CN113872894A - Unmanned aerial vehicle, routing inspection communication system and channel estimation method - Google Patents
Unmanned aerial vehicle, routing inspection communication system and channel estimation method Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0224—Channel estimation using sounding signals
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64C—AEROPLANES; HELICOPTERS
- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
- B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
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- 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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/0212—Channel estimation of impulse response
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/024—Channel estimation channel estimation algorithms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
- H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
- H04L25/0202—Channel estimation
- H04L25/024—Channel estimation channel estimation algorithms
- H04L25/0256—Channel estimation using minimum mean square error criteria
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
Abstract
An unmanned aerial vehicle inspection communication channel estimation method comprises the steps of setting unmanned aerial vehicle wireless channel pilot frequency; estimating channel impulse response at the pilot frequency; and obtaining the impulse response of the whole channel of the unmanned aerial vehicle polling communication according to the channel impulse response at the pilot frequency. The pilot frequency adopts comb-shaped pilot frequency, at least comprises 3 columns, the middle column carries pilot frequency information, and the rest front and rear columns are all zero. The step of estimating the channel impulse response at the pilot frequency comprises the steps of obtaining the channel impulse response at the pilot frequency by utilizing an LS channel estimation algorithm and using the channel impulse response as a Kalman filtering initial value; taking the response obtained in the last step as a Kalman filtering initial value, performing iteration by utilizing Kalman filtering, and recording the obtained channel impulse response; and optimizing the Kalman filtering result by utilizing the frequency domain characteristic and based on SVD.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicles, and particularly relates to an unmanned aerial vehicle, a patrol communication system and a channel estimation method.
Background
During the air inspection of the unmanned aerial vehicle, the transmission of wireless signals is constrained by a wireless channel due to the height, the moving speed and the complex environment of the unmanned aerial vehicle, so that the distortions such as amplitude fading, phase deviation, Doppler spread and the like are generated, meanwhile, the channel environment is rapidly changed, and the priori information of the channel is difficult to predict, so that the channel has the time-varying characteristic.
In the prior art, channel estimation is generally classified into blind estimation, pilot-based estimation and semi-blind estimation. Blind estimation utilizes second-order statistical characteristics of transmitted and received signals to carry out channel estimation, and overhigh complexity often exists in practice; the pilot frequency-based estimation method estimates the channel state information by sending orthogonal pilot frequency, and has lower complexity and strong real-time performance; the semi-blind estimation is the first two compromise, and combines the second-order statistical characteristics of the signal and the transmission pilot frequency to accurately estimate the channel state information. The existing estimation scheme has high complexity and harsh application conditions, and is not suitable for a high-speed environment. Therefore, the current channel estimation method suitable for ground wireless mobile communication cannot be well adapted to the requirement of unmanned aerial vehicle channel estimation.
Disclosure of Invention
The embodiment of the invention provides a smart grid unmanned aerial vehicle routing inspection channel estimation method, which comprises the following steps,
establishing an unmanned aerial vehicle low-altitude channel model based on unmanned aerial vehicle data transmission link channel characteristics;
estimating channel impulse response at the pilot frequency position by an LS algorithm, estimating the channel impulse response at the pilot frequency position by Kalman filtering, and recovering the channel impulse response at the subchannel position of the transmission data information by an interpolation algorithm;
and estimating and correcting the estimation result by using the frequency domain correlation characteristic of the time-varying channel of the unmanned aerial vehicle and the minimum mean error criterion.
The embodiment of the invention adopts an improved Kalman filtering structure based on the channel estimation of the comb-shaped pilot training symbol, and improves the channel estimation performance of the downlink of the unmanned aerial vehicle.
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The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
fig. 1 is a schematic diagram of an unmanned aerial vehicle inspection communication system according to one embodiment of the invention.
Fig. 2 is a schematic block diagram of an unmanned aerial vehicle inspection communication system according to one embodiment of the present invention.
Detailed Description
The unmanned aerial vehicle communication faces the challenges of high transmission rate, multipath fading, Doppler frequency offset and the like, and if a flat fading channel is adopted, the unmanned aerial vehicle communication does not accord with the fast fading characteristic of the unmanned aerial vehicle channel aiming at the specific channel estimation problem in the unmanned aerial vehicle communication scene.
Due to the fact that the scale of a domestic power grid is large, inspection and maintenance of a power line are important. The large-bandwidth, high-reliability and low-delay communication of the 5G technology can meet the requirement of unmanned aerial vehicle routing inspection. However, the power transmission line corridor environment is complex and changeable, and when the unmanned aerial vehicle patrols and examines, the channel environment and the channel transmission coefficient of the unmanned aerial vehicle and the base station change in real time. In order to guarantee the reliability and effectiveness of communication, the unmanned aerial vehicle terminal needs to estimate and track the channel transmission coefficient after change in real time.
According to the requirements faced by unmanned aerial vehicle communication, with reference to actual application conditions, the invention provides an OFDM unmanned aerial vehicle communication system with variable transmission rate.
According to one or more embodiments, an unmanned aerial vehicle inspection communication system. Because transmission line overlength, unmanned aerial vehicle storage patrols and examines video picture data volume too big, need transmit the video picture that detects in real time and accept the basic station on ground. Under a large-scale MIMO millimeter wave unmanned aerial vehicle communication scene, a system model of an unmanned aerial vehicle terminal and a base station under the scene is established by establishing a return access point in a segmented manner, and the problems of video picture real-time transmission and channel coefficient real-time feedback are solved.
The embodiment aims at the situation that the power transmission line of the smart power grid is too long, the data quantity of the video pictures stored and patrolled by the unmanned aerial vehicle is too large, and the detection video pictures need to be transmitted to the ground receiving base station in real time. A system model of the unmanned aerial vehicle terminal and the base station under the scene is established by establishing the return access points in a segmented mode, and the problems of real-time video picture transmission and real-time channel coefficient estimation are solved.
In accordance with one or more embodiments, a drone channel estimation algorithm addresses the channel coefficient estimation problem of the drone communication subsystem.
Firstly, an unmanned aerial vehicle low-altitude channel model is established based on unmanned aerial vehicle data transmission link channel characteristics.
And estimating channel impulse response at the pilot frequency position by an LS algorithm, estimating the channel impulse response at the pilot frequency position by adopting Kalman filtering, and recovering the channel impulse response at the subchannel position of the transmission data information by using an interpolation algorithm.
And finally, estimating and correcting an estimation result by using the frequency domain correlation characteristic of the time-varying channel of the unmanned aerial vehicle and the minimum mean error criterion, so that the estimation precision is improved, the noise interference is reduced, and the tracking of the time-varying channel of the unmanned aerial vehicle is facilitated.
According to one or more embodiments, a drone channel estimation algorithm includes the steps of:
(1) designing a pilot frequency;
(2) estimating channel impulse response at the pilot frequency;
(3) and recovering the impulse response of the whole data channel by using the channel response at the pilot frequency estimated in the last step.
The pilot frequency design scheme is that comb-shaped pilot frequency is adopted and mainly comprises three columns, wherein the middle column carries pilot frequency information, and the rest two columns are all zero symbols. This can reduce the interference of the channel, and the interference can be reduced because all zero symbols are around the pilot.
Wherein, the step of estimating the channel impulse response at the pilot frequency comprises:
(1) and obtaining a channel impulse response at a pilot frequency position by using an LS channel estimation algorithm to be used as a Kalman filtering initial value.
(2) And taking the response obtained in the last step as a Kalman filtering initial value, performing iteration by utilizing Kalman filtering, and recording the obtained channel impulse response.
(3) The Kalman filtering result is optimized by utilizing the frequency domain characteristic and based on SVD, so that the estimation precision is improved.
The step of recovering the impulse response of the data channel comprises processing the estimated frequency response of the pilot channel by using a proper interpolation algorithm, thereby obtaining the frequency effect of the whole channel. And adopting an improved linear interpolation algorithm according to the actual application scene.
The improved interpolation algorithm comprises two steps:
(1) and according to the linear interpolation, expanding the channel estimation value at the pilot frequency and carrying out inverse fast Fourier transform to obtain a time domain channel value.
(2) And (4) taking sampling points outside the maximum time path as noise, setting the corresponding time domain channel value to be 0, and performing fast Fourier transform to obtain a final channel estimation value. The algorithm can eliminate most of the channel noise to improve the accuracy of the channel estimation.
In this embodiment, an LS channel estimation algorithm is first used to calculate the initial kalman filter value at the comb pilot, the pilot sequence of the transmitted signal is known, and the LS channel estimation algorithm can be used to estimate the channel impulse response at the pilot. And taking the obtained channel response at the pilot frequency position of the first time as an initial value of Kalman filtering, and performing iteration by using a Kalman filter. And optimizing the Kalman filtering result by utilizing the frequency domain characteristic and based on SVD.
The time domain characteristic of an unmanned aerial vehicle channel is fully considered in the traditional Kalman algorithm, channel frequency response values at different moments are estimated, and the frequency domain characteristic of the unmanned aerial vehicle channel is not considered. The embodiment of the invention provides an optimization algorithm, which further optimizes the Kalman filtering channel frequency response value based on MMSE and adopts an SVD (singular value decomposition) method to perform rank reduction processing on the related autocorrelation matrix. Specifically, the method comprises the following steps of,
(1) firstly, calculating the error between the obtained Kalman filtering channel frequency response value and the LS estimation channel frequency response value, and then optimizing the channel estimation value by adopting Minimum Mean Square Error (MMSE).
(2) And performing rank reduction processing on the autocorrelation matrix related to the step one by adopting a singular value decomposition method.
The optimization algorithm reduces the calculated amount, and simultaneously utilizes the cross correlation of channel impulse responses at different moments to improve the estimation precision.
In the embodiment, based on an unmanned aerial vehicle communication scene, a channel model of an unmanned aerial vehicle terminal and a base station terminal in the scene is constructed, the estimation problem of channel state information in the scene is solved, and the channel estimation problem of an unmanned aerial vehicle routing inspection communication subsystem is refined and solved. The time-varying characteristic of the unmanned aerial vehicle inspection communication system is studied, the characteristic that the channel environment changes violently when the unmanned aerial vehicle flies is fully utilized, and the method for estimating the channel by using Kalman filtering and suitable for the time-varying channel of the unmanned aerial vehicle is provided. The method fully considers the characteristics of the time-varying channel of the unmanned aerial vehicle, improves the estimation precision, and meanwhile utilizes SVD decomposition to perform rank reduction processing on the correlation matrix, simplifies the operation process and improves the operation rate.
Therefore, the channel estimation technology using the improved carl filter provided by the embodiment sufficiently considers the time-frequency characteristic of the time-varying channel of the unmanned aerial vehicle, improves the algorithm precision, and meanwhile, simplifies the operation process by using the related technology and improves the operation rate of the channel estimation technology algorithm.
As shown in fig. 1, is a diagram of a communication system scenario for a drone. According to the practical unmanned aerial vehicle power transmission line inspection case, considering that the transmitting power of the unmanned aerial vehicle communication equipment is limited, a receiving base station is built on a power tower every 5km, the unmanned aerial vehicle transmits high-definition video pictures collected by a high-definition camera or an infrared camera to the receiving base station in real time, and the base station transmits to a ground master control console through a communication line carried on the power tower, so that the problems that the power transmission line is too long and the transmission and storage data amount is too large are solved.
Meanwhile, in order to solve the problem that the channel environment is changeable in the unmanned aerial vehicle inspection process, a system model of the unmanned aerial vehicle terminal and the base station under the scene is established by establishing the return access points in a segmented mode, and the problem of real-time estimation of the channel coefficient is solved. Under the establishment of a system model, a novel channel estimation algorithm is established, the characteristics of the time-varying channel of the unmanned aerial vehicle are fully considered, improvement is carried out according to the characteristics, and the feedback reconstruction performance is improved by reducing the feedback quantity through the characteristics of the relevant time-varying channel.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An unmanned aerial vehicle inspection communication channel estimation method is characterized by comprising the following steps,
setting unmanned aerial vehicle wireless channel pilot frequency;
estimating channel impulse response at the pilot frequency;
and obtaining the impulse response of the whole channel of the unmanned aerial vehicle polling communication according to the channel impulse response at the pilot frequency.
2. The channel estimation method according to claim 1, wherein the pilots are comb pilots, and at least include 3 columns, the middle column carries pilot information, and the remaining front and rear columns are all zeros.
3. The channel estimation method of claim 1, wherein the step of estimating the channel impulse response at the pilot comprises,
(1) and obtaining a channel impulse response at a pilot frequency position by using an LS channel estimation algorithm to be used as a Kalman filtering initial value.
(2) And taking the response obtained in the last step as a Kalman filtering initial value, performing iteration by utilizing Kalman filtering, and recording the obtained channel impulse response.
(3) And optimizing the Kalman filtering result by utilizing the frequency domain characteristic and based on SVD.
4. The channel estimation method of claim 1, wherein the step of obtaining the impulse response of the entire channel comprises,
obtaining the frequency response of the whole channel by utilizing a linear interpolation algorithm according to the estimated frequency response of the channel at the pilot frequency, wherein the linear interpolation algorithm comprises the following steps:
(1) according to linear interpolation, expanding the channel estimation value at the pilot frequency and carrying out inverse fast Fourier transform to obtain a time domain channel value;
(2) and (4) taking sampling points outside the maximum time path as noise, setting the corresponding time domain channel value to be 0, and performing fast Fourier transform to obtain a final channel estimation value.
5. The channel estimation method of claim 3, wherein the optimizing for Kalman filtering results comprises,
and optimizing the Kalman filtering channel frequency response value based on MMSE, and performing rank reduction processing on the related autocorrelation matrix by adopting an SVD (singular value decomposition) method.
6. An unmanned aerial vehicle inspection communication system for estimating an unmanned aerial vehicle inspection communication channel using the method of claim 1, wherein the unmanned aerial vehicle inspection communication system is used for the inspection of smart grids by unmanned aerial vehicles.
7. The unmanned aerial vehicle inspection communication system according to claim 6, including a plurality of ground receiving base stations to which the unmanned aerial vehicle transmits segment-wise detected video images obtained in the inspection.
8. The unmanned aerial vehicle inspection communication system of claim 7, wherein the ground receiving base station is disposed on a power tower or a power pole.
9. The unmanned aerial vehicle inspection communication system according to claim 1, wherein the unmanned aerial vehicle inspection communication is based on 5G OFDM.
10. An unmanned aerial vehicle for smart grid polling, wherein the unmanned aerial vehicle establishes data communication with a ground receiving base station based on 5G OFDM, and the channel estimation method is as claimed in claim 1.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114978365A (en) * | 2022-05-30 | 2022-08-30 | 中国电子科技集团公司第五十四研究所 | Multi-node time-frequency signal inspection and data processing method based on center reference |
| CN115955296A (en) * | 2023-03-14 | 2023-04-11 | 北京城市轨道交通咨询有限公司 | Unmanned inspection-based rail transit operation and maintenance data transmission method and device |
| CN116208206A (en) * | 2023-05-05 | 2023-06-02 | 南京邮电大学 | MIMO unmanned aerial vehicle communication design method based on Kalman filtering parameter configuration |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104486266A (en) * | 2014-12-12 | 2015-04-01 | 中国科学院自动化研究所 | Method and device for estimating channel based on MIMO-OFDM system |
| CN107070823A (en) * | 2017-05-12 | 2017-08-18 | 重庆大学 | Parameter model channel estimation methods based on Kalman filtering |
-
2021
- 2021-09-17 CN CN202111090446.4A patent/CN113872894A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104486266A (en) * | 2014-12-12 | 2015-04-01 | 中国科学院自动化研究所 | Method and device for estimating channel based on MIMO-OFDM system |
| CN107070823A (en) * | 2017-05-12 | 2017-08-18 | 重庆大学 | Parameter model channel estimation methods based on Kalman filtering |
Non-Patent Citations (1)
| Title |
|---|
| 施华杰: "一种基于改进卡尔曼滤波的无人机信道估计算法", 《无线电通信技术》, pages 401 - 406 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114978365A (en) * | 2022-05-30 | 2022-08-30 | 中国电子科技集团公司第五十四研究所 | Multi-node time-frequency signal inspection and data processing method based on center reference |
| CN114978365B (en) * | 2022-05-30 | 2023-12-29 | 中国电子科技集团公司第五十四研究所 | Multi-node time-frequency signal inspection and data processing method based on center reference |
| CN115955296A (en) * | 2023-03-14 | 2023-04-11 | 北京城市轨道交通咨询有限公司 | Unmanned inspection-based rail transit operation and maintenance data transmission method and device |
| CN116208206A (en) * | 2023-05-05 | 2023-06-02 | 南京邮电大学 | MIMO unmanned aerial vehicle communication design method based on Kalman filtering parameter configuration |
| CN116208206B (en) * | 2023-05-05 | 2023-07-04 | 南京邮电大学 | MIMO unmanned aerial vehicle communication design method based on Kalman filtering parameter configuration |
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