CN117713967A - Intelligent frequency spectrum sensing anti-interference communication system for ad hoc network unmanned aerial vehicle - Google Patents
Intelligent frequency spectrum sensing anti-interference communication system for ad hoc network unmanned aerial vehicle Download PDFInfo
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Abstract
The invention discloses an intelligent frequency spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle. The invention improves the microwave communication mode of the existing dynamic networking unmanned aerial vehicle, constructs a distributed anti-interference wireless communication system based on intelligent frequency spectrum sensing aiming at a communication system with a fixed frequency band and a fixed mode, carries out decision making according to the signal quality evaluation of each node, can adaptively allocate frequency spectrum resources, greatly improves the anti-interference capability, changes the anti-interference capability into a high-code rate data communication system with an online variable frequency band and adaptively changed according to an external electromagnetic environment, ensures that data can be transmitted at high speed in real time even under the conditions of strong industrial interference and severe transmission environment, and has very obvious practical significance for fully playing the whole function of the dynamic networking unmanned aerial vehicle. In various industries of unmanned aerial vehicle service, the unmanned aerial vehicle can better assist departments to complete tasks, and the most visual information on the ground can be collected at the highest speed.
Description
Technical Field
The invention belongs to the technical field of intelligent anti-interference of unmanned aerial vehicles, and particularly relates to an intelligent spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle.
Background
Because the aircraft is continuously sailing or hovering dynamically changing, the physical environment of the surrounding space changes at any time, and under the normal environment, the aircraft adopts a wave band below 6.0GHz for wireless communication, but the aircraft is more used in the 3G/4G and industry of the frequency bands, and the interference and the background noise are quite large. If the environment is very strong in interference, the frequency point can be dynamically selected, the strong interference frequency point is avoided, the complex and efficient modulation mode is adopted, the code rate is improved, and the data transmission speed of the unmanned aerial vehicle is accelerated.
When a relatively poor communication environment is encountered, in order to reduce the error rate of data transmission, the communication bandwidth needs to be reduced, and the low frequency band can be adopted to reduce the code rate so as to improve the reliability of data downlink. The adaptive code rate is changed, so that the frequency band of communication needs to be changed online, the working efficiency of unmanned aerial vehicle communication is improved, and the situation that the load data has higher requirements on the data transmission speed and quality is met.
However, in the design scheme of the traditional unmanned aerial vehicle communication system, the frequency band is usually in a fixed mode, and is fixed during the circuit design of the transmitter, so that the frequency band cannot be adaptively changed on line in real time according to the frequency spectrum state. This is very disadvantageous for wireless communication of a real-time drone, which is flexible to communicate according to environmental changes of a wireless channel.
Disclosure of Invention
The invention aims at: in order to solve the above-mentioned problem, a system for ad hoc network unmanned aerial vehicle intelligent spectrum sensing anti-interference communication is provided.
The technical scheme adopted by the invention is as follows: an anti-interference communication system for intelligent spectrum sensing of an ad hoc network unmanned aerial vehicle, which is divided into 5 processing units:
the unmanned aerial vehicle transmitter baseband processing unit comprises digital modulation coding and up-conversion;
the unmanned aerial vehicle radio frequency transmitting unit comprises a high-speed DA, a filtering, frequency selecting, carrier modulation and power amplifier module;
the unmanned aerial vehicle radio frequency receiving unit comprises compression coding processing and channel coding;
the unmanned aerial vehicle receiver radio frequency processing unit comprises low noise amplification, down-conversion, de-hopping and digital demodulation;
the unmanned aerial vehicle receiver spectrum sensing unit;
the unmanned aerial vehicle is a transmitter end, the baseband processing unit carries out interleaving and RS coding on the data stream, then forms a serial data stream, and carries out digital modulation OFDM, and the intelligent frequency selection function;
the radio frequency processing unit comprises a D/A, a local oscillator, a frequency mixing part, a pre-power amplification part, a filtering part and a main power amplification part, wherein the unit receives a transmitted baseband signal, and the baseband signal is finally input to a radio frequency interface after the D/A, the frequency mixing part, the power amplification part and the filtering part and is transmitted through an antenna unit;
the ground station is a receiver end, data received by an antenna are output to the frequency spectrum sensing unit through a radio frequency link, the frequency selection generating unit is used for decoding and demodulating the data to the baseband processing unit and the rear-end data processor, and then the data is displayed on a screen in real time through interface software after being decoded, so that the data transmission process of the whole unmanned aerial vehicle communication link is completed.
