CN110996133A - Video signal transmission method for unmanned aerial vehicle - Google Patents
Video signal transmission method for unmanned aerial vehicle Download PDFInfo
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/438—Interfacing the downstream path of the transmission network originating from a server, e.g. retrieving encoded video stream packets from an IP network
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- H04N21/238—Interfacing the downstream path of the transmission network, e.g. adapting the transmission rate of a video stream to network bandwidth; Processing of multiplex streams
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Abstract
The invention relates to a video signal transmission method for an unmanned aerial vehicle, which comprises the following steps: (1) the transmitting terminal generates a data source and encodes the data source; (2) carrying out modulation processing on the coded information and carrying out digital-to-analog conversion to obtain an analog signal to be transmitted; (3) establishing an unmanned aerial vehicle channel model, performing convolution on the analog signal and the channel, and adding an AWGN channel; (4) a receiving end receives the signal subjected to the channel impact response and performs analog-to-digital conversion to obtain a digital signal; (5) carrying out conversion processing on the digital signals and carrying out channel estimation to obtain estimated parallel signals; (6) and demodulating and decoding the estimated parallel signals to obtain received data. Compared with the prior art, the method has the advantages of improving the signal transmission efficiency, reducing the error rate of the transmission signal and the like.
Description
Technical Field
The invention relates to the technical field of wireless communication, in particular to a video signal transmission method for an unmanned aerial vehicle.
Background
In recent years, unmanned aerial vehicles are widely applied in the industrial and commercial fields, and have higher requirements on video communication functions of the unmanned aerial vehicles when the unmanned aerial vehicles are used for tasks such as power line detection and maintenance, mountainous terrain mapping, aerial photography and the like. In the traditional mode, an unmanned aerial vehicle can carry photographic equipment and other sensing equipment to obtain high-resolution image data signals, but the ground end cannot obtain the high-resolution image data signals in real time and can obtain the high-resolution image data signals only after a task is finished, and the data acquisition period is long. With the development of digital communication technology and the development of broadband digital image technology, the unmanned aerial vehicle needs to realize wireless ultra-high definition video transmission through a video signal transmission system, and the data transmission rate is generally required to be up to more than 4 Mbps.
The current real-time data interaction among unmanned aerial vehicles is mainly based on positioning, remote measurement and control, and the data rate is generally below 100 kbps. The link layer mainly adopts the conventional narrow-band communication technology, and the mainstream modulation means at present are BPSK and QPSK. Although these traditional modulation methods can effectively transmit flight control commands under limited broadband, the requirements of the unmanned aerial vehicle on the real-time performance and the definition of wireless ultra-high definition video transmission cannot be met. If the transmission rate of information is increased, the frequency bandwidth occupied by the communication system is also increased. The spectrum resources are then limited and non-renewable and cannot grow indefinitely as the demand for communication increases. In order to improve transmission bandwidth and spectral efficiency, the application of Orthogonal Frequency Division Multiplexing (OFDM) technology in the field of unmanned aerial vehicles is being studied at home and abroad. The OFDM is a multi-carrier data transmission technology, the time interval of one symbol is expanded by the number of IFFT points by utilizing the orthogonality of sub-carriers, the frequency spectrum utilization rate is high, the multi-path interference resistance is good, and the data transmission method is very suitable for a data link of an unmanned aerial vehicle.
The terrain environment of the unmanned aerial vehicle is complex and changeable, so that the communication quality of the unmanned aerial vehicle is seriously influenced by the deterioration of channel conditions caused by multipath effect, shadow fading, Doppler frequency shift and the like. The characteristics of the unmanned aerial vehicle channels enable the amplitude and phase of each subcarrier of OFDM to randomly change along with the influence of factors such as carrier frequency offset, timing frequency offset and frequency selective fading, so that fading occurs in a channel re-time domain and a channel frequency domain, and intersymbol interference is generated. At present, a pilot frequency-assisted channel estimation algorithm is widely adopted to recover signals at a receiving end, and the algorithm firstly inserts a pilot frequency pattern in a time domain or a frequency domain, secondly estimates channel response on pilot frequency subcarriers, and finally recovers channel estimation on all subcarriers through an interpolation algorithm.
The channel estimation algorithm on the pilot subcarriers mainly includes a Least Square (LS) algorithm and a Minimum Mean Square Error (MMSE) algorithm. The LS algorithm has low operation complexity and time complexity, but is easily influenced by noise; the MMSE algorithm takes the influence of noise into consideration, the performance is improved well, but the operation complexity and the time complexity are very high, and the implementation by an FPGA is difficult.
Common interpolation algorithms include: constant interpolation, linear interpolation, quadratic interpolation, cubic interpolation, and the like. As the order of the interpolation polynomial increases, the interpolation performance also increases, but the complexity also increases. In practical application, the pilot frequency ratio is low, the performance of constant interpolation and linear interpolation is general, and the complexity of multi-time interpolation is high, so that the method is difficult to be used in practice.
Disclosure of Invention
The present invention is directed to a video signal transmission method for an unmanned aerial vehicle, which overcomes the above-mentioned drawbacks of the prior art.
The purpose of the invention can be realized by the following technical scheme:
a video signal transmission method for a drone, the method comprising the steps of:
(1) the transmitting terminal generates a data source and encodes the data source;
(2) carrying out modulation processing on the coded information and carrying out digital-to-analog conversion to obtain an analog signal to be transmitted;
(3) establishing an unmanned aerial vehicle channel model, performing convolution on the analog signal and the channel, and adding an AWGN channel;
(4) a receiving end receives the signal subjected to the channel impact response and performs analog-to-digital conversion to obtain a digital signal;
(5) carrying out conversion processing on the digital signals and carrying out channel estimation to obtain estimated parallel signals;
(6) and demodulating and decoding the estimated parallel signals to obtain received data.
And (1) RS encoding the data source.
The step (2) specifically comprises the following steps:
(21) carrying out 16QAM mapper modulation on the coded information to obtain a signal modulated by the 16QAM mapper;
(22) performing serial-to-parallel conversion on the modulated signals to obtain parallel signals;
(23) inserting comb-shaped pilot frequency into the parallel signals on a frequency domain to obtain transmission signals containing pilot frequency signals;
(24) carrying out inverse Fourier transform on the transmission signal containing the pilot signal to obtain an OFDM modulation signal;
(25) inserting a cyclic prefix into the OFDM modulation signal in a time domain and performing parallel-serial conversion to obtain the OFDM modulation signal containing the cyclic prefix;
(26) and D/A conversion is carried out on the OFDM modulation signal containing the cyclic prefix to obtain an analog signal to be transmitted.
Step (3) establishing an unmanned aerial vehicle channel model, in particular establishing an unmanned aerial vehicle channel model h of a k path under a flight statek：
a is the amplitude of the line-of-sight component,
the line-of-sight component of the k-th path in flight, c the magnitude of the scatter component,
is the scattered component of the k-th path in flight, fDLOSDoppler shift, T, for LOS pathsampleFor the sampling time, N is the number of OFDM signal symbols, thetanIs the phase of the nth OFDM symbol, thetanSatisfies the uniform distribution on [0,2 pi ],
for the Doppler shift of the nth OFDM symbol, δ (t) is the Dirac function, τmaxFor maximum time delay, δ (t- τ)max) Is the unit impulse response after the maximum time delay.
The step (5) is specifically as follows:
(51) removing a cyclic prefix from the digital signal and performing serial-to-parallel conversion to obtain a parallel signal;
(52) carrying out Fourier transformation on the parallel signals to obtain transmission signals to be estimated;
(53) and the transmission signal to be estimated adopts an improved Kalman filtering algorithm and a DFT interpolation algorithm to carry out channel estimation and channel equalization to obtain an estimated parallel signal.
The channel estimation and channel equalization of the improved Kalman filtering algorithm and the DFT interpolation algorithm in the step (53) specifically comprises the following steps:
(a) estimating channel response at a first time instant using LS algorithm
As an initial value for the iteration;
(b) calculating Kalman filter parameters including a state transition matrix, a process noise correlation matrix and a measurement noise correlation matrix;
(c) performing channel estimation by using Kalman filtering to obtain Kalman filtering channel frequency response value
The self-correlation matrix is subjected to rank reduction by adopting an SVD (singular value decomposition) method to obtain an estimated channel frequency response value at a pilot frequency position
(d) Obtaining estimated channel frequency responses corresponding to the N subcarriers through a DFT interpolation algorithm;
(e) and performing channel equalization, and recovering the transmission signal by estimating the channel frequency response to obtain the estimated parallel signal.
The step (6) is specifically as follows:
(61) performing serial-to-parallel conversion on the estimated parallel signals to obtain serial signals;
(62) demodulating the serial signal to obtain a demodulated signal;
(63) and decoding the demodulation signal to obtain received data.
And (62) demodulating by adopting 16QAM to obtain a demodulation signal.
And (63) performing RS decoding on the demodulation signal to obtain received data.
Compared with the prior art, the invention has the following advantages:
(1) the invention adopts 16QAM modulation, compared with the traditional UAV communication system adopting QPSK modulation, the data transmission efficiency can be improved, and the invention is more suitable for the video signal transmission of the UAV;
(2) the statistical unmanned aerial vehicle channel model is adopted, and compared with other signal transmission system frameworks, the statistical unmanned aerial vehicle channel model has higher pertinence and can simulate the signal transmission performance of the unmanned aerial vehicle in the flight state and different environments;
(3) the invention provides a new improved channel estimation algorithm based on a Kalman filter, and adopts an interpolation algorithm based on DFT, and simulation results show that the algorithm has remarkable advantages compared with LS channel estimation algorithm in the traditional unmanned aerial vehicle signal transmission system.
Drawings
Fig. 1 is a block diagram of the steps of a video signal transmission method for a drone according to the method of the present invention;
FIG. 2 is a block diagram of a statistical UAV channel model used in the method of the present invention;
FIG. 3 is a flow chart of channel estimation for the method of the present invention;
FIG. 4 is a plot of the mean square error of the channel under unmanned aerial vehicle channel conditions using the method of the present invention;
fig. 5 is a diagram illustrating the effect of the time domain signal estimation of the drone according to the method of the present invention.
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, a video signal transmission method for a drone, the method comprising the steps of:
step 1: the transmitter generates a data source, and coded information is obtained through RS coding;
step 2: carrying out 16QAM mapper modulation on the coded information obtained in the step 1 to obtain a signal modulated by the 16QAM mapper;
and step 3: performing serial-to-parallel conversion on the signal modulated in the step 2 to obtain a parallel signal;
and 4, step 4: inserting pilot frequency into the parallel signals obtained in the step (3), and selecting to insert comb-shaped pilot frequency into a frequency domain to obtain transmission signals containing pilot frequency signals;
and 5: performing inverse Fourier transform (IFFT) processing on the transmission signal containing the pilot signal obtained in the step 4 to obtain an OFDM modulation signal;
step 6: and (5) inserting a Cyclic Prefix (CP) into the OFDM modulation signal obtained in the step (5) in a time domain, and performing parallel-serial conversion to complete OFDM modulation.
And 7: performing digital-to-analog conversion (DAC) on the OFDM signal containing the CP obtained in the step (6) to obtain an analog signal;
and 8: an unmanned aerial vehicle channel model is built as shown in fig. 2, and an unmanned aerial vehicle channel model h of the k-th path in the flight statek：
a is the amplitude of the line-of-sight component,
the line-of-sight component of the k-th path in flight, c the magnitude of the scatter component,
is the scattered component of the k-th path in flight, fDLOSDoppler shift, T, for LOS pathsampleFor the sampling time, N is the number of OFDM signal symbols, thetanIs the phase of the nth OFDM symbol, thetanSatisfies the uniform distribution on [0,2 pi ],
for the Doppler shift of the nth OFDM symbol, δ (t) is the Dirac function, τmaxFor maximum time delay, δ (t- τ)max) Is the unit impulse response after the maximum time delay.
