CN116647311B - Unmanned aerial vehicle communication signal processing method, system and equipment based on blind source separation - Google Patents
Unmanned aerial vehicle communication signal processing method, system and equipment based on blind source separation Download PDFInfo
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- H—ELECTRICITY
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Abstract
The invention relates to a method, a system and equipment for processing communication signals of an unmanned aerial vehicle based on blind source separation, wherein the method adopts the following steps: the input information is processed into baseband data symbols, and the baseband data symbols are sent out by an antenna; an upper path of antenna and a lower path of antenna are adopted to receive radio frequency signals, after LNA low noise amplification and filtration are carried out on the two paths of signals, down mixing treatment is carried out to obtain intermediate frequency signals, blind source separation is carried out on the two paths of intermediate frequency signals at the same time, correlation capturing is carried out on the generated separated signals respectively, one path of signals with correlation peaks exceeding a gate valve is selected to carry out despreading demodulation treatment, and pre-information is output for decoding and error correction after frequency offset correction and phase correction, and received data is output; aiming at the short-time stationarity of signals in time slots and the non-vanishing time correlation of colored noise, interference signals and useful signals are separated from communication signals with comb spectrum blocking interference pollution, the useful communication signals are extracted, and analysis of actual signals shows that the method has a good effect on unmanned aerial vehicle communication interference signal processing.
Description
Technical Field
The invention relates to the technical field of communication, in particular to an unmanned aerial vehicle communication signal processing method and system based on blind source separation.
Background
In the broadband mobile communication process, interference suppression is realized through a time domain adaptive filtering algorithm, interference suppression can also be performed through a signal processing method or wavelet transformation method of a transformation domain, a time domain signal is subjected to Fourier transformation to a frequency domain, interference identification and zero resetting processing are performed, and then inverse Fourier transformation is performed to the time domain for demodulation. In recent years, the problem of spectrum leakage in FFT is improved, so that the frequency domain processing technology is widely paid attention to, and the overlap transformation and the wavelet transformation have advantages in the aspect of data reconstruction, but the transformation algorithm hardware is difficult to realize and is not suitable for engineering application. In addition, global search methods such as genetic algorithm and the like are introduced into an array antenna beam forming method, and nulls are formed in the interference direction. When the number of antenna units is large, the global search method needs a long time to complete the beamforming process, so that the real-time function of the application scene is limited.
At present, unmanned aerial vehicle mobile communication generally adopts modes such as frequency hopping, direct spread spectrum, OFDM (multi-carrier orthogonal modulation), single carrier, and the like, communication channel electromagnetic environment generally has unintentional interference such as broadcast interference, industrial interference, lightning interference, and targeted interference such as tracking interference, blocking interference, synchronous system interference, and the like, tracking interference mainly tracks frequency hopping of frequency hopping communication signals, generates interference signals covering a frequency hopping frequency band to interfere according to the frequency hopping, requires that the frequency hopping frequency of a communication terminal machine is greater than the reaction speed of interference equipment, but the frequency hopping interference equipment covers interference in the frequency band, and has an interference effect by adopting blocking type power suppression. The synchronous system plays an important role in communication, and if an jammer jams the synchronous system, the jammer can have a great influence on the communication system. All interference falling into the passband will have an intensity higher than that of the useful signal, which will affect the Automatic Gain Control (AGC) of the receiver, and the receiver will control the gain of the receiving channel by taking the intensity of the interfering signal as gain feedback, so that the useful signal will not get enough gain amplification, and will not reach the demodulation threshold. The frequency domain interference return-to-zero elimination method generally adopted has a certain limitation, and the anti-interference performance is greatly restricted. Aiming at typical comb spectrum blocking suppression interference, the interference is separated through a blind source separation algorithm, AGC effective gain amplification is carried out on a useful signal channel, and correct demodulation of reception is ensured.
The blind source separation (blind sources separation, BSS) method is proposed by Herault and Jutten in 1986 at the earliest, and is mainly used for separating useful sound signals from various mixed sound signals of underwater vehicles such as torpedoes, ships, submarines and the like, and is applied to separation and identification of the sound signals. In recent years, along with the wide application of signal processing technology in various fields of science and technology, blind source separation technology is applied to data communication and array signal processing besides voice signal separation and recognition, so as to realize multi-user signal separation; the method is applied to image processing and recognition, and realizes image feature extraction, image denoising and moving object detection; the method is applied to the processing of the geoscience spatial information, and realizes the classification and identification of remote sensing images; the method is applied to biomedical signal processing and realizes signal separation of electrocardiograms, electroencephalograms, magnetoencephalography and the like. The blind source separation algorithm is applied to separation of comb-shaped blocking interference of unmanned aerial vehicle communication, and no more research and exploration exists at present.
Blind source separation algorithms are classified into independent component analysis algorithms (Independent Component Analysis, ICA), non-negative matrix factorization algorithms (Nonnegative Matrix Factorization, NMF) and sparse component analysis algorithms (Sparse Component Analysis, SCA) by decomposition of signal features. Because the receiver is a Single Channel, the observation signals comprise a plurality of signals such as information sources, multipath, interference and the like, the blind source separation problem is converted into a Single Channel BSS (SCBSS) problem, and the Single Channel blind source separation algorithm is a special condition of underdetermined blind source separation, and refers to a processing technology for separating mixed signals of a plurality of source signals received by one sensor at the same time. At this time, it is very difficult to estimate the multi-channel source signal by using only the single channel observation signal.
The single-channel blind source separation method commonly used at present is a single-channel blind source separation method based on cyclic spectrum domain filtering, parameter difference and virtual multiple channels. In 1992, cohen proposed whether the signal can be separated into multi-component signals or not, which are closely related to the clustering characteristics of the multi-component signals in the time-frequency joint domain. In 2002 Lewis and Sejnowski propose a super-complete ICA, which provides a concept for solving the underdetermined problem, but the implementation is still difficult.
The unmanned aerial vehicle communication signal processing method based on blind source separation is required to be capable of effectively separating interference signals and useful communication signals and achieving unification of multiple interference patterns.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides an unmanned aerial vehicle communication signal processing method based on blind source separation, an unmanned aerial vehicle communication signal processing system based on blind source separation and unmanned aerial vehicle communication signal processing equipment based on blind source separation.
The technical scheme adopted for solving the technical problems is as follows:
the unmanned aerial vehicle communication signal processing method based on blind source separation is constructed, and comprises the following steps:
the data transmission process comprises the following steps: the input information is converted into baseband data symbols through spread spectrum and data mapping processing after coding, interleaving and rate matching and punching, and the baseband data symbols are sent out by an antenna after radio frequency power amplification and filtering;
the data receiving process comprises the following steps: the method comprises the steps of receiving radio frequency signals by an upper antenna and a lower antenna, performing LNA low noise amplification and filtering on the two signals, performing down mixing treatment to obtain intermediate frequency signals, performing blind source separation on the two intermediate frequency signals at the same time by adopting frequency hopping frequency locked by a phase-locked loop, performing correlation capturing on the generated separated signals, performing despreading demodulation treatment on one signal with a correlation peak exceeding a gate valve, performing frequency offset correction and phase correction, outputting pre-information for decoding and correcting errors, and outputting received data.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein the method for simultaneously carrying out blind source separation on two paths of intermediate frequency signals comprises the following steps:
step one: initializing system parameters, and setting sampling rate, symbol rate, framing length, spread spectrum bit number, modulation mode and Doppler frequency offset parameters;
step two: according to the set frame format, the information source dataProcessed according to the data transmission process, emits a simulated radio frequency signal +.>；
Step three: radio frequency signalThrough simulation channel, gaussian white noise is added according to signal-to-noise ratio>Adding blocking interference according to the interference-to-signal ratio/>The superimposed signals are respectively sent into two paths of receiving channels at the same time and respectively used as +.>The two paths of signal receiving signals are respectively and simultaneously processed from the fourth step to the tenth step; wherein (1)>The formula is adopted:
；
step four: for two paths of signalsThe centering and whitening treatment are carried out to obtain +.>；
Step five: initializing: iteration number k=0, weight vector w (0) = {0}, convergence threshold；
Step six: k=k+1;
step seven: for separation matrixUpdating;
step eight: for separation matrixCarrying out normalization processing on the weight;
step nine: if it isIf the algorithm is not converged, returning to the step six;
step ten: the algorithm converges stably to obtain independent componentsAt this time->The interfering signal has been separated.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein the method for simultaneously carrying out blind source separation on two paths of intermediate frequency signals further comprises the following steps:
step eleven: will bePerforming frequency offset correction and phase offset correction;
step twelve: de-interleaving, de-spreading, decoding the output information and comparing the output data with the originally transmitted dataCarrying out error rate statistics;
step thirteen: and (5) ending the simulation flow.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein a FAST_ICA algorithm is adopted to calculate a separation matrix W by maximizing negative entropy, and an objective function is as follows:
；
wherein ,for the number of independent components +.>Is positive constant, +.>Is a Gaussian random variable with zero mean and unit variance,/is>Representing mathematical expectation values +.>As a non-quadratic function.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein the unmanned aerial vehicle communication signal processing method comprises the following steps ofThe method comprises the following steps:
assume thatWith zero mean and unit variance, the source signals are super-gaussian and sub-gaussian signals:
；
wherein ,is a constant;
thenThe derivative of (2) is:
；
when (when)When in use, by->Negative entropy can be approximated as:
；
solving a separation matrixSo that the separated estimated signal +.>Enable function->Reaching the maximum;
specifying variance of independent componentsThe above problems are converted into the constraint conditionIs the maximum value of (2); let Lagrangian multiplier be +.>The following steps are:
；
upper pair of rollersDeriving, and enabling the derivative to be zero, and then:
；
let the upper left side beThe gradient is as follows:
；
obtained by Newton iteration methodIs defined by the iterative formula:
；
in the above-mentioned method, the step of,the iteration times;
；
after each iteration of the separation matrix, it is normalized:
。
the invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein the set frame format adopts:
pilot sequence 0 uses 4 pseudo codes 256 long as the synchronization header;
the pilot frequency sequence 1 adopts 4 pseudo codes with 256 lengths for indicating the format of a data transmission frame, and carries out self-adaptive multi-rate communication;
the pilot sequence 2 adopts 2 pseudo codes with 128 lengths, and is used for accurately correcting the phase offset of each section of data packet of useful signals obtained by signal blind source separation under the condition of low signal-to-noise ratio sensitivity.
The invention discloses a blind source separation-based unmanned aerial vehicle communication signal processing method, wherein the data structure of a data transmission time slot has 13 bursts, and the method comprises the following steps:
burst 0 consists of RU segment, pilot sequence 0 and pilot sequence 1;
burst 1 through burst 12 are composed of 3 parts: data segment 0, pilot sequence 2, data segment 1.
The unmanned aerial vehicle communication signal processing system based on blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on blind source separation, wherein the system comprises a data sending processing unit and a data receiving processing unit;
the data transmission processing unit is used for carrying out coding, interleaving and rate matching perforation on input information, converting the input information into baseband data symbols after spread spectrum and data mapping treatment, and transmitting the baseband data symbols by an antenna after radio frequency power amplification and filtering;
the data receiving and processing unit is used for receiving radio frequency signals by adopting an upper path of antenna and a lower path of antenna, performing LNA low noise amplification and filtration on the two paths of signals, performing down mixing treatment to obtain intermediate frequency signals, performing blind source separation on the two paths of intermediate frequency signals at the same time by adopting frequency hopping frequency locked by a phase-locked loop, performing correlation capturing on the generated separated signals respectively, performing despreading demodulation processing on one path of signals with correlation peaks exceeding a gate valve, outputting pre-information for decoding and correcting errors after frequency offset correction and phase correction, and outputting received data.
The unmanned aerial vehicle communication signal processing equipment based on the blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on the blind source separation, wherein the unmanned aerial vehicle communication signal processing equipment based on the blind source separation comprises a transmitting end and a receiving end;
the transmitting end encodes, interweaves and rate matching punches the input information, and then changes the input information into baseband data symbols after spread spectrum and data mapping processing, and the baseband data symbols are transmitted by an antenna after radio frequency power amplification and filtering;
the receiving end adopts an upper path of antenna and a lower path of antenna to receive radio frequency signals, after LNA low noise amplification and filtration are carried out on the two paths of signals, intermediate frequency signals are obtained through down mixing, the mixed local frequency adopts frequency hopping frequency locked by a phase-locked loop, blind source separation is carried out on the two paths of intermediate frequency signals at the same time, the generated separated signals are respectively subjected to correlation capturing, one path of signal with a correlation peak exceeding a gate valve is selected for despreading demodulation processing, and after frequency offset correction and phase correction, pre-information is output for decoding and error correction, and received data is output.
The invention has the beneficial effects that: the invention adopts the idea of blind source separation, and based on a communication blind source separation anti-interference method of spatial pre-whitening, aiming at the short-time stationarity of signals in time slots and the non-vanishing time correlation of colored noise, interference signals and useful signals are separated from communication signals with comb spectrum blocking interference pollution, the useful communication signals are extracted, and analysis of actual signals shows that the method has better effect on unmanned plane communication interference signal processing, and also has better adaptability and robustness.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention will be further described with reference to the accompanying drawings and embodiments, in which the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained by those skilled in the art without inventive effort:
fig. 1 is a data transmission flow chart of a blind source separation-based unmanned aerial vehicle communication signal processing method according to a preferred embodiment of the present invention;
fig. 2 is a data receiving flow chart of a processing method of communication signals of an unmanned aerial vehicle based on blind source separation according to a preferred embodiment of the invention;
FIG. 3 is a flow chart of a prior art data reception;
fig. 4 is a physical frame structure schematic diagram of a method for processing communication signals of an unmanned aerial vehicle based on blind source separation according to a preferred embodiment of the present invention;
fig. 5 is an original signal spectrum (with noise) sent by a transmitting end of a frequency hopping spread spectrum system of an unmanned aerial vehicle communication signal processing method based on blind source separation according to a preferred embodiment of the present invention;
fig. 6 is a schematic diagram of a method for processing communication signals of an unmanned aerial vehicle based on blind source separation according to a preferred embodiment of the present invention, wherein interference signal spectrum (interference signal ratio 10 dB) is blocked by superposition of wireless channels;
fig. 7 is a comb spectrum blocking interference superposition spectrum received by a receiver of the unmanned aerial vehicle communication signal processing method based on blind source separation according to the preferred embodiment of the present invention;
fig. 8a is a diagram of a time domain signal with interference received by an antenna on a receiver of a method for processing a communication signal of an unmanned aerial vehicle based on blind source separation according to a preferred embodiment of the present invention;
fig. 8b is a time domain signal diagram with interference received by an antenna under a receiver of a blind source separation-based unmanned aerial vehicle communication signal processing method according to a preferred embodiment of the present invention
Fig. 9a is a time domain signal diagram of a signal 1 separated by a blind source separation algorithm according to a blind source separation-based unmanned aerial vehicle communication signal processing method according to a preferred embodiment of the present invention;
fig. 9b is a time domain signal diagram of a signal 2 separated by a blind source separation algorithm according to a blind source separation-based unmanned aerial vehicle communication signal processing method according to a preferred embodiment of the present invention;
fig. 10 is a comb spectrum blocking interference signal spectrum separated by fast_ica algorithm according to the unmanned aerial vehicle communication signal processing method based on blind source separation according to the preferred embodiment of the present invention;
FIG. 11 is a spectrum of useful signals separated by FAST_ICA algorithm in the unmanned aerial vehicle communication signal processing method based on blind source separation according to the preferred embodiment of the present invention;
fig. 12 is a schematic diagram of a blind source separation method model of the present invention in the unmanned aerial vehicle communication signal processing method based on blind source separation according to the preferred embodiment of the present invention;
fig. 13 is a schematic diagram of a blind source separation method model of a blind source separation-based unmanned aerial vehicle communication signal processing method according to a preferred embodiment of the present invention;
FIG. 14 is a schematic diagram of a conventional ICA algorithm;
fig. 15 is a schematic block diagram of a blind source separation-based unmanned aerial vehicle communication signal processing system in accordance with a preferred embodiment of the present invention;
fig. 16 is a schematic diagram of an application of the unmanned aerial vehicle communication signal processing system based on blind source separation according to the preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the following description will be made in detail with reference to the technical solutions in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by a person skilled in the art without any inventive effort, are intended to be within the scope of the present invention, based on the embodiments of the present invention.
The unmanned aerial vehicle communication signal processing method based on blind source separation according to the preferred embodiment of the invention is shown in fig. 1, and referring to fig. 2-7, 8a, 8b, 9a, 9b, 10, 11, 12, 13 and 14, the method comprises the following steps:
as shown in fig. 1, the data transmission procedure: after coding, interleaving and rate matching perforation, the input information increases the error correction capability of data, becomes a baseband data symbol after spread spectrum and data mapping processing, and is sent out by an antenna after radio frequency power amplification and filtering;
for the data transmission process, a preferred implementation example is as follows:
the unmanned aerial vehicle ground-air anti-interference communication system adopts a TDMA networking mode, and combines the sensitivity of remote communication and the capacity of network nodes. The system is set to take 5 ms as one time slot and 400 ms as one time frame, and one time frame contains 80 time slots. After 2/3LDPC coding and interleaving (index_1536) and punching (rate matching), the input source data is directly spread by pseudo code, and forms a physical frame together with the inserted pilot frequency, the inserted synchronous header pseudo code, the frame format indicator and the phase correction pseudo code, see fig. 4. After up-mixing and filtering, the physical frame is amplified by a radio frequency amplifier and transmitted by an antenna.
Each time slot is composed of a data transmission part and a guard interval, wherein the length of the data transmission part of the data transmission time slot is 4.02ms, and the guard interval length reaches 0.98ms in order to ensure the long-distance communication of more than 200 Km.
The physical frame structure designed by the invention has the advantages that the pilot frequency sequence 0 adopts 4 pseudo codes with 256 lengths as the synchronous heads, so that the signal capturing and synchronizing performance of useful signals obtained by signal blind source separation under the condition of low signal-to-noise ratio sensitivity and Doppler frequency shift correction precision are ensured; the pilot frequency sequence 1 adopts 4 pseudo codes with 256 lengths for indicating the format of a data transmission frame, and carries out self-adaptive multi-rate communication; the pilot sequence 1 adopts 2 pseudo codes with 128 lengths, and is used for accurately correcting the phase offset of each section of data packet of useful signals obtained by signal blind source separation under the condition of low signal-to-noise ratio sensitivity.
The data structure of the data transmission time slot has 13 bursts in total, and the burst structure has 2 kinds.
Burst 0 consists of 3 parts: RU (pilot) segment (256 chips), pilot sequence 0 (256×4 chips), pilot sequence 1 (256×4 chips).
Burst 1 through burst 12 are composed of 3 parts: data segment 0 (1920 chips), pilot sequence 2 (128×2 chips), data segment 1 (1920 chips).
The data segment is used for bearing all types of services provided by the system, and the bit transmission sequence is low-bit first transmission and high-bit later transmission. Pilot sequences 0, 1 implement signal acquisition, fine synchronization, and frame format indication functions and may represent 4 states. Pilot sequence 2 implements phase correction.
As shown in fig. 2, the data reception process: the method comprises the steps of receiving radio frequency signals by an upper antenna and a lower antenna, performing LNA low noise amplification and filtering on the two signals, performing down mixing treatment to obtain intermediate frequency signals, performing blind source separation on the two intermediate frequency signals at the same time by adopting frequency hopping frequency locked by a phase-locked loop, performing correlation capturing on the generated separated signals, performing despreading demodulation treatment on one signal with a correlation peak exceeding a gate valve, performing frequency offset correction and phase correction, outputting pre-information for decoding and correcting errors, and outputting received data;
as shown in fig. 3, the working principle of a conventional receiver is shown, in a conventional digital communication receiver, after a received signal is amplified and mixed to an intermediate frequency, the analog signal is subjected to ADC sampling after being subjected to intermediate amplification and intermediate frequency filtering, the sampled data is subjected to capturing and receiving processing, and finally the received information is decoded. When the interference intensity exceeds the pseudo code processing gain of synchronous acquisition under the condition that the wireless signals received by the antenna are overlapped with blocking interference in a passband, the signals cannot be acquired, the useful signal intensity cannot be effectively estimated, an accurate AGC gain control signal cannot be output, and demodulation is invalid;
the following is described for blind source separation:
the received signal (with interference) measured from a multiple-input multiple-output MIMO nonlinear dynamic system is known asThe algorithm is to find an inverse system to reconstruct the estimated original source signalSource signal->Unknown, output is +.>. in the formula ,is +.>Is a hybrid-split matrix. />Is a noise signal. The blind source separation model is shown in fig. 12:
wherein ,for receiving signals +.>Is a whitening matrix of the received signal +.>Applying a linear transformation to become a white signal having unit variance of each component and being uncorrelated with each other +.>Then:
；
this process is known as whitening, also known as spheroidization or normalized decorrelation.Is a whitened mixed signal, and then has. Will->And let->，/>Is a global mixing matrix. Then
；
Due to linear transformationConnected +.> and />Is two white random vectors, thus matrix +.>Is an orthogonal matrix. Will->As a new received signal, the whitening process causes the original mixing matrix +.>Simplified into a new orthogonal matrix +.>。
Likewise, if the matrix is splitInput is whitened mixed signal +.>Output after separation->Satisfy the following requirements
；
The above shows that blind source separation is carried out on the whitened data, and a matrix is separatedMust be an orthogonal matrix.
If it isIs->Linear transient set of (2), then->，/>Is->The blind source separation problem is reduced to a unmixed matrix W such that +.>Obtained->Approximately the source signal->。
While the conventional feature matrix joint diagonalization (JADE) algorithm achieves separation by joint approximate diagonalization of the fourth-order covariance matrix, the classical FAST fixed-point independent component analysis (FAST_ICA) algorithm achieves separation by selecting an appropriate separation matrix to maximize a cost function that represents non-Gaussian properties of the separated signal. The above algorithm is proposed based on the stationarity of the signal and is not suitable for direct use with non-stationary, time-varying signals. The method uses the short-time stationarity of the signal and the non-vanishing time correlation of the colored noise, and adopts a communication blind source separation anti-interference method based on spatial pre-whitening.
As shown in fig. 14, the ICA algorithm is an optimizing process, and reduces the complexity of data processing through a whitening preprocessing process, so that the independent components obtained by the division approach the source signal to the maximum extent. The whitening treatment isThe ICA algorithm is essentially a process of optimization criteria (objective function selection) and optimization algorithms.
In order to make the algorithm converge FAST, the fast_ica processing algorithm is adopted, and the objective function is a vectorThe set of probability density distributions of (2) is mapped to an operator of a real-valued function, in effect a functional of a probability density function of a random variable, the objective function reaching a maximum or a minimum only if the random variables are statistically independent of each other. The objective function constructing method uses KL divergence as a measure of statistical independence, different objective functions can be deduced from the KL divergence, mainly including an objective function for minimizing mutual information, an objective function for maximizing information transmission or an objective function for maximizing negative entropy, an objective function for maximum likelihood estimation, a constant modulus objective function, an objective function for high-order cumulative amount, an objective function for high-order moment, etc., and the objective function for maximizing negative entropy is adopted, and the negative entropy is the standard for measuring non-Gaussian performance best, and has the advantages that under the condition of low noise, the maximization of the mutual information between the input and the output means that the information redundancy between the input and the output is minimum, so that the mutual information between the outputs is minimum, and all output components are mutually independent in statistics
The blind source separation method is adopted, and a model is shown in fig. 13: the unmanned aerial vehicle communication signal processing system based on blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on blind source separation,
wherein ,for the transmitted signal vector, +.>Is channel white noise>Is an interference signal vector, ">The mixing matrix is composed of instantaneous mixing coefficients at a certain moment, and reflects the interference characteristics of instantaneous transmission;
the method for simultaneously carrying out blind source separation on two paths of intermediate frequency signals comprises the following steps:
step one: initializing system parameters, and setting sampling rate, symbol rate, framing length, spread spectrum bit number, modulation mode and Doppler frequency offset parameters;
step two: according to the set frame format, the information source dataProcessed according to the data transmission process, sends out the simulated radio frequency signal +.>；
Step three: radio frequency signalThrough simulation channel, gaussian white noise is added according to signal-to-noise ratio>Adding blocking interference according to the interference-to-signal ratio>The superimposed signals are respectively sent into two paths of receiving channels at the same time and respectively used as +.>The two paths of signal receiving signals are respectively and simultaneously processed from the fourth step to the tenth step; wherein (1)>The formula is adopted:
；
step four: for two paths of signalsThe centering and whitening treatment are carried out to obtain +.>；
Step five: initializing: iteration number k=0, weight vector w (0) = {0}, convergence threshold；
Step six: k=k+1;
step seven: for separation matrixUpdating;
step eight: for separation matrixCarrying out normalization processing on the weight;
step nine: if it isIf the algorithm is not converged, returning to the step six;
step ten: the algorithm converges stably to obtain independent componentsAt this time->The interfering signal has been separated.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein, the method for simultaneously carrying out blind source separation on two paths of intermediate frequency signals further comprises the following steps:
step eleven: will bePerforming frequency offset correction and phase offset correction;
step twelve: de-interleaving, de-spreading, decoding the output information and comparing the output data with the originally transmitted dataCarrying out error rate statistics;
step thirteen: and (5) ending the simulation flow.
The invention discloses an unmanned aerial vehicle communication signal processing method based on blind source separation, wherein a FAST_ICA algorithm is adopted to calculate a separation matrix W by maximizing negative entropy, and an objective function is as follows:
；
wherein ,for the number of independent components +.>Is positive constant, +.>Is a Gaussian random variable with zero mean and unit variance,/is>Representing mathematical expectation values +.>Is a non-quadratic function;
the method comprises the following steps:
assume thatWith zero mean and unit variance, the source signals are super-gaussian and sub-gaussian signals:
；
wherein ,is a constant;
thenThe derivative of (2) is:
，/>；
when (when)When in use, by->Negative entropy can be approximated as:
；
solving a separation matrixSo that the separated estimated signal +.>Enable function->Reaching the maximum;
specifying variance of independent componentsThe above problems are converted into the constraint conditionIs the maximum value of (2); let Lagrangian multiplier be +.>The following steps are:
；
upper pair of rollersDeriving, and enabling the derivative to be zero, and then:
；
let the upper left side beThe gradient is as follows:
；
obtained by Newton iteration methodIs defined by the iterative formula:
；
in the above-mentioned method, the step of,the iteration times;
；
after each iteration of the separation matrix, it is normalized:
。
the performance simulation verification is described as follows:
the algorithm adopts MATLAB simulation, QPSK modulation mode, symbol rate fb=12.8 Mbps, sampling rate fs=51.2 Mbps,24 times spread spectrum, and 2/3LDPC channel coding. The signal bandwidth is 16MHz, the user data transmission rate is 8Mbps, the Doppler frequency shift is 5.5KHz by adopting a 13 frame data burst mode, and the comb spectrum blocks the interference bandwidth of 400KHz and the interference signal to interference ratio is 10dB.
In the snr= -8db state, the useful signal is weak, and the fast_ica algorithm is not adopted, so that the demodulation is completely impossible. After interference separation by the FAST_ICA algorithm, the demodulation error rate is 0, and correct demodulation is performