CN114143872A - Multi-mobile-device positioning method based on unmanned aerial vehicle-mounted WiFi probe - Google Patents
Multi-mobile-device positioning method based on unmanned aerial vehicle-mounted WiFi probe Download PDFInfo
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Abstract
The invention relates to a multi-mobile-device positioning method based on an unmanned aerial vehicle-mounted WiFi probe, which comprises the following steps: s1, flying the multiple unmanned aerial vehicles in the target area according to the set path and roughly scanning the mobile equipment; s2, initializing a list of the mobile equipment to be positioned according to the rough scanning result and acquiring the position coordinates of the mobile equipment to be positioned; s3, carrying out region division on the target region; s4, calculating the positions and the densities of the divided areas; s5, calculating the optimal path of the unmanned aerial vehicle from the current point to traverse each divided area; s6, sequentially traversing each divided area by an array formed by a plurality of unmanned aerial vehicles according to an optimal path, accurately scanning the mobile equipment in the divided area by the unmanned aerial vehicle array, determining the position coordinates of the mobile equipment and updating a list of the mobile equipment to be positioned; s7, if the mobile equipment to be positioned still exists, repeating the steps S4-S6, otherwise, completing the positioning. Compared with the prior art, the invention has the advantages of high positioning speed, high precision and flexible positioning area.
Description
Technical Field
The invention relates to the technical field of mobile equipment positioning, in particular to a multi-mobile-equipment positioning method based on an unmanned aerial vehicle-mounted WiFi probe.
Background
In recent years, mobile devices such as smartphones have become one of the essential items for most people. These mobile devices tend to have WiFi capability and most people will choose to turn this on by default. In order to detect WiFi access points existing around, a mobile device turning on WiFi continuously sends Probe Request frames to the surroundings, which include information such as sending time, device MAC address, signal strength, etc. The Wi-Fi Probe can sense nearby WiFi devices by capturing Probe Request frames, and the approximate distance between the Wi-Fi device and the Probe can be obtained through signal strength (RSSI) conversion.
WiFi probe technology is commonly used for traffic statistics and target location. For example, publication CN112584491A discloses a WiFi probe-based object positioning system for locating an object on a floor within a building. The publication CN111866725A discloses a people stream detection method based on WiFi probe technology, which is used for monitoring people stream and track. However, current WiFi probe technology is mostly based on fixed locations, which must be deployed in the field in advance for use; meanwhile, in a disaster scenario, when a WiFi probe deployed in advance is damaged or network is interrupted, it cannot function.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a multi-mobile-device positioning method based on an unmanned airborne WiFi probe.
The purpose of the invention can be realized by the following technical scheme:
a multi-mobile-device positioning method based on an unmanned airborne WiFi probe comprises the following steps:
s1, the multiple unmanned aerial vehicles respectively fly according to the set paths in the target area, and the airborne WiFi probes are utilized to roughly scan the mobile equipment in the environment in the flying process;
s2, initializing a list of the mobile equipment to be positioned according to the rough scanning result, and preliminarily acquiring the position coordinates of the mobile equipment to be positioned;
s3, carrying out region division on the target region;
s4, calculating the position of each divided area and the density of the mobile equipment to be positioned currently;
s5, calculating the optimal path of each divided area containing the mobile equipment to be positioned from the current point of the unmanned aerial vehicle;
s6, enabling the array formed by the multiple unmanned aerial vehicles to fly to a first divided area according to the planned optimal path, accurately scanning the mobile equipment in the divided area by the unmanned aerial vehicle array in the divided area, determining the position coordinates of the mobile equipment and updating a list of the mobile equipment to be positioned;
s7, judging whether the mobile equipment to be positioned still exists, if so, repeating the steps S4-S6, and if not, finishing positioning.
Preferably, step S2 specifically includes:
s21, counting data packets from the same mobile equipment;
s22, converting the signal intensity into distance for all the data packets;
s23, calculating the position of each mobile device by utilizing the triangulation positioning principle;
s24, adding the devices to the list of mobile devices to be located.
Preferably, the position of the divided area in step S4 is an average position of all mobile devices to be located in the divided area.
Preferably, the density of the mobile devices to be positioned in the divided area in step S4 is the number of the mobile devices to be positioned in the divided area.