Convolving the analog signal obtained in step 7 with the channel, and adding an AWGN channel.
And step 9: receiving the signal subjected to channel impulse response obtained in the step 8 at a receiving end, and completing analog-to-digital conversion (ADC) to obtain a digital signal to be processed;
step 10: removing the CP from the digital signal obtained in the step 9, and completing serial-parallel conversion to obtain a parallel signal;
step 11: performing Fourier transform (FFT) on the parallel signals obtained in the step 10 to obtain transmission signals to be estimated;
step 12: and (3) performing channel estimation and channel equalization on the transmission signal to be estimated obtained in the step (11), and obtaining an estimated parallel signal by adopting an improved Kalman filtering algorithm and a DFT interpolation algorithm.
As shown in fig. 3, the channel estimation and channel equalization performed by the improved kalman filter algorithm and the DFT interpolation algorithm specifically includes:
(a) estimating channel response at a first time instant using LS algorithm
As an initial value for the iteration;
(b) calculating Kalman filter parameters including a state transition matrix, a process noise correlation matrix and a measurement noise correlation matrix;
(c) performing channel estimation by using Kalman filtering to obtain Kalman filtering channel frequency response value
Using SVD decompositionThe method carries out rank reduction processing on the related autocorrelation matrix to obtain the estimated channel frequency response value at the pilot frequency
(d) Obtaining estimated channel frequency responses corresponding to the N subcarriers through a DFT interpolation algorithm;
(e) and performing channel equalization, and recovering the transmission signal by estimating the channel frequency response to obtain the estimated parallel signal.
Step 13: performing serial-to-parallel conversion on the parallel obtained in the step 12 to obtain a serial signal;
step 14: performing demapper on the serial signal obtained in the step 13 to complete 16QAM demodulation;
step 15: and RS decoding the demodulation signal obtained in the step 14 to obtain received data.
An embodiment of the invention was simulated as follows, with the parameter settings shown in table 1.
Table 1 video signal transmission system parameter setting based on unmanned aerial vehicle
The simulation of the unmanned aerial vehicle video signal transmission system and the traditional unmanned aerial vehicle communication system is carried out in a Matlab environment, an unmanned aerial vehicle channel and an AWGN channel in a flight state are used, wherein a channel mean square error curve is shown in figure 4, an estimated channel obtained by the transmission system is compared with a real channel time domain when figure 5 is-10 dB, and it can be seen from the figure that compared with a traditional channel estimation algorithm based on an LS algorithm, the unmanned aerial vehicle signal transmission system based on the improved Kalman filtering channel estimation algorithm has more excellent performance, the channel mean square error is always lower than an order of magnitude, and the unmanned aerial vehicle channel can be tracked in real time.
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 (9)
1. A video signal transmission method for a drone, the method comprising the steps of:
(1) the transmitting terminal generates a data source and encodes the data source;
(2) carrying out modulation processing on the coded information and carrying out digital-to-analog conversion to obtain an analog signal to be transmitted;
(3) establishing an unmanned aerial vehicle channel model, performing convolution on the analog signal and the channel, and adding an AWGN channel;
(4) a receiving end receives the signal subjected to the channel impact response and performs analog-to-digital conversion to obtain a digital signal;
(5) carrying out conversion processing on the digital signals and carrying out channel estimation to obtain estimated parallel signals;
(6) and demodulating and decoding the estimated parallel signals to obtain received data.
2. The video signal transmission method for the drone of claim 1, wherein step (1) RS encodes the data source.
3. The video signal transmission method for the unmanned aerial vehicle according to claim 1, wherein the step (2) specifically comprises:
(21) carrying out 16QAM mapper modulation on the coded information to obtain a signal modulated by the 16QAM mapper;
(22) performing serial-to-parallel conversion on the modulated signals to obtain parallel signals;
(23) inserting comb-shaped pilot frequency into the parallel signals on a frequency domain to obtain transmission signals containing pilot frequency signals;
(24) carrying out inverse Fourier transform on the transmission signal containing the pilot signal to obtain an OFDM modulation signal;
(25) inserting a cyclic prefix into the OFDM modulation signal in a time domain and performing parallel-serial conversion to obtain the OFDM modulation signal containing the cyclic prefix;
(26) and D/A conversion is carried out on the OFDM modulation signal containing the cyclic prefix to obtain an analog signal to be transmitted.
4. A video signal transmission method for unmanned aerial vehicle according to claim 1, wherein the step (3) of establishing the unmanned aerial vehicle channel model is specifically establishing the unmanned aerial vehicle channel model h of the k-th path in flightk：
a is the amplitude of the line-of-sight component,
the line-of-sight component of the k-th path in flight, c the magnitude of the scatter component,
is the scattered component of the k-th path in flight, fDLOSDoppler shift, T, for LOS pathsampleFor the sampling time, N is the number of OFDM signal symbols, thetanIs the phase of the nth OFDM symbol, thetanSatisfies the uniform distribution on [0,2 pi ], fDnFor the Doppler shift of the nth OFDM symbol, δ (t) is the Dirac function, τmaxFor maximum time delay, δ (t- τ)max) Is the unit impulse response after the maximum time delay.
5. The video signal transmission method for the unmanned aerial vehicle according to claim 1, wherein the step (5) is specifically as follows:
(51) removing a cyclic prefix from the digital signal and performing serial-to-parallel conversion to obtain a parallel signal;
(52) carrying out Fourier transformation on the parallel signals to obtain transmission signals to be estimated;
(53) and the transmission signal to be estimated adopts an improved Kalman filtering algorithm and a DFT interpolation algorithm to carry out channel estimation and channel equalization to obtain an estimated parallel signal.
6. The video signal transmission method for the unmanned aerial vehicle according to claim 5, wherein the step (53) of performing the channel estimation and the channel equalization by using the modified Kalman filtering algorithm and the DFT interpolation algorithm comprises:
(a) estimating channel response at a first time instant using LS algorithm
As an initial value for the iteration;
(b) calculating Kalman filter parameters including a state transition matrix, a process noise correlation matrix and a measurement noise correlation matrix;
(c) performing channel estimation by using Kalman filtering to obtain Kalman filtering channel frequency response value
The self-correlation matrix is subjected to rank reduction by adopting an SVD (singular value decomposition) method to obtain an estimated channel frequency response value at a pilot frequency position
(d) Obtaining estimated channel frequency responses corresponding to the N subcarriers through a DFT interpolation algorithm;
(e) and performing channel equalization, and recovering the transmission signal by estimating the channel frequency response to obtain the estimated parallel signal.
7. The video signal transmission method for the unmanned aerial vehicle according to claim 1, wherein the step (6) is specifically as follows:
(61) performing serial-to-parallel conversion on the estimated parallel signals to obtain serial signals;
(62) demodulating the serial signal to obtain a demodulated signal;
(63) and decoding the demodulation signal to obtain received data.
8. A video signal transmission method for a drone according to claim 7, characterised in that step (62) uses 16QAM demodulation to obtain the demodulated signal.
9. The video signal transmission method for the drone of claim 7, wherein the step (63) of RS decoding the demodulated signal results in the received data.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| JP2008092583A (en) * | 1995-07-12 | 2008-04-17 | Thomson Consumer Electronics Inc | Apparatus for decoding video signals encoded in different formats |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| JP2008092583A (en) * | 1995-07-12 | 2008-04-17 | Thomson Consumer Electronics Inc | Apparatus for decoding video signals encoded in different formats |
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|---|
| 施华杰，肖曼琳，郑棋，陈兴杰: "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》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112911223A (en) * | 2021-01-15 | 2021-06-04 | 四川一电航空技术有限公司 | Image processing method, device and equipment based on aircraft and storage medium |
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