranged in the indoor scene and is modeled as a single cuboid or a combination of a plurality of cuboids;
step S2, for the indoor scene, setting a PD at the bottom thereof, the PD being used for measuring the light signal intensity of the LED and having in-plane moving capability, the PD measuring the corresponding light signal intensity by moving u positions in the indoor scene;
step S3, executing algorithm one or executing algorithm two to obtain the estimated range of the obstacle, wherein,
the first algorithm comprises the following steps:
let the PD traverse through all u positions, assuming that it receives the light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein, alpha is a coefficient representing a threshold value, an area on the ith position of the PD and the jth LED connecting line is marked as an obstacle area detected by the jth LED;
after traversing is completed, intersecting the obstacle regions detected by each LED to obtain the range estimation of the obstacle;
the second algorithm comprises the following steps:
let the PD traverse through all u positions, assuming that it receives the light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein, alpha is a coefficient representing a threshold value, the ith position of the PD and the area on the jth LED connecting line are removed from the whole space;
after the traversal is complete, the space remaining in the indoor environment is taken as a three-dimensional estimate of the obstruction.
Further, in step S1, the three-dimensional coordinates of the n LEDs are represented by a three-dimensional column vector pLED1To pLEDnRepresents; when the obstacle is modeled as a single cuboid, it is characterized by 6 parameters, including:
in the formula, PobstacleThe representation is a spatial range matrix of the obstacle, and each element in the matrix is represented as the minimum and maximum coordinate values of the cuboid on the three axes x, y and z in the global coordinate system of the indoor scene.
Further, in the step S2, the PD is mounted on a moving object or a human body, and a u × 3 dimensional matrix P is adoptedPDThree-dimensional coordinates representing all u positions of PD, using a u x n dimensional matrix
The element in the ith row and the jth column of the PD representing the actual received optical signal intensity
Representative is the intensity of the optical signal from the jth LED received by the PD at the ith location.
Further, the radiation pattern of the n LEDs satisfies the lambert model, and the expression is:
in the formula, s is the theoretical light signal intensity received by PD, stThe transmission power of the LED, m is the Lambert coefficient of the LED, A is the effective receiving area of the PD, d is the distance from the LED to the PD, and theta and phi are respectively included angles between the optical link and the normal direction of the LED and the normal direction of the PD.
Further, the PD processes the emission signals of different LEDs in a frequency division multiplexing manner.
Further, the method of dividing the indoor scene into equal-sized cubes to quantify the area where the obstacle exists or does not exist specifically includes:
in the first algorithm, the intensity of the light signal received from the jth LED at the ith position is assumed to be
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein alpha is a coefficient representing a threshold, subdividing a connecting line between the ith position of the PD and the jth LED according to a fixed length, subdividing the connecting line into a plurality of nodes, judging which small cube of the whole space each node corresponds to after being subdivided, and then attributing the small cubes into an obstacle set detected by the jth LED;
in the second algorithm, the intensity of the light signal received from the jth LED at the ith position is assumed to be
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
And if alpha is a coefficient representing a threshold value, subdividing a connecting line between the ith position of the PD and the jth LED according to a fixed length, subdividing the connecting line into a plurality of nodes, judging which small cube of the whole space each node corresponds to after being subdivided, then removing the small cubes from the indoor scene, and enabling the rest small cubes to be classified in an obstacle set detected by the jth LED.
Further, in executing the algorithm one, an LED is disposed at each of four corners of the top surface of the indoor scene, and then the PD is made to traverse the bottom surface of the indoor scene and measure the intensity of the light signal of the LED.
Further, when executing the second algorithm, the method specifically includes:
firstly, arranging LEDs at four corners of the top surface and four corners of the bottom surface of the indoor scene;
then, the PD is made to move on the bottom surface of the indoor scene and the light signal intensity of the four LEDs on the top surface is measured;
and finally, enabling the PD to move close to a certain side face of the indoor scene, simultaneously measuring the light signal intensity of the four LEDs opposite to the side face, and sequentially measuring the light signal intensity of the LEDs on the four side faces of the indoor scene according to a clockwise or anticlockwise sequence.
The invention has the beneficial effects that:
the invention has lower realization cost and only needs to arrange a small number of LEDs and PDs. Meanwhile, whether the barrier exists on the current link is judged only according to the optical signal received by the PD, and the judgment is realized without depending on a complex algorithm.
Drawings
Fig. 1 is a schematic view of a scene for implementing an indoor environment modeling method based on visible light communication and positioning provided in example 1;
FIG. 2 is a schematic diagram of the Lambertian model provided in example 1;
fig. 3 is a schematic diagram of the result of detecting the obstacle 1 when the algorithm one is executed, provided in embodiment 1, wherein fig. 3a is a schematic diagram of detecting the obstacle 1 by using a single LED, fig. 3b is a detection result as a whole, and fig. 3c is a top view of fig. 3 b;
fig. 4 is a schematic diagram of the result of detecting the obstacle 2 when the algorithm one is executed, provided in embodiment 1, wherein fig. 4a is a schematic diagram of detecting the obstacle 2 by a single LED, fig. 4b is a detection result as a whole, and fig. 4c is a top view of fig. 4 b;
fig. 5 is a schematic diagram of the results of measuring the obstacle 1 and the obstacle 2 when executing the second algorithm provided in embodiment 1, where fig. 5a is the overall detection result, fig. 5b is the front view of fig. 5a, fig. 5c is the left view of fig. 5a, and fig. 5d is the top view of fig. 5 a.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1 to 5, the present embodiment provides a method for modeling an indoor environment based on visible light communication and positioning, which specifically includes the following steps:
in a global coordinate system of an indoor scene, the coordinate minimum value and the coordinate maximum value of the cuboid on the three axes of x, y and z are expressed as follows:
in the formula, PobstacleA spatial range matrix of the obstacle is represented.
Specifically, in this embodiment, the radiation pattern of the LED should satisfy the lambert model, and the expression is:
in the formula, s is the optical signal intensity theoretically received by the PD; stIs the transmitted power of the LED; m is the Lambor coefficient of the LED; a is the effective receiving area of the PD; d is the distance of the LED from the PD; theta and phi are the normal direction and P of the optical link and the LED respectivelyD angle of normal. The value of the Lambor coefficient m is close to 1, so that the LED can obtain a larger light beam coverage range.
Specifically, in the present embodiment, the emission signals of different LEDs are processed in a frequency division multiplexing (FDD) manner, so that the PD can distinguish the optical signals of different LEDs.
And 2, enabling the PD to measure the light signal intensity of the LED at a plurality of different positions. Assuming that PDs are placed at a total of u different positions, we then use a u x 3 dimensional matrix PPDThree-dimensional coordinates representing all u positions of PD, using a u x n dimensional matrix
The element representing the actual received optical signal intensity of PD at ith row and jth column
Representative is the intensity of the optical signal from the jth LED received by the PD at the ith location.
And 3, executing the algorithm 1 or the algorithm 2 to obtain the range estimation of the indoor obstacle.
The first algorithm is as follows:
let the PD traverse through all u positions, assuming that it receives the light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Where α is a coefficient representing a threshold, specifically, α may be 0.5, or another coefficient, and is not limited in this embodiment; the area on the ith position of the PD and the jth LED connection is marked as the barrier area detected by the jth LED.
Finally, after the traversal is completed, intersection is carried out on the obstacle areas detected by each LED to serve as range estimation of the indoor obstacle.
And (3) algorithm II:
the idea of the algorithm is opposite to that of the first algorithm. The PD is still traversed at all u positions, assuming that it receives a light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Where α is a coefficient representing a threshold value, then it represents that no obstacle exists on this optical link, and the i-th position of PD and the area on the j-th LED connection are removed from the entire space. Finally, after the traversal is completed, we take the space remaining in the indoor environment as a three-dimensional estimate of the obstacle.
Specifically, in the present embodiment, a specific region in the indoor space is marked, and then in algorithm one and algorithm two, the region where the obstacle is present or not is quantified by dividing the entire indoor space into equally large small cubes.
More specifically, for algorithm one, if the PD detects that the intensity of the optical signal of the jth LED at the ith position is significantly smaller than the calculated value of the lambert model, then there is an obstacle existing on the connection line between the ith position of the PD and the jth LED. Subdividing the connecting line according to a fixed length, judging which small cube of the whole space each node after being subdivided corresponds to, and then classifying the small cubes into the set of obstacles detected by the jth LED. Similar operations are also used for algorithm two.
Specifically, in the embodiment, the algorithm is more suitable for estimating the position range of the obstacle projected on the ground, and is not suitable for estimating the height of the obstacle, that is, the two-dimensional estimation of the obstacle can be completed. Algorithm two can achieve three-bit estimation of the obstacle, but requires more measurement data samples.
Specifically, in the present embodiment, the implementation of algorithm one and algorithm two requires the PD to measure the light signal intensity of the LED at different positions, so the present embodiment refers to 6 sides of the indoor environment as "upper side", "lower side", "front side", "rear side", "left side", and "right side".
In algorithm one, an LED is placed on each of the top 4 corners, and then the PD is traversed below and the intensity of the LED light signal is measured.
In the second algorithm, one LED is placed on each of 8 top corners of the indoor environment, and then the PD is moved below and measures the light signal of the LED above, moved to the left and measures the light signal of the LED to the right, moved to the right and measures the light signal from the LED to the left, moved in front and measures the light signal of the LED behind, and moved behind and measures the light signal of the LED in front, thus measuring 4 sets of data more than one compared to the algorithm.
To illustrate the modeling method provided in this implementation more clearly, as shown in fig. 1, this implementation constructs an indoor scene with length l, width w, and height h, and assumes that several LEDs for VLC and VLP are placed on top of this scene. At the bottom of the indoor scene, a PD is provided, which can be moved freely, and can be carried by a pedestrian or other moving object, for receiving the light signal emitted by the LED.
For the sake of convenience of simulation analysis, the obstacle is modeled as a rectangular parallelepiped (or a combination of a plurality of rectangular cuboids, for example, a single rectangular parallelepiped in fig. 1) as shown in fig. 1, and such a rectangular parallelepiped can be described by 6 parameters: in the global coordinate system of the indoor scene, the coordinate minimum and maximum values of the cuboid on the three axes x, y and z, i.e. the minimum and maximum values
Wherein P isobstacleRepresenting a spatial range matrix of the obstacle; x is the number ofmin、xmax、ymin、ymax、zminAnd zmaxThese 6 parameters are shown in figure 1.
In order to determine the position of the obstacle, the PD needs to measure the intensity of the light signals of all the LEDs at many different positions, and when the light signal from a certain LED is blocked by the obstacle, the PD cannot receive the light signal from the LED. By using this property of light traveling in a straight line, the position of an obstacle in the indoor environment can be detected.
As shown in FIG. 2, the LED on the ceiling irradiates its optical signal onto the PD, the angle between the optical link and the normal direction of the LED and the angle between the optical link and the normal direction of the PD are theta and phi respectively, the link length is d, and thus the PD receives the optical signal from the LED with the intensity of the optical signal from the LED
Where s is the theoretical PD received optical signal strength; stIs the transmitted power of the LED; m is the Lambor coefficient of the LED; a is the effective receiving area of the PD. Assuming that the arrangement of the LED and PD is as shown in fig. 1, the normal directions of both LED and PD are parallel to the z-axis, where θ is equal to Φ, and this angle can be calculated from the three-dimensional coordinates of LED and PD.
The theoretical calculation result of the lambert model can help to judge whether the received signal fading is caused by path attenuation or shadow fading.
Fig. 3 shows the result of the algorithm one detecting an obstacle 1. Assuming that the indoor environment is a 4m × 4m × 4m cube, the obstacle 1 is located at the center of the environment, and the related parameters are described as
The unit is m. It can be seen that since the obstacle is located at the center of the room at this time, the LED can completely detect the area where the obstacle is located. Even so, the algorithm one cannot accurately estimate the height of the obstacle, but can relatively accurately estimate the projection of the obstacle on the ground.
Fig. 4 shows the result of the algorithm one detecting an obstacle 2. The obstacle 2 is closer to the corner of the indoor environment than the obstacle 1 and the height increases to 2m, the relevant parameter being described as
The unit is m. It can be seen that, at this time, the LED cannot completely detect the area where the obstacle is located, and the area of the obstacle is significantly higher than the detection range of the LED. Even so, the algorithm one can estimate the projection of the obstacle on the ground relatively accurately. The algorithm one can thus be seen as a two-dimensional estimation of the obstacle.
Fig. 5 shows the result of the algorithm for detecting both obstacle 1 and obstacle 2. It can be seen that the second algorithm can detect the three-dimensional spatial range of the obstacle more accurately, including the height information that the first algorithm cannot detect.
The invention is not described in detail, but is well known to those skilled in the art.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (8)
1. An indoor environment modeling method based on visible light communication and positioning is characterized by comprising the following steps:
step S1, aiming at an indoor scene, arranging n LEDs on the top of the indoor scene, wherein the n LEDs are used for emitting optical signals, and an obstacle is also arranged in the indoor scene and is modeled as a single cuboid or a combination of a plurality of cuboids;
step S2, for the indoor scene, setting a PD at the bottom thereof, the PD being used for measuring the light signal intensity of the LED and having in-plane moving capability, the PD measuring the corresponding light signal intensity by moving u positions in the indoor scene;
step S3, executing algorithm one or executing algorithm two to obtain the estimated range of the obstacle, wherein,
the first algorithm comprises the following steps:
let the PD traverse through all u positions, assuming that it receives the light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein, alpha is a coefficient representing a threshold value, an area on the ith position of the PD and the jth LED connecting line is marked as an obstacle area detected by the jth LED;
after traversing is completed, intersecting the obstacle regions detected by each LED to obtain the range estimation of the obstacle;
the second algorithm comprises the following steps:
let the PD traverse through all u positions, assuming that it receives the light signal from the jth LED at the ith position with an intensity of
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein, alpha is a coefficient representing a threshold value, the ith position of the PD and the area on the jth LED connecting line are removed from the whole space;
after the traversal is complete, the space remaining in the indoor environment is taken as a three-dimensional estimate of the obstruction.
2. The method for modeling indoor environment based on visible light communication and positioning as claimed in claim 1, wherein in said step S1, said n LEDs have three-dimensional coordinates represented by a three-dimensional column vector pLED1To pLEDnRepresents; when the obstacle is modeled as a single cuboid, its features are characterized by 6 parametersA number, including:
in the formula, PobstacleThe representation is a spatial range matrix of the obstacle, and each element in the matrix is represented as the minimum and maximum coordinate values of the cuboid on the three axes x, y and z in the global coordinate system of the indoor scene.
3. The method for modeling indoor environment based on visible light communication and positioning as claimed in claim 2, wherein in step S2, the PD is mounted on a moving object or a human body and adopts a u x 3 dimensional matrix PPDThree-dimensional coordinates representing all u positions of PD, using a u x n dimensional matrix
The element in the ith row and the jth column of the PD representing the actual received optical signal intensity
Representative is the intensity of the optical signal from the jth LED received by the PD at the ith location.
4. The modeling method of indoor environment based on visible light communication and positioning as claimed in claim 3, wherein the radiation pattern of the n LEDs satisfies the Lambert model, and the expression is:
in the formula, s is the theoretical light signal intensity received by PD, stIs the transmission power of the LED, m is the Lambert coefficient of the LED, A is the effective receiving area of the PD, d is the distance from the LED to the PD, and theta and phi are the clips of the optical link with the normal direction of the LED and the normal direction of the PD respectivelyAnd (4) an angle.
5. The method as claimed in claim 4, wherein the PD processes the emission signals of different LEDs in a frequency division multiplexing manner.
6. The indoor environment modeling method based on visible light communication and positioning as claimed in claim 5, wherein the quantifying the area with or without obstacles by dividing the indoor scene into equal-sized cubes comprises:
in the first algorithm, the intensity of the light signal received from the jth LED at the ith position is assumed to be
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein alpha is a coefficient representing a threshold, subdividing a connecting line between the ith position of the PD and the jth LED according to a fixed length, subdividing the connecting line into a plurality of nodes, judging which small cube of the whole space each node corresponds to after being subdivided, and then attributing the small cubes into an obstacle set detected by the jth LED;
in the second algorithm, the intensity of the light signal received from the jth LED at the ith position is assumed to be
And the corresponding theoretical calculation value of the Lambor model is sijIf, if
Wherein alpha is a coefficient representing a threshold value, subdividing the i-th position of the PD and the connecting line of the j-th LED according to a fixed length, subdividing the connecting line into a plurality of nodes, and judging each subdivided nodeAnd (4) corresponding to which small cube of the whole space, and then removing the small cubes from the indoor scene, wherein the rest small cubes are classified in the set of obstacles detected by the jth LED.
7. The method of claim 6, wherein in executing the algorithm, an LED is disposed at each of four corners of the top surface of the indoor scene, and then the PD is made to traverse the bottom surface of the indoor scene and measure the light signal intensity of the LED.
8. The indoor environment modeling method based on visible light communication and positioning as claimed in claim 6, wherein when executing the second algorithm, specifically comprising:
firstly, arranging LEDs at four corners of the top surface and four corners of the bottom surface of the indoor scene;
then, the PD is made to move on the bottom surface of the indoor scene and the light signal intensity of the four LEDs on the top surface is measured;
and finally, enabling the PD to move close to a certain side face of the indoor scene, simultaneously measuring the light signal intensity of the four LEDs opposite to the side face, and sequentially measuring the light signal intensity of the LEDs on the four side faces of the indoor scene according to a clockwise or anticlockwise sequence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210027519.3A CN114362823B (en) | 2022-01-11 | 2022-01-11 | Indoor environment modeling method based on visible light communication and positioning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210027519.3A CN114362823B (en) | 2022-01-11 | 2022-01-11 | Indoor environment modeling method based on visible light communication and positioning |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114362823A true CN114362823A (en) | 2022-04-15 |
| CN114362823B CN114362823B (en) | 2024-02-20 |
Family
ID=81109354
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210027519.3A Active CN114362823B (en) | 2022-01-11 | 2022-01-11 | Indoor environment modeling method based on visible light communication and positioning |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114362823B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170276767A1 (en) * | 2014-12-10 | 2017-09-28 | University Of South Australia | Visible Light Based Indoor Positioning System |
| CN107994940A (en) * | 2017-11-17 | 2018-05-04 | 华南理工大学 | A kind of visible ray localization method based on TABU search |
| CN109188360A (en) * | 2018-09-21 | 2019-01-11 | 西安电子科技大学 | A kind of indoor visible light 3-D positioning method based on bat algorithm |
| CN109302234A (en) * | 2018-10-26 | 2019-02-01 | 西安电子科技大学 | A kind of calculation method of indoor visible light communication system Complex Channel impulse response |
-
2022
- 2022-01-11 CN CN202210027519.3A patent/CN114362823B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170276767A1 (en) * | 2014-12-10 | 2017-09-28 | University Of South Australia | Visible Light Based Indoor Positioning System |
| CN107994940A (en) * | 2017-11-17 | 2018-05-04 | 华南理工大学 | A kind of visible ray localization method based on TABU search |
| CN109188360A (en) * | 2018-09-21 | 2019-01-11 | 西安电子科技大学 | A kind of indoor visible light 3-D positioning method based on bat algorithm |
| CN109302234A (en) * | 2018-10-26 | 2019-02-01 | 西安电子科技大学 | A kind of calculation method of indoor visible light communication system Complex Channel impulse response |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114362823B (en) | 2024-02-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US10887029B2 (en) | Method and apparatus for analysing communication channel in consideration of material and contours of objects | |
| CN109891781B (en) | Method and apparatus for analyzing communication channel in consideration of material and contour of object | |
| US10820213B2 (en) | Method and apparatus for analyzing communication environment based on property information of an object | |
| Yang et al. | Counting people in crowds with a real-time network of simple image sensors | |
| CN105549011B (en) | A kind of unilateral 2 points of strabismus imaging method of building based on MIMO through-wall radars | |
| KR20190038117A (en) | Method and apparatus for analyzing communication environment | |
| WO2023051774A1 (en) | Monitoring method and system, apparatus, and computer program product | |
| CN113064117A (en) | Deep learning-based radiation source positioning method and device | |
| CN108413966A (en) | Localization method based on a variety of sensing ranging technology indoor locating systems | |
| JP2019083027A (en) | Method and apparatus for determining detection range of traffic route | |
| CN101877820A (en) | Tracking method of multiple equipment-free object by wireless network on basis of dynamic clustering algorithm | |
| CN114362823A (en) | Indoor environment modeling method based on visible light communication and positioning | |
| Wang et al. | Indoor localization service based on the data fusion of Wi-Fi and RFID | |
| CN113093162A (en) | Personnel trajectory tracking system based on AIOT and video linkage | |
| CN107801168A (en) | A kind of localization method of the adaptive passive type target in outdoor | |
| Ivšić et al. | Analysis of safe ultrawideband human-robot communication in automated collaborative warehouse | |
| CN108761391B (en) | Model type equipment-free target positioning method | |
| Zhang et al. | Precise visual positioning of agricultural mobile robots with a fiducial marker reprojection approach | |
| CN101720057A (en) | Single non-equipment object tracking method based on optimal covering algorithm of wireless network | |
| CN111537949B (en) | Road vehicle three-dimensional passive positioning method and system based on WSN | |
| Xie et al. | Sensor placement in the ultrasonic positioning system | |
| Hsiao et al. | Two practical considerations of beacon deployment for ultrasound-based indoor localization systems | |
| Rampa et al. | Physical model-based calibration for device-free radio localization and motion tracking | |
| Ferranti et al. | Practical issues in deploying mobile agents to explore a sensor-instrumented environment | |
| Karimipour et al. | Spatial coverage estimation and optimization in geosensor networks deployment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |