CN115133977A - Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization - Google Patents
Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization Download PDFInfo
- Publication number
- CN115133977A CN115133977A CN202210730374.3A CN202210730374A CN115133977A CN 115133977 A CN115133977 A CN 115133977A CN 202210730374 A CN202210730374 A CN 202210730374A CN 115133977 A CN115133977 A CN 115133977A
- Authority
- CN
- China
- Prior art keywords
- unmanned aerial
- aerial vehicle
- minimization
- base station
- information age
- 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
- 230000008447 perception Effects 0.000 title claims abstract description 55
- 238000004891 communication Methods 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000005457 optimization Methods 0.000 title claims abstract description 17
- 230000005540 biological transmission Effects 0.000 claims description 7
- 239000000654 additive Substances 0.000 claims description 5
- 230000000996 additive effect Effects 0.000 claims description 5
- 230000010354 integration Effects 0.000 description 4
- 230000002452 interceptive effect Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002567 autonomic effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/933—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of aircraft or spacecraft
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
- H04W84/04—Large scale networks; Deep hierarchical networks
- H04W84/06—Airborne or Satellite Networks
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Signal Processing (AREA)
- Astronomy & Astrophysics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Electromagnetism (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses an unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization, which comprises the following steps: an unmanned aerial vehicle communication perception integrated system is constructed and system parameters are obtained, the system comprises a ground base station, an unmanned aerial vehicle and a user, the base station, the unmanned aerial vehicle and the user are all provided with single antennas, and the unmanned aerial vehicle is provided with a radar perception device; constructing an information age minimization expression according to an unmanned aerial vehicle communication perception integrated system model; acquiring an optimal position relational expression of the unmanned aerial vehicle through an information age minimization expression; and substituting the system parameters into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method. The invention reduces the information age of the data, ensures the freshness of the data and improves the accuracy of the decision of the base station.
Description
Technical Field
The invention relates to the technical field of mobile communication, in particular to a position optimization method of an unmanned aerial vehicle communication perception integrated system based on information age minimization.
Background
With the rapid development of the internet of things, a large number of real-time sensing applications, such as intelligent vehicles, disaster monitoring, precision agriculture and digital health monitoring, appear. In most sensing applications, the state information of interest will change rapidly over time. Therefore, it is necessary to report status information to the base station as soon as possible so that the base station can make an accurate decision. To measure the freshness of the information, it can be quantified by a new performance indicator, information age. The age of information is defined as the time elapsed since the generation of the most recent sensing packet sent to the destination. However, for some typical application scenarios, due to the occlusion of buildings, the line-of-sight communication link between the base station and the terminal is weak, the rate of transmitting data is low, and it is difficult to guarantee the requirement of information freshness. Therefore, in the past several years, the unmanned aerial vehicle is widely applied to the wireless communication system to help the real-time information updating system by virtue of its strong line-of-sight link, high mobility and high service coverage, which is a promising technology. In addition, when unmanned aerial vehicle rapidly develops, communication perception integration network has brought bigger value to unmanned aerial vehicle application. In the communication perception integration network, unmanned aerial vehicle has the ability of autonomic perception through equipping radar sensing equipment, and radar sensing and communication can share hardware such as antenna and transceiver, have reduced the size and the cost of equipment, have realized better perception performance.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization.
The technical scheme adopted by the invention is as follows:
an unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization comprises the following steps:
step S1, constructing an unmanned aerial vehicle communication perception integrated system and obtaining system parameters, wherein the system comprises a ground base station, an unmanned aerial vehicle and a user, the base station, the unmanned aerial vehicle and the user are all provided with a single antenna, and the unmanned aerial vehicle is provided with a radar perception device;
step S2, constructing an information age minimization expression according to the unmanned aerial vehicle communication perception integrated system model;
step S3, obtaining an optimal position relational expression of the unmanned aerial vehicle through the information age minimization expression;
and step S4, substituting the system parameters into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
Further, in order to improve the performance of unmanned aerial vehicle communication perception integration system to through optimizing unmanned aerial vehicle's position, reduce the information age of data, guarantee the new freshness of data, improve the accuracy of basic station decision-making. The communication method between the unmanned aerial vehicle and the ground base station and the user in the step S1 comprises the following steps:
step S1-1, sending radar waveform sensing users by the unmanned aerial vehicle; in the sensing stage, the time for sensing the target user by the unmanned aerial vehicle is T 1 ；
Specifically, assuming that the drone is not interfering, the drone only receives echo signals reflected back by the user. The channel gain between the drone and the target is:
wherein, 2d 2 For the distance traveled by the echo signal, d 2 Is the distance, beta, between the drone and the target to be perceived 0 Is the large scale channel power at a reference distance of 1 m.
λ∈[0,1]Parameters are allocated for the ground distance between the base station and the user,h is the flying height of unmanned aerial vehicle.
The sensing performance is characterized by the Cramer-rao (cr) bound:
where α >0 is a constant, depending on the particular parameter to be estimated, B is the bandwidth, ρ is the signal-to-noise ratio (SNR) of the received signal reflected from the perceptual target, and T1 is the time the drone spends perceiving the target.
Signal to noise ratio perceived by unmanned aerial vehicle of
Wherein Pmax is the perception power of the unmanned aerial vehicle, sigma 2 Is additive white gaussian noise power.
When CR is bound
Not greater than a given threshold, e.g.
In time, the drone successfully completes the perception task.
Then the perception is constrained to
Wherein,
step S1-2, the unmanned aerial vehicle generates a perception data packet with the size of D bit and transmits the perception data packet to the base station when perceiving the user; in the communication stage, the time for the unmanned aerial vehicle to transmit the sensing data packet to the base station is T 2 ；
Specifically, in the information transmission phase, after the unmanned aerial vehicle successfully perceives the data, new perception data needs to be sent to the base station immediately. The channel gain between the drone and the base station is:
wherein d is 1 Is the distance between the base station and the drone,
the achievable rate of the communication phase can be approximately expressed as
Wherein,
the total throughput of the transmission time T2 is
To complete the transmission of each sensing packet, the transmitted data should be no less than the sensed data: r General assembly ≥D。
Further, the information age minimizing expression in step S2 is as follows:
0≤λ≤1
the PAoI is the time elapsed from the beginning of sending a sensing data packet until the successful update of the next sensing data packet when the unmanned aerial vehicle successfully senses the information from the radar echo; PAoI ═ T 1 +2T 2 ；λ∈[0，1]The ground distance distribution parameters between the base station and the user are represented, namely the horizontal distance from the base station to the unmanned aerial vehicle is lambda d, and the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) d;pmax represents the perceived power of the radar waveform of the drone; b represents a communication bandwidth; h represents the flying height of the unmanned aerial vehicle; h represents the height of the base station; d represents the distance between the base station and the user; intermediate process parameters
β 0 For large scale channel power at a reference distance of 1m, σ 2 Is additive white Gaussian noise power;
Δ 0 is composed of
All constant parts after expansion, CR bound
Not greater than a given threshold value Δ, i.e.
The unmanned aerial vehicle successfully completes the sensing task. Also indicates a perceptual constraint of
Specifically, in order to measure the information freshness of the base station, PAoI is used as a performance index. The AoI (age of information) is defined as the time elapsed since the last generation of a sensing packet sent to the base station. When the unmanned aerial vehicle successfully senses the information from the radar echo, the sensing data packet is generated, AoI starts to increase, until the next sensing data packet is updated successfully, at this time, AoI reaches the maximum value, namely PAoI (PAoI is the time when the next sensing data packet is transmitted — the time when the sensing data packet is generated). Each target generates a sensing data packet after updating, and each sensing data packet has a PAoI value. The PAoI value of each sensing data packet of the target is the same, namely PAoI ═ T 1 +2T 2 。
Further, the CR (Cramer-Rao) boundary
For the purpose of characterizing the performance of the sensor,
where p represents the signal-to-noise ratio (SNR) of the received signal reflected from the perceptual target,
CR boundary
Not greater than a given threshold Δ, the drone successfully completes the perception task, i.e.
All constant parts after unfolding are denoted Δ 0 ；
Further, the relationship of the optimal positions of the unmanned aerial vehicles that are not obtained in step S3 is as follows:
further, the specific method for calculating the optimal position of the drone in step S4 is as follows: calculating lambda satisfying the relation by using a Newton iteration method, and recording the lambda as lambda opt (ii) a The obtained lambda is opt Substituting the initial value into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
Further, the specific step of step S4 is:
step S4-1, presetting convergence accuracy as 10 -3 And assuming an initial lambda value;
step S4-2, substituting the initial lambda value into the optimal position relation of the unmanned aerial vehicle to obtain the optimized lambda opt ；
Step S4-3, judging optimized lambda opt Whether the difference between the value of (d) and the initial lambda value is less than a preset convergence accuracy; if so, λ to be optimized opt As a value ofDistributing coefficients of the optimal position of the unmanned aerial vehicle and finishing iterative computation; otherwise, the resulting optimized λ opt As an initial lambda value and step S4-2 is performed.
Further, the initial λ value is 0.6.
Further, when the unmanned aerial vehicle is in the optimal position, the horizontal distance from the base station to the unmanned aerial vehicle is lambda opt d, the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) opt )d。
By adopting the technical scheme, in the unmanned aerial vehicle communication perception integrated system, the information age of data is reduced by optimizing the position of the unmanned aerial vehicle, the freshness of the data is ensured, and the accuracy of base station decision is improved.
Drawings
The invention is described in further detail below with reference to the accompanying drawings and the detailed description;
fig. 1 is a schematic diagram of an unmanned aerial vehicle communication perception integrated system architecture based on information age minimization according to the present invention;
fig. 2 is a schematic flow chart of the unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
As shown in fig. 1 or 2, the invention discloses an unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization, which comprises the following steps:
step S1, constructing an unmanned aerial vehicle communication perception integrated system and obtaining system parameters, wherein as shown in FIG. 1, the system comprises a ground Base Station (BS), an Unmanned Aerial Vehicle (UAV) and a user (Target), the base station, the UAV and the user are all provided with single antennas, and the UAV is provided with a radar perception device;
step S2, constructing an information age minimization expression according to the unmanned aerial vehicle communication perception integrated system model;
step S3, obtaining an optimal position relational expression of the unmanned aerial vehicle through the information age minimization expression;
and step S4, substituting the system parameters into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
Further, in order to improve the performance of unmanned aerial vehicle communication perception integration system to through optimizing unmanned aerial vehicle's position, reduce the information age of data, guarantee the new freshness of data, improve the accuracy of basic station decision-making. The communication method between the unmanned aerial vehicle and the ground base station and the user in the step S1 comprises the following steps:
step S1-1, sending radar waveform sensing users by the unmanned aerial vehicle; in the perception stage, the time for the unmanned aerial vehicle to perceive the target user is T 1 ；
Specifically, assuming that the drone is not interfering, the drone only receives echo signals reflected back by the user. The channel gain between the drone and the target is:
wherein, 2d 2 For the distance traveled by the echo signal, d 2 Is the distance, beta, between the unmanned aerial vehicle and the target to be sensed 0 Is the large scale channel power at a reference distance of 1 m.
λ∈[0,1]And distributing parameters for the ground distance between the base station and the user, wherein H is the flying height of the unmanned aerial vehicle.
The sensing performance is characterized by the Cramer-rao (cr) bound:
where α >0 is a constant, depending on the particular parameter to be estimated, B is the bandwidth, ρ is the signal-to-noise ratio (SNR) of the received signal reflected from the perceptual target, and T1 is the time the drone spends perceiving the target.
Signal to noise ratio perceived by unmanned aerial vehicle of
Wherein Pmax is the perceived power of the UAV, σ 2 Is additive white gaussian noise power.
When the CR bound
Not greater than a given threshold, e.g.
In time, the drone successfully completes the sensing task.
Then the perception is constrained to
Wherein,
step S1-2, the unmanned aerial vehicle generates a perception data packet with the size of D bit and transmits the perception data packet to the base station when perceiving the user; in the communication phase, the time for the unmanned aerial vehicle to transmit the sensing data packet to the base station is T 2 ；
Specifically, in the information transmission stage, after the unmanned aerial vehicle successfully senses the data, new sensing data needs to be immediately sent to the base station. The channel gain between the drone and the base station is:
wherein d is 1 Is the distance between the base station and the drone,
the achievable rate of the communication phase can be approximately expressed as
Wherein,
the total throughput of the transmission time T2 is
To complete the transmission of each sensing packet, the data transmitted should be no less than the sensed data: r General assembly ≥D。
Further, the information age minimizing expression in step S2 is as follows:
0λ≤1
the PAoI is the time elapsed from the beginning of sending a sensing data packet until the successful update of the next sensing data packet when the unmanned aerial vehicle successfully senses the information from the radar echo; PAoI ═ T 1 +2T 2 ；λ∈[0，1]Representing the ground distance distribution parameters between the base station and the user, namely the horizontal distance from the base station to the unmanned aerial vehicle is lambda d, and the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) d; pmax represents the perceived power of the radar waveform of the drone; b represents a communication bandwidth; h represents the flying height of the unmanned aerial vehicle; h represents the height of the base station; d represents the distance between the base station and the user;
β 0 for large scale channel power at a reference distance of 1m, σ 2 Is additive white gaussian noise power;
Δ 0 is composed of
All constant parts after expansion, CR bound
Not greater than a given threshold value Δ, i.e.
The unmanned aerial vehicle successfully completes the sensing task. Also indicates a perceptual constraint of
Specifically, in order to measure the information freshness of the base station, PAoI is used as a performance index. The aoi (age of information) is defined as the time elapsed since the last generation of a sensing packet sent to the base station. When the unmanned aerial vehicle successfully perceives the information from the radar echo, the perception data packet is generated, AoI starts to increase until the next perception data packet is updated successfully, and at this time, AoI reaches the maximum value, namely PAoI (PAoI is the time when the next perception data packet is transmitted-the time when the perception data packet is generated). Each target generates a sensing data packet after updating, and each sensing data packet has a PAoI value. The PAoI value of each sensing data packet of the target is the same, namely PAoI ═ T 1 +2T 2 。
Further, the CR (Cramer-Rao) boundary
For the purpose of characterizing the sensing performance,
where p represents the signal-to-noise ratio (SNR) of the received signal reflected from the perceptual target,
CR boundary
Not greater than a given threshold Δ, the drone succeedsCompleting a perception task, i.e.
All constant parts after unfolding are denoted Δ 0 ；
Further, the relationship of the optimal positions of the unmanned aerial vehicles that are not obtained in step S3 is as follows:
further, the specific method for calculating the optimal position of the drone in step S4 is as follows: calculating lambda satisfying the relation by using a Newton iteration method, and recording the lambda as lambda opt (ii) a The obtained lambda is opt Substituting the initial value into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
Further, the specific step of step S4 is:
step S4-1, presetting convergence accuracy as 10 -3 And assuming an initial lambda value;
step S4-2, substituting the initial lambda value into the optimal position relation of the unmanned aerial vehicle to obtain the optimized lambda opt ；
Step S4-3, judging the optimized lambda opt Whether the difference between the value of (d) and the initial lambda value is less than a preset convergence accuracy; if so, λ to be optimized opt The value of (a) is used as the distribution coefficient of the optimal position of the unmanned aerial vehicle and the iterative computation is ended; otherwise, the resulting optimized λ opt As an initial lambda value and step S4-2 is performed.
Further, the initial λ value is 0.6.
Further, when the unmanned aerial vehicle is in the optimal position, the horizontal distance from the base station to the unmanned aerial vehicle is lambda opt d, the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) opt )d。
By adopting the technical scheme, in the unmanned aerial vehicle communication perception integrated system, the position of the unmanned aerial vehicle is optimized, the information age of data is reduced, the freshness of the data is ensured, and the accuracy of base station decision is improved.
It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The embodiments and features of the embodiments in the present application may be combined with each other without conflict. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
Claims (9)
1. Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization is characterized in that: which comprises the following steps:
step S1, constructing an unmanned aerial vehicle communication perception integrated system and obtaining system parameters, wherein the system comprises a ground base station, an unmanned aerial vehicle and a user, the base station, the unmanned aerial vehicle and the user are all provided with a single antenna, and the unmanned aerial vehicle is provided with a radar perception device;
step S2, constructing an information age minimization expression according to the unmanned aerial vehicle communication perception integrated system model;
step S3, obtaining an optimal position relational expression of the unmanned aerial vehicle through the information age minimization expression;
and step S4, substituting the system parameters into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
2. The unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization of claim 1, wherein: the communication method between the unmanned aerial vehicle and the ground base station and the user in the step S1 comprises the following steps:
step S1-1, sending radar waveform sensing users by the unmanned aerial vehicle; in the perception stage, the time for the unmanned aerial vehicle to perceive the target user is T 1 ；
Step (ii) ofS1-2, the unmanned aerial vehicle generates a sensing data packet with the size of D bits when sensing the user and transmits the sensing data packet to the base station; in the communication phase, the time for the unmanned aerial vehicle to transmit the sensing data packet to the base station is T 2 。
3. The information age minimization-based unmanned aerial vehicle communication perception integrated system position optimization method according to claim 2, characterized in that: the information age minimization expression in step S2 is as follows:
the PAoI is the time that the unmanned aerial vehicle transmits a sensing data packet when successfully sensing information from a radar echo and the time elapses from the beginning of the transmission of the sensing data packet until the next sensing data packet is updated successfully; PAoI ═ T 1 +2T 2 ；λ∈[0，1]The ground distance distribution parameters between the base station and the user are represented, namely the horizontal distance from the base station to the unmanned aerial vehicle is lambda d, and the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) d; pmax represents the perceived power of the radar waveform of the drone; b represents a communication bandwidth; h represents the flying height of the unmanned aerial vehicle; h represents the height of the base station; d represents the distance between the base station and the user; intermediate process parameters
β 0 For large-scale channel power at a reference distance of 1m, σ 2 Is additive white Gaussian noise power;
Δ 0 is CR boundary
And is
The unmanned aerial vehicle successfully completes the sensing task.
4. The unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization of claim 3, wherein: CR (Cramer-Rao) boundary
For the purpose of characterizing the sensing performance,
where p represents the signal-to-noise ratio (SNR) of the received signal reflected from the perceptual target,
5. the unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization of claim 3, wherein: the relationship formula of the optimal position of the unmanned aerial vehicle, which is not obtained in step S3, is:
6. the unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization of claim 5, wherein: concrete method for calculating optimal position of unmanned aerial vehicle in step S4The method comprises the following steps: calculating lambda satisfying the relation by using a Newton iteration method, and recording the lambda as lambda opt (ii) a The obtained lambda is opt Substituting the initial value into the relation of the optimal position of the unmanned aerial vehicle, and calculating the optimal position of the unmanned aerial vehicle by using a Newton iteration method.
7. The information age minimization-based unmanned aerial vehicle communication perception integrated system position optimization method according to claim 6, characterized in that: the specific step of step S4 is:
step S4-1, presetting convergence accuracy as 10 -3 And assuming an initial lambda value;
step S4-2, substituting the initial lambda value into the optimal position relation of the unmanned aerial vehicle to obtain the optimized lambda opt ；
Step S4-3, judging optimized lambda opt Whether the difference between the value of (d) and the initial lambda value is less than a preset convergence accuracy; if so, λ to be optimized opt The value of (a) is used as the distribution coefficient of the optimal position of the unmanned aerial vehicle and the iterative computation is ended; otherwise, the resulting optimized λ opt As an initial lambda value and step S4-2 is performed.
8. The unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization of claim 7, wherein: the initial lambda value was 0.6.
9. The information age minimization-based unmanned aerial vehicle communication perception integrated system position optimization method according to claim 7, characterized in that: when the unmanned aerial vehicle is in the best position, the horizontal distance from the base station to the unmanned aerial vehicle is lambda opt d, the horizontal distance from the unmanned aerial vehicle to the user is (1-lambda) opt )d。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210730374.3A CN115133977B (en) | 2022-06-24 | 2022-06-24 | Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210730374.3A CN115133977B (en) | 2022-06-24 | 2022-06-24 | Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115133977A true CN115133977A (en) | 2022-09-30 |
| CN115133977B CN115133977B (en) | 2023-05-02 |
Family
ID=83380532
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210730374.3A Active CN115133977B (en) | 2022-06-24 | 2022-06-24 | Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115133977B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115714793A (en) * | 2022-11-08 | 2023-02-24 | 大连海事大学 | Method for transmitting perception information in industrial Internet of things on demand |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030033178A1 (en) * | 2001-07-31 | 2003-02-13 | The Boeing Company | Method, system and computer program product for analyzing maintenance operations and assessing the readiness of repairable systems |
| CN110543185A (en) * | 2019-07-19 | 2019-12-06 | 宁波大学 | unmanned aerial vehicle data collection method based on minimum information age |
| KR20210069549A (en) * | 2019-12-03 | 2021-06-11 | 경희대학교 산학협력단 | Multiple unmanned aerial vehicles navigation oprimizaition method and multiple unmanned aerial vehicles system using the same |
| CN114389679A (en) * | 2022-01-13 | 2022-04-22 | 吉林大学 | Multi-antenna unmanned aerial vehicle sensing and transmission optimization method based on information age minimization |
-
2022
- 2022-06-24 CN CN202210730374.3A patent/CN115133977B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20030033178A1 (en) * | 2001-07-31 | 2003-02-13 | The Boeing Company | Method, system and computer program product for analyzing maintenance operations and assessing the readiness of repairable systems |
| CN110543185A (en) * | 2019-07-19 | 2019-12-06 | 宁波大学 | unmanned aerial vehicle data collection method based on minimum information age |
| KR20210069549A (en) * | 2019-12-03 | 2021-06-11 | 경희대학교 산학협력단 | Multiple unmanned aerial vehicles navigation oprimizaition method and multiple unmanned aerial vehicles system using the same |
| CN114389679A (en) * | 2022-01-13 | 2022-04-22 | 吉林大学 | Multi-antenna unmanned aerial vehicle sensing and transmission optimization method based on information age minimization |
Non-Patent Citations (1)
| Title |
|---|
| HUIMIN HU 等: "AoI-Minimal Trajectory Planning and Data Collection in UAV-Assisted Wireless Powered IoT Networks" * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115714793A (en) * | 2022-11-08 | 2023-02-24 | 大连海事大学 | Method for transmitting perception information in industrial Internet of things on demand |
| CN115714793B (en) * | 2022-11-08 | 2023-09-15 | 大连海事大学 | On-demand transmission method for perception information in industrial Internet of things |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115133977B (en) | 2023-05-02 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110430577B (en) | Unmanned aerial vehicle base station group deployment method based on time correlation | |
| CN1205485C (en) | Wireless user position update using infrastructure measurements | |
| US5293642A (en) | Method of locating a mobile station | |
| Salehi et al. | Flash: Federated learning for automated selection of high-band mmwave sectors | |
| JP4609231B2 (en) | Wireless position detection method and system | |
| CN109526019B (en) | Method for correcting wireless private network base station coverage propagation model based on measured data | |
| CN105430664B (en) | It is a kind of to be fitted the method and apparatus that path loss is propagated in prediction based on classification | |
| US20080080429A1 (en) | Minimum variance location estimation in wireless networks | |
| JP7511957B2 (en) | Communication beam selection method for intelligent reflecting surface based on beam index map | |
| CN111867039A (en) | TA (timing advance) determination method and device of terminal equipment | |
| CN105676179A (en) | 433MHz signal based indoor positioning method and system | |
| CN112866904B (en) | Channel-training-free large-dimension communication beam alignment method based on beam index map | |
| CN115133977A (en) | Unmanned aerial vehicle communication perception integrated system position optimization method based on information age minimization | |
| US8639255B2 (en) | Simulation system, simulation apparatus, and simulation method | |
| JP6304653B2 (en) | Interference estimation system, wireless device | |
| Reddy et al. | Spectrum cartography techniques, challenges, opportunities, and applications: A survey | |
| TW202023217A (en) | A method and system of measuring radio wave distribution of a radio signal source and estimating corresponding radio characteristics by using a flying vehicle | |
| Park et al. | Optimal pilot and data power allocation for joint communication-radar air-to-ground networks | |
| CN117278085B (en) | Vehicle-mounted millimeter wave terminal beam tracking method, device, equipment and storage medium | |
| CN118233946A (en) | Network selection method considering unmanned aerial vehicle quality of test in air-ground heterogeneous wireless network | |
| KR20160046247A (en) | Method for predicting radio propagation path loss and apparatus thereof | |
| CN114222312B (en) | Moving target tracking method based on self-adaptive wave beam | |
| CN107154819B (en) | Satellite relay selection optimization method based on geographical position information | |
| CN115714988A (en) | Network control method and device, vehicle, electronic equipment and readable storage medium | |
| KR101646658B1 (en) | Methods of cooperative sensing using optimizing threshold in CR, and apparatus thereof |
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 |