In a preferred embodiment, the ground receiver is added with an intelligent frequency spectrum sensing and frequency selecting processing unit, namely the intelligent frequency spectrum sensing and frequency selecting processing unit is added on the basis of a traditional communication device, and is designed into an intelligent communication mode, namely a ground station selects an optimal spectrum section through frequency spectrum sensing according to the current electromagnetic environment and spectrum resource state, a communication instruction is injected, the instruction is received through an autopilot of the unmanned aerial vehicle, the instruction is transmitted to a baseband processing unit through an SPI serial port, the instruction is analyzed by the baseband processing unit, and different radio frequency points are selected according to the instruction;
the basic idea of intelligent frequency selection is as follows: the frequency spectrum sensing unit actively senses the channel quality information of each frequency point, and carries out subsequent frequency selection sequence updating decision according to the sensed channel quality information, and eliminates the frequency points with interference frequency points or poor channel quality so as to avoid the adverse effect of the frequency points on the system performance.
In a preferred embodiment, the steps of the smart spectrum sensing function of the terrestrial receiver are as follows:
selecting a perception technology: according to the system requirement and available hardware resources, a proper frequency spectrum sensing technology is selected, and the invention adopts an energy detection technology, namely, the calculated power spectrum density is used as the input of a frequency spectrum sensing unit;
collecting signals: using wireless receiving equipment, a chip adopts software radio equipment consisting of AD9371 and FPGA, signal samples in the environment are collected, the collected samples are time domain signals, and sample points are quantized data;
signal pretreatment: preprocessing the acquired signals, filtering, sampling and time-frequency converting so as to carry out subsequent spectrum analysis;
spectral analysis: performing cyclic spectrum estimation analysis on the preprocessed signals to obtain spectrum information, namely transforming the time domain signals obtained by sampling into frequency domain signals through FFT;
threshold value judgment: selecting a proper threshold judgment method according to a sensing technology, comparing spectrum information with a threshold, and judging whether a spectrum is occupied or idle;
making a perception strategy: making a corresponding decision according to the result of the threshold decision, and judging whether the frequency spectrum is available or not; if the spectrum is considered idle, measuring signal strength, signal-to-noise ratio, and interference-to-signal ratio indicators of the respective channels to determine to select a channel having good signal quality;
and constructing a networking unmanned aerial vehicle frequency spectrum sensing scheme.
In a preferred embodiment, the cyclic spectrum estimation analysis adopts a Welch method to estimate the power spectrum density of a discrete signal x [ n ] with the sampling frequency of Fs, and carries out a perception decision, and the implementation steps comprise:
1) Framing signals: dividing the signal x [ N ] into a plurality of overlapped frames, wherein the length of each frame is N, and the overlapping rate is set to be 50%; framing may be achieved using a sliding window;
2) Windowing: applying a window function w [ n ], such as a hanning window, to each frame; the length of the window function should be the same as the frame length;
3) Fourier transform FFT: applying a fourier transform (FFT) to each frame after windowing to obtain a spectral representation of each frame;
4) Square calculation: performing amplitude square operation on the frequency spectrum of each frame to obtain power spectrum estimation of each frame;
5) Average calculation: the power spectrum estimation results for a plurality of frames are averaged.
5. A system for ad hoc network unmanned aerial vehicle intelligent spectrum sensing anti-interference communication as claimed in claim 3, wherein: the threshold decision algorithm is as follows:
6) Spectral information of the input signal and a threshold;
7) Calculating the energy value energy of the received signal;
8) If the energy is greater than the threshold value threshold, judging that the spectrum resource is available;
9) Otherwise, judging that the spectrum resource is not available;
10 Outputting the availability result of the spectrum resource.
In a preferred embodiment, the spectrum sensing scheme of the unmanned aerial vehicle for networking essentially measures the interference condition of each frequency point, and the interference condition can be measured by using the noise power of each frequency point; the received signal power measured when no node in the network transmits data is the noise power; in order to reduce extra time slot expenditure during spectrum sensing, each node switches to a receiving state after completing data transmission of the transmitting time slot, and noise power measurement of the transmitting frequency point is performed within the protection time length of the tail of the time slot.
In a preferred embodiment, the smart spectrum sensing is performed as follows:
for the quality information of the removed frequency points, the state of the removed frequency points is perceived periodically through a receiving and transmitting frequency set of a control node, so that when the interference of the removed frequency points is eliminated, the frequency points can be normally recovered for use;
after the frequency point quality information measured by each node is quantized, the quantized result is added to the physical layer frame header information and reported to the master node for convergence processing and decision making; when the background noise of the measured frequency point is higher than a set threshold value, the frequency point at the node is considered to be unavailable, a frequency point mark is set to be 1, and otherwise, the frequency point mark is set to be 0;
the node carries the node ID, the frequency point ID and the frequency point mark in the sending time slot to carry out framing sending, and the frequency point sensing result is converged to the master node to make a decision;
after receiving frequency point sensing information gathered by each node, the master node eliminates unavailable frequency points in the current frequency selection pattern; removing the frequency selection frequency point, and considering channel quality measurement results of all nodes on the frequency point; theoretically, when a node exists in the network and the frequency point of the node is unavailable, the frequency point is removed from the frequency selection pattern;
after the main node eliminates the interference frequency points, updating the state information of all the frequency points; all the frequency points use a 1bit frequency point mark to represent the state, 1 represents that the frequency point is unavailable, and 0 represents that the frequency point is available; the updated frequency point information is added into the frame header information of the physical layer for transmission;
when the node selects the frequency, determining the next frequency point to be selected according to the frequency selection pattern and the frequency point state, namely when the next frequency point to be selected in the frequency selection pattern is available, selecting the frequency to the frequency point for working next time, and when the next frequency point to be selected in the frequency selection pattern is unavailable, continuing to search the available frequency point in sequence in a backward delay way; at this time, all nodes in the network can adaptively update the frequency selection sequence in the network according to a unified rule.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
1. according to the invention, the microwave communication mode of the existing dynamic networking unmanned aerial vehicle is improved, an intelligent frequency spectrum sensing and signal processing unit is added for a communication system only with a fixed frequency band and a fixed mode, a distributed anti-interference wireless communication system based on intelligent frequency spectrum sensing is constructed, and according to signal quality evaluation of each node, decision can be made to adaptively allocate frequency spectrum resources, so that the anti-interference capability is greatly improved, the high-code rate data communication system with an online variable frequency band and adaptively changed according to an external electromagnetic environment is changed, and the high-speed real-time data transmission can be ensured even under the conditions of strong industrial interference and severe transmission environment, so that the method has very obvious practical significance for fully playing the whole functions of the dynamic networking unmanned aerial vehicle. In each trade of unmanned aerial vehicle service, can assist each department to accomplish the task better to gather the most audio-visual information in ground with fastest speed, the device has advantages such as function is strong, the adaptability is good, the reliability is high, the configuration is nimble, directly perceived convenient.
2. According to the invention, the intelligent frequency spectrum sensing communication function which is not possessed by the traditional unmanned aerial vehicle is added, the frequency range of 300Mhz to 6.0GHz can be selected in a self-adaptive mode, not only can the frequency point be adjusted by software in the range of 300Mhz to 6.0GHz, but also the bandwidth is adjustable (< 100 MHz), and compared with the traditional fixed-mode communication mode, the performance and the flexibility are greatly improved. The invention solves the problems that the frequency is fixed, the modulation mode is fixed, and the transmission mode cannot be adjusted on line according to the channel environment in the traditional scheme, and can ensure that the large-size high-resolution image acquired on site can be transmitted back to the ground in real time by an optimized system, so that ground station data processing personnel can take emergency measures according to the large-size high-resolution image, such as immediately coping with disaster, disorder and the like, thereby avoiding the defect that the image data are lost or can be acquired afterwards, ensuring the on-time completion of the unmanned aerial vehicle inspection, reconnaissance, mapping and other tasks, and fully playing the due role of the unmanned aerial vehicle.
Drawings
FIG. 1 is a schematic block diagram of intelligent anti-interference communication of an unmanned aerial vehicle of the present invention;
FIG. 2 is a block diagram of a cyclic spectrum sensing decision in the present invention;
FIG. 3 is a timing diagram of the intelligent spectrum sensing according to the present invention;
fig. 4 is a block diagram of distributed intelligent spectrum sensing in accordance with the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
With reference to figures 1-4 of the drawings,
an intelligent spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle can be applied to other platforms such as vehicle platforms and occasions needing wireless networking data transmission. The system and method of the present invention will be described in further detail below with reference to a communication system capable of transmitting high resolution real-time images by using a dynamic networking unmanned aerial vehicle intelligent spectrum sensing system.
When the unmanned aerial vehicle communication device works normally, the volume is 160mm multiplied by 100mm, the weight is 2Kg, and the power consumption is 55W. By adopting the transceiver working in the frequency band of 0.3-6 GHz, the transmission code rate can reach 4Mbps, the error rate is 10-6, the output power is 5W, and the ground-to-air distance is 30 km.
The modulation scheme in the example is an anti-multipath OFDM scheme, and other modulation schemes can be adopted, so that the method is similar.
The special image encoder adopts an H265 standard algorithm, the image acquired by the industrial digital camera is a color image, the pixel precision is 24 bits, the resolution ratio is 1920 x 1080, 30 frames per second, and generally, 2 Mbps-8 Mbps are required for transmitting high-definition video. In general, the remote measurement and engineering parameter data transmitted by the device are very small, and only 50Kbps is needed.
In the intelligent frequency spectrum sensing and signal processing unit, the frequency band of 0.3-6 GHz is divided into 57 sub-channels, the bandwidth of each channel is 100MHz, and the center frequency of each sub-channel represents the channel. The spectrum sensing, the identification and the decision are all carried out based on 57 channels, the CNN deep learning machine model is built, the spectrum data after sampling processing is input, the busy and idle states of 57 channels can be output through a CNN network, the '1' is an idle state, and the '0' is an unavailable busy state.
The acquired spectrum data set is divided into a training set and a testing set, and parameters of an optimized CNN deep learning model are obtained through big data training of the training set, so that the method can be used for detecting 57 channel busy and idle states of each unmanned aerial vehicle node on line, selecting idle channels and marking.
For the selected idle channels, the invention selects the optimal channel for the communication of the dynamic networking unmanned aerial vehicle by comparing the signal-to-noise ratio, the interference-to-signal ratio and other channel quality evaluation indexes of each idle channel.
In spectrum identification, the implementation steps of the convolutional neural network algorithm are as follows:
data preparation: the spectral data is converted into a form suitable for CNN input. The one-dimensional spectrum data is represented as a two-dimensional matrix, wherein one dimension represents time, the other dimension represents frequency, and the matrix value is amplitude.
And (3) network architecture design: the CNN structure includes a convolutional layer, a pooling layer, and a fully-connected layer. The convolution layer is used for extracting the characteristics of the spectrum data, the pooling layer is used for reducing the data dimension, the full-connection layer is used for classifying and outputting the results, and 57 state results are output in a classified mode, and the value is 0 or 1, namely the idle state or the occupied state.
Training a network: the CNN is trained using the labeled spectral dataset. And the parameters of the CNN are adjusted through a back propagation algorithm and a gradient descent optimization algorithm, so that the CNN can adapt to the characteristic extraction and classification tasks of the frequency spectrum data.
Model evaluation: the performance of CNNs was evaluated using separate test data sets. Indexes such as accuracy, precision, recall, etc. are typically used to evaluate the classification performance of the model.
Model application: and identifying and classifying the new unknown spectrum data by using the trained CNN model.
It should be noted that the specific parameter settings and network architecture design of CNNs will vary depending on the specific spectrum identification task and data characteristics. In addition, techniques such as data preprocessing, data enhancement, and model optimization can also be used in combination to improve the performance of CNN in spectrum identification.
In a word, the CNN algorithm has wide application in spectrum recognition, and can effectively extract the characteristics of spectrum data and perform classification recognition.
When the following 3 working conditions are adopted, the communication system can select different modes on line according to the environmental conditions, so that the communication quality is improved:
(1) When the transmission device transmits 1080P high-definition images, remote control instructions are injected through a ground station to select an intelligent perception mode, frequency bands below 6G are adopted, the code rate is less than or equal to 4Mbps, and the modulation mode is OFDM;
(2) When the packet loss rate of the communication system is more than 10%, injecting a remote control instruction to select a mode, and adopting a P frequency band, wherein the code rate is less than or equal to 2Mbps, and the modulation mode is OFDM;
(3) When the communication system works in the L/S/C frequency band, strong interference is received, and the communication system works in an intelligent working mode, each node can adaptively select a channel, the code rate is less than or equal to 4Mbps, and the modulation mode is OFDM;
therefore, by default, an OFDM modulation mode is adopted, and under the condition of 4M bandwidth, the telemetry, the engineering parameter source packet and the image data stream are packed to form a unified data frame, the length of the data frame is 1024 bytes, a data stream with the length of less than 4Mbps is formed, and then operations such as digital modulation are performed, and finally the data stream is input to the radio frequency module and is transmitted through an antenna.
At the receiving end of the ground station monitoring center, signals received from the antenna pass through a radio frequency module, a demodulation and frame-decoding processing module, and the decoded data are divided into two paths according to a source packet format, namely one path is telemetry and engineering parameters, and the other path is image compressed code stream data.
And the server of the ground monitoring center collects and processes electromagnetic signals of the surrounding environment to generate large frequency spectrum data, and performs online training on the CNN deep learning model to update model parameters in real time. The deep learning model is utilized to perform spectrum sensing, the optimal communication channel is selected, the communication performance and the data transmission quality are improved, and a communication system with good robustness and anti-interference is established.
According to the invention, the microwave communication mode of the existing dynamic networking unmanned aerial vehicle is improved, an intelligent frequency spectrum sensing and signal processing unit is added for a communication system only with a fixed frequency band and a fixed mode, a distributed anti-interference wireless communication system based on intelligent frequency spectrum sensing is constructed, and according to signal quality evaluation of each node, decision can be made to adaptively allocate frequency spectrum resources, so that the anti-interference capability is greatly improved, the high-code rate data communication system with an online variable frequency band and adaptively changed according to an external electromagnetic environment is changed, and the high-speed real-time data transmission can be ensured even under the conditions of strong industrial interference and severe transmission environment, so that the method has very obvious practical significance for fully playing the whole functions of the dynamic networking unmanned aerial vehicle. In each trade of unmanned aerial vehicle service, can assist each department to accomplish the task better to gather the most audio-visual information in ground with fastest speed, the device has advantages such as function is strong, the adaptability is good, the reliability is high, the configuration is nimble, directly perceived convenient.
According to the invention, the intelligent frequency spectrum sensing communication function which is not possessed by the traditional unmanned aerial vehicle is added, the frequency range of 300Mhz to 6.0GHz can be selected in a self-adaptive mode, not only can the frequency point be adjusted by software in the range of 300Mhz to 6.0GHz, but also the bandwidth is adjustable (< 100 MHz), and compared with the traditional fixed-mode communication mode, the performance and the flexibility are greatly improved. The invention solves the problems that the frequency is fixed, the modulation mode is fixed, and the transmission mode cannot be adjusted on line according to the channel environment in the traditional scheme, and can ensure that the large-size high-resolution image acquired on site can be transmitted back to the ground in real time by an optimized system, so that ground station data processing personnel can take emergency measures according to the large-size high-resolution image, such as immediately coping with disaster, disorder and the like, thereby avoiding the defect that the image data are lost or can be acquired afterwards, ensuring the on-time completion of the unmanned aerial vehicle inspection, reconnaissance, mapping and other tasks, and fully playing the due role of the unmanned aerial vehicle.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. An intelligent frequency spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle, which is characterized in that: the intelligent spectrum sensing anti-interference communication system for the ad hoc network unmanned aerial vehicle is divided into 5 processing units:
the unmanned aerial vehicle transmitter baseband processing unit comprises digital modulation coding and up-conversion;
the unmanned aerial vehicle radio frequency transmitting unit comprises a high-speed DA, a filtering, frequency selecting, carrier modulation and power amplifier module;
the unmanned aerial vehicle radio frequency receiving unit comprises compression coding processing and channel coding;
the unmanned aerial vehicle receiver radio frequency processing unit comprises low noise amplification, down-conversion, de-hopping and digital demodulation;
the unmanned aerial vehicle receiver spectrum sensing unit;
the unmanned aerial vehicle is a transmitter end, the baseband processing unit carries out interleaving and RS coding on the data stream, then forms a serial data stream, and carries out digital modulation OFDM, and the intelligent frequency selection function;
the radio frequency processing unit comprises a D/A, a local oscillator, a frequency mixing part, a pre-power amplification part, a filtering part and a main power amplification part, wherein the unit receives a transmitted baseband signal, and the baseband signal is finally input to a radio frequency interface after the D/A, the frequency mixing part, the power amplification part and the filtering part and is transmitted through an antenna unit;
the ground station is a receiver end, data received by an antenna are output to the frequency spectrum sensing unit through a radio frequency link, the frequency selection generating unit is used for decoding and demodulating the data to the baseband processing unit and the rear-end data processor, and then the data is displayed on a screen in real time through interface software after being decoded, so that the data transmission process of the whole unmanned aerial vehicle communication link is completed.
2. The intelligent spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle according to claim 1, wherein: the ground receiver is added with an intelligent frequency spectrum sensing and frequency selecting processing unit, namely the intelligent frequency spectrum sensing and frequency selecting processing unit is added on the basis of a traditional communication device, and is designed into an intelligent communication mode, namely a ground station selects the optimal spectrum section of a frequency spectrum through frequency spectrum sensing according to the current electromagnetic environment and the state of frequency spectrum resources, a communication instruction is injected, the instruction is received through an autopilot of an unmanned aerial vehicle, the instruction is transmitted to a baseband processing unit through an SPI serial port, the baseband processing unit analyzes the instruction, and different radio frequency points are selected according to the instruction;
the basic idea of intelligent frequency selection is as follows: the frequency spectrum sensing unit actively senses the channel quality information of each frequency point, and carries out subsequent frequency selection sequence updating decision according to the sensed channel quality information, and eliminates the frequency points with interference frequency points or poor channel quality so as to avoid the adverse effect of the frequency points on the system performance.
3. The intelligent spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle according to claim 1, wherein: the steps of the intelligent frequency spectrum sensing function of the ground receiver are as follows:
selecting a perception technology: according to the system requirement and available hardware resources, a proper frequency spectrum sensing technology is selected, and the invention adopts an energy detection technology, namely, the calculated power spectrum density is used as the input of a frequency spectrum sensing unit;
collecting signals: using wireless receiving equipment, a chip adopts software radio equipment consisting of AD9371 and FPGA, signal samples in the environment are collected, the collected samples are time domain signals, and sample points are quantized data;
signal pretreatment: preprocessing the acquired signals, filtering, sampling and time-frequency converting so as to carry out subsequent spectrum analysis;
spectral analysis: performing cyclic spectrum estimation analysis on the preprocessed signals to obtain spectrum information, namely transforming the time domain signals obtained by sampling into frequency domain signals through FFT;
threshold value judgment: selecting a proper threshold judgment method according to a sensing technology, comparing spectrum information with a threshold, and judging whether a spectrum is occupied or idle;
making a perception strategy: making a corresponding decision according to the result of the threshold decision, and judging whether the frequency spectrum is available or not; if the spectrum is considered idle, measuring signal strength, signal-to-noise ratio, and interference-to-signal ratio indicators of the respective channels to determine to select a channel having good signal quality;
and constructing a networking unmanned aerial vehicle frequency spectrum sensing scheme.
4. A system for ad hoc network unmanned aerial vehicle intelligent spectrum sensing anti-interference communication as claimed in claim 3, wherein: the cyclic spectrum estimation analysis adopts a Welch method to estimate the power spectrum density of a discrete signal x [ n ] with the sampling frequency of Fs, and carries out perception decision, and the implementation steps comprise:
1) Framing signals: dividing the signal x [ N ] into a plurality of overlapped frames, wherein the length of each frame is N, and the overlapping rate is set to be 50%; framing may be achieved using a sliding window;
2) Windowing: applying a window function w [ n ], such as a hanning window, to each frame; the length of the window function should be the same as the frame length;
3) Fourier transform FFT: applying a fourier transform (FFT) to each frame after windowing to obtain a spectral representation of each frame;
4) Square calculation: performing amplitude square operation on the frequency spectrum of each frame to obtain power spectrum estimation of each frame;
5) Average calculation: the power spectrum estimation results for a plurality of frames are averaged.
5. A system for ad hoc network unmanned aerial vehicle intelligent spectrum sensing anti-interference communication as claimed in claim 3, wherein: the threshold decision algorithm is as follows:
6) Spectral information of the input signal and a threshold;
7) Calculating the energy value energy of the received signal;
8) If the energy is greater than the threshold value threshold, judging that the spectrum resource is available;
9) Otherwise, judging that the spectrum resource is not available;
10 Outputting the availability result of the spectrum resource.
6. The intelligent spectrum sensing anti-interference communication system for an ad hoc network unmanned aerial vehicle according to claim 1, wherein: the networking unmanned aerial vehicle frequency spectrum sensing scheme essentially measures the interference condition of each frequency point, and the interference condition can be measured by utilizing the noise power of each frequency point; the received signal power measured when no node in the network transmits data is the noise power; in order to reduce extra time slot expenditure during spectrum sensing, each node switches to a receiving state after completing data transmission of the transmitting time slot, and noise power measurement of the transmitting frequency point is performed within the protection time length of the tail of the time slot.
7. A system for ad hoc network unmanned aerial vehicle intelligent spectrum sensing anti-interference communication as claimed in claim 3, wherein: the intelligent spectrum sensing comprises the following steps:
for the quality information of the removed frequency points, the state of the removed frequency points is perceived periodically through a receiving and transmitting frequency set of a control node, so that when the interference of the removed frequency points is eliminated, the frequency points can be normally recovered for use;
after the frequency point quality information measured by each node is quantized, the quantized result is added to the physical layer frame header information and reported to the master node for convergence processing and decision making; when the background noise of the measured frequency point is higher than a set threshold value, the frequency point at the node is considered to be unavailable, a frequency point mark is set to be 1, and otherwise, the frequency point mark is set to be 0;
the node carries the node ID, the frequency point ID and the frequency point mark in the sending time slot to carry out framing sending, and the frequency point sensing result is converged to the master node to make a decision;
after receiving frequency point sensing information gathered by each node, the master node eliminates unavailable frequency points in the current frequency selection pattern; removing the frequency selection frequency point, and considering channel quality measurement results of all nodes on the frequency point; theoretically, when a node exists in the network and the frequency point of the node is unavailable, the frequency point is removed from the frequency selection pattern;
after the main node eliminates the interference frequency points, updating the state information of all the frequency points; all the frequency points use a 1bit frequency point mark to represent the state, 1 represents that the frequency point is unavailable, and 0 represents that the frequency point is available; the updated frequency point information is added into the frame header information of the physical layer for transmission;
when the node selects the frequency, determining the next frequency point to be selected according to the frequency selection pattern and the frequency point state, namely when the next frequency point to be selected in the frequency selection pattern is available, selecting the frequency to the frequency point for working next time, and when the next frequency point to be selected in the frequency selection pattern is unavailable, continuing to search the available frequency point in sequence in a backward delay way; at this time, all nodes in the network can adaptively update the frequency selection sequence in the network according to a unified rule.
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