Preferably, step S5 is specifically:
s51, calculating the distance between the divided regions:
wherein D isi,jIs the distance, X, between the ith and jth division regionsi、YiIs the position coordinate of the ith division area, Xj、YjFor the position coordinate of the j-th division area, NjThe density of the mobile devices currently to be positioned in the jth divided region, MAX (N) is the maximum value of the densities of the mobile devices currently to be positioned in all the divided regions, N is the total number of the divided regions, WjAnd HjIs the width and height of the jth dividing region, and alpha is a density influence coefficient;
and S52, solving the optimal path by utilizing a heuristic algorithm based on the position information of the current point of the unmanned aerial vehicle and the distance between the divided regions.
Preferably, the heuristic algorithm for solving the optimal path in step S52 includes a simulated annealing algorithm, a genetic algorithm, and an ant colony algorithm.
Preferably, when the drone array precisely scans the mobile devices in the divided area in step S6, the plurality of drones scan around the divided area with the calculated position of the currently precisely scanned divided area as a center point.
Preferably, the determining the location coordinates of the mobile device and updating the list of mobile devices to be located in step S6 specifically includes:
s61, calculating the position coordinates of each mobile device in the currently scanned divided area;
s62, for the mobile devices in the divided area, if they are located in the same divided area in M consecutive scans, determining their precise coordinates, marking as the located mobile device and deleting from the list of mobile devices currently to be located, and if a new mobile device appears, adding it to the list of mobile devices to be located.
Preferably, three unmanned aerial vehicles are used for positioning, and in the rough scanning stage, the three unmanned aerial vehicles fly in the target area according to S-shaped routes respectively.
Preferably, in the fine scanning phase, three drones scan each of the divided areas while maintaining the formation of an equilateral triangle.
Compared with the prior art, the invention has the following advantages:
(1) the invention creatively provides a method for positioning mobile equipment by using an unmanned aerial vehicle-mounted WiFi probe, wherein a plurality of unmanned aerial vehicles fly to target points and respectively collect and mutually exchange WiFi data to position the mobile equipment, and output information such as equipment positions, density maps and the like.
(2) The invention creatively provides an unmanned aerial vehicle flight track scheme for positioning mobile equipment, so that the unmanned aerial vehicle preferentially finishes scanning of an area with high crowd density under the condition of ensuring that the total scanning time is relatively short, and the accurate and continuous positioning of the equipment can be realized.
Drawings
Fig. 1 is a flow chart of a multi-mobile-device positioning method based on an unmanned airborne WiFi probe according to the present invention.
Fig. 2 is a schematic diagram of an S-shaped flight path of the unmanned aerial vehicle.
Fig. 3 is a schematic diagram of the unmanned aerial vehicle scanning a partitioned area.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. Note that the following description of the embodiments is merely a substantial example, and the present invention is not intended to be limited to the application or the use thereof, and is not limited to the following embodiments.
Examples
As shown in fig. 1, the present embodiment provides a method for positioning multiple mobile devices based on an unmanned airborne WiFi probe, the method including:
s1, the multiple unmanned aerial vehicles respectively fly according to the set paths in the target area, and the airborne WiFi probes are utilized to roughly scan the mobile equipment in the environment in the flying process;
s2, initializing a list of the mobile equipment to be positioned according to the rough scanning result, and preliminarily acquiring the position coordinates of the mobile equipment to be positioned;
s3, carrying out region division on the target region;
s4, calculating the position of each divided area and the density of the mobile equipment to be positioned currently;
s5, calculating the optimal path of each divided area containing the mobile equipment to be positioned from the current point of the unmanned aerial vehicle;
s6, enabling an array formed by a plurality of unmanned aerial vehicles to fly to a first divided area according to a planned optimal path, wherein in the divided area, the unmanned aerial vehicle array accurately scans mobile equipment in the divided area, determines the position coordinates of the mobile equipment and updates a list of the mobile equipment to be positioned, and when the unmanned aerial vehicle array accurately scans the mobile equipment in the divided area, the plurality of unmanned aerial vehicles take the calculation position of the currently accurately scanned divided area as a central point and scan around the divided area;
s7, judging whether the mobile equipment to be positioned still exists, if so, repeating the steps S4-S6, and if not, finishing positioning.
In this embodiment, preferably, three unmanned aerial vehicles are used for positioning, and in the rough scanning stage, the three unmanned aerial vehicles fly in the target area according to the S-shaped route respectively. In the accurate scanning stage, three unmanned aerial vehicles keep the formation of an equilateral triangle to scan each divided region. In other preferred embodiments, more than three unmanned aerial vehicles can be used for positioning and scanning, and the following scanning modes can be satisfied: in the rough scanning stage, the scanning ranges of the multiple unmanned aerial vehicles can cover the whole target area, so that all the mobile devices can be scanned; in the accurate scanning stage, a plurality of unmanned aerial vehicles surround the division area to be scanned, and if four unmanned aerial vehicles are adopted, an annular array is formed.
The specific implementation scheme of the invention is specifically described below by using three unmanned aerial vehicles for positioning:
the S1 has the beneficial effects that the flying according to the S-shaped route is as follows: the unmanned aerial vehicle can fly linearly as much as possible, and the scanning speed is increased; different drones complete the scanning of the whole area with the shortest total path length. Fig. 2 is a schematic diagram of the flight path of the unmanned aerial vehicle in step S1, which includes the following steps:
s11: and calculating S-shaped flight path track points of each unmanned aerial vehicle according to the size of the target area. Distance between two unmanned aerial vehicle's parallel scanning orbit can not exceed 2R, and wherein R is the furthest detection distance of wiFi probe to guarantee that all wiFi equipment can both be detected in the region, distance between this embodiment unmanned aerial vehicle's the parallel scanning orbit is got
S12: three unmanned aerial vehicles fly according to track point precalculated respectively, utilize the data package that machine carries wiFi probe sent to the wiFi equipment in the environment to scan and extract partial information storage in the flight process. The storage format is (time, device MAC address, signal strength RSSI, drone number, drone location).
S13: the two slave unmanned aerial vehicles send the acquired data to the master unmanned aerial vehicle.
Step S2 includes the following steps:
s21: data packets from the same mobile device (with the same MAC address) are counted.
S22: for all packets, the signal strength (RSSI) is converted to distance. Conversion formula is
Wherein P isdFor the received signal strength of the WiFi probe at distance d, Pd0Is the reference signal strength of the device at a distance of 1m and n is the path attenuation exponent.
S23: the position of each device is calculated using triangulation principles. Position (X, Y) of the device
(X-xi)2+(Y-yi)2＝di 2，
Wherein (x)i,yi) To WiFi probe position, diIs the distance of the WiFi probe from the device. Each packet of the device satisfies the relationship, and the position (X, Y) of the device is found using the least squares method.
S24: these devices are added to the list of devices to be located.
Further, in step S3, the divided region is an arbitrary region designated by a person. The beneficial effect of introducing the divided areas is that the method better meets the actual requirements of post-disaster search and rescue. The specific location of a mobile device is not so important (there may be situations such as people walking; positioning errors always exist, and if the accuracy is not an order of magnitude improvement, there is no practical significance to the rescuers), the rescuers are concerned about whether trapped people exist in a specific area (such as a building, a forest) or not, and the number of people is large. Through multiple measurements, the correctness of the divided area where the equipment is located is ensured, and the position error of the equipment inside the divided area is tolerated.
According to different requirements, the divided regions can be regions with any shapes, such as rectangles, circles and irregular figures, which are specified in advance, and only the condition that the set of all the regions is the division of the target region is required to be met. Preferably, all the divided regions are represented by rectangles in the present method. The rectangle can greatly reduce the space (represented by a quadruple) required by storing the region, and the complexity of the algorithm is reduced. The divided regions may be rectangles of different sizes, or may be rectangles formed by equally dividing the target region without special requirements.
The position of the divided area in step S4 is the average position of all mobile devices to be located in the divided area. The density of mobile devices to be located within a partitioned area is the number of mobile devices to be located within the partitioned area.
Step S5 includes the following steps:
s51: the distance between the divided regions is calculated. Is calculated by the formula
Wherein D isi,jIs the distance, X, between the ith and jth division regionsi、YiIs the position coordinate of the ith division area, Xj、YjFor the position coordinate of the j-th division area, NjThe density of the mobile devices currently to be positioned in the jth divided region, MAX (N) is the maximum value of the densities of the mobile devices currently to be positioned in all the divided regions, N is the total number of the divided regions, WjAnd HjIs the width and height of the jth divisional area, and α is the density influence coefficient. The beneficial effect of using this kind of calculation mode is, under the same circumstances of distance, this kind of calculation mode can make unmanned aerial vehicle priority fly to the region that equipment density is bigger and search, accomplish as much equipment location as possible in as short time as possible, strive for the time for disaster monitoring and rescue.
S52: and solving the optimal path problem as a traveling salesman problem. Since the problem is an NP-hard problem and it is difficult to find an accurate answer within a polynomial time, a heuristic algorithm (such as a simulated annealing algorithm, a genetic algorithm, an ant colony algorithm) is used to find a relatively good answer within a specified time. For example, the solution is performed by using a simulated annealing algorithm in the present embodiment.
Step S6 specifically includes: and the three airplanes keep the formation of the equilateral triangle flying to the position point of the first area in the optimal path, and carry out data acquisition through the airborne WiFi probe. Determining the position coordinates of the mobile device and updating a list of mobile devices to be positioned, specifically comprising:
s61, calculating the position coordinates of each mobile device in the currently scanned divided area;
s62, for the mobile devices in the divided area, if they are located in the same divided area in M consecutive scans, determining their precise coordinates, marking as the located mobile device and deleting from the list of mobile devices currently to be located, and if a new mobile device appears, adding it to the list of mobile devices to be located.
By maintaining the list of the devices to be positioned, on one hand, each device is ensured to be scanned for multiple times before the position is determined, and the position is converged; on the other hand, the position of the equipment can be dynamically updated along with time, and continuous monitoring is realized.
The invention provides a method for carrying out multi-target positioning by utilizing an unmanned aerial vehicle-mounted WiFi probe. Compared with a common WiFi probe positioning scheme with fixed points, the method and the system can be used for positioning the mobile equipment carried by personnel in a specific area when a city is in a disaster or an emergency, are not required to be limited by the deployment condition of the WiFi probe with the fixed points, and provide area crowd density data support for pre-disaster monitoring and post-disaster rescue. In particular, the method gives the mobile device position from coarse to fine using two scans. The division area concept is introduced, and the positioning error is kept within an acceptable range while the actual requirement is considered. The optimal path planning problem is solved by utilizing a heuristic algorithm, and a distance calculation formula is innovatively provided, so that the unmanned aerial vehicle can scan the area with high crowd density as soon as possible under the condition that the total time is as short as possible, and the scanning result of the important area is given as soon as possible. And a list of equipment to be positioned is introduced, and more accurate and continuous dynamic positioning is realized by maintaining the list.
The above embodiments are merely examples and do not limit the scope of the present invention. These embodiments may be implemented in other various manners, and various omissions, substitutions, and changes may be made without departing from the technical spirit of the present invention.
Claims (10)
1. A multi-mobile-device positioning method based on an unmanned airborne WiFi probe is characterized by comprising the following steps:
s1, the multiple unmanned aerial vehicles respectively fly according to the set paths in the target area, and the airborne WiFi probes are utilized to roughly scan the mobile equipment in the environment in the flying process;
s2, initializing a list of the mobile equipment to be positioned according to the rough scanning result, and preliminarily acquiring the position coordinates of the mobile equipment to be positioned;
s3, carrying out region division on the target region;
s4, calculating the position of each divided area and the density of the mobile equipment to be positioned currently;
s5, calculating the optimal path of each divided area containing the mobile equipment to be positioned from the current point of the unmanned aerial vehicle;
s6, enabling the array formed by the multiple unmanned aerial vehicles to fly to a first divided area according to the planned optimal path, accurately scanning the mobile equipment in the divided area by the unmanned aerial vehicle array in the divided area, determining the position coordinates of the mobile equipment and updating a list of the mobile equipment to be positioned;
s7, judging whether the mobile equipment to be positioned still exists, if so, repeating the steps S4-S6, and if not, finishing positioning.
2. The method as claimed in claim 1, wherein the step S2 specifically includes:
s21, counting data packets from the same mobile equipment;
s22, converting the signal intensity into distance for all the data packets;
s23, calculating the position of each mobile device by utilizing the triangulation positioning principle;
s24, adding the devices to the list of mobile devices to be located.
3. The method as claimed in claim 1, wherein the position of the divided region in step S4 is an average position of all the mobile devices to be located in the divided region.
4. The method of claim 1, wherein the density of the mobile devices to be located in the partitioned area in step S4 is equal to the number of the mobile devices to be located in the partitioned area.
5. The method as claimed in claim 1, wherein the step S5 is specifically as follows:
s51, calculating the distance between the divided regions:
wherein D isi,jIs the distance, X, between the ith and jth division regionsi、YiIs the position coordinate of the ith division area, Xj、YjFor the position coordinate of the j-th division area, NjThe density of the mobile devices currently to be positioned in the jth divided region, MAX (N) is the maximum value of the densities of the mobile devices currently to be positioned in all the divided regions, N is the total number of the divided regions, WjAnd HjIs the width and height of the jth dividing region, and alpha is a density influence coefficient;
and S52, solving the optimal path by utilizing a heuristic algorithm based on the position information of the current point of the unmanned aerial vehicle and the distance between the divided regions.
6. The method as claimed in claim 5, wherein the heuristic algorithm for solving the optimal path in step S52 comprises simulated annealing algorithm, genetic algorithm, and ant colony algorithm.
7. The method as claimed in claim 1, wherein the plurality of drones scan around the precisely scanned divided area with the calculated position of the currently scanned divided area as the center point when the array of drones precisely scans the mobile devices in the divided area in step S6.
8. The method as claimed in claim 1, wherein the step of determining the location coordinates of the mobile device and updating the list of mobile devices to be located in step S6 specifically comprises:
s61, calculating the position coordinates of each mobile device in the currently scanned divided area;
s62, for the mobile devices in the divided area, if they are located in the same divided area in M consecutive scans, determining their precise coordinates, marking as the located mobile device and deleting from the list of mobile devices currently to be located, and if a new mobile device appears, adding it to the list of mobile devices to be located.
9. The method as claimed in claim 1, wherein three drones are used for positioning, and during the rough scanning stage, the three drones fly in the target area according to S-shaped routes respectively.
10. The method of claim 9, wherein three drones scan each divided area while maintaining the formation of equilateral triangle.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117336821A (en) * | 2023-10-17 | 2024-01-02 | 中国电子科技集团公司第十五研究所 | Multi-mode signal transmission method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104039009A (en) * | 2014-06-23 | 2014-09-10 | 西北大学 | Target wireless device positioning method based on regional division |
| CN108801266A (en) * | 2018-06-21 | 2018-11-13 | 河南大学 | A kind of path planning method that multiple no-manned plane searches for uncertain environment |
| CN109520504A (en) * | 2018-11-27 | 2019-03-26 | 北京航空航天大学 | A kind of unmanned plane inspection method for optimizing route based on grid discretization |
| CN109635661A (en) * | 2018-11-13 | 2019-04-16 | 同济大学 | A kind of far field wireless charging reception object detection method based on convolutional neural networks |
| CN109669173A (en) * | 2019-01-08 | 2019-04-23 | 南京航空航天大学 | A kind of ground target localization method relatively strong and weak based on unmanned plane and signal |
| US20200034646A1 (en) * | 2018-07-24 | 2020-01-30 | Exyn Technologies | Unmanned Aerial Localization and Orientation |
| US20200103552A1 (en) * | 2018-10-02 | 2020-04-02 | Robert S. Phelan | Unmanned aerial vehicle system and methods |
-
2021
- 2021-11-25 CN CN202111410024.0A patent/CN114143872B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104039009A (en) * | 2014-06-23 | 2014-09-10 | 西北大学 | Target wireless device positioning method based on regional division |
| CN108801266A (en) * | 2018-06-21 | 2018-11-13 | 河南大学 | A kind of path planning method that multiple no-manned plane searches for uncertain environment |
| US20200034646A1 (en) * | 2018-07-24 | 2020-01-30 | Exyn Technologies | Unmanned Aerial Localization and Orientation |
| US20200103552A1 (en) * | 2018-10-02 | 2020-04-02 | Robert S. Phelan | Unmanned aerial vehicle system and methods |
| CN109635661A (en) * | 2018-11-13 | 2019-04-16 | 同济大学 | A kind of far field wireless charging reception object detection method based on convolutional neural networks |
| CN109520504A (en) * | 2018-11-27 | 2019-03-26 | 北京航空航天大学 | A kind of unmanned plane inspection method for optimizing route based on grid discretization |
| CN109669173A (en) * | 2019-01-08 | 2019-04-23 | 南京航空航天大学 | A kind of ground target localization method relatively strong and weak based on unmanned plane and signal |
Non-Patent Citations (4)
| Title |
|---|
| HYUKSEONG KWON: "Robust mobile ground target localization using ground image features with UAV position compensation techniques", 《2012 12TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS》 * |
| 刘馨竹等: "无人机协同任务的优化方案", 《数学的实践与认识》 * |
| 赵玉等: "多无人机协同定位下的路径优化", 《信号处理》 * |
| 高勇等: "Damage localization under ambient vibration using changes in flexibility", 《EARTHQUAKE ENGINEERING AND ENGINEERING VIBRATION》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117336821A (en) * | 2023-10-17 | 2024-01-02 | 中国电子科技集团公司第十五研究所 | Multi-mode signal transmission method and device |
| CN117336821B (en) * | 2023-10-17 | 2024-04-09 | 中国电子科技集团公司第十五研究所 | Multi-mode signal transmission method and device |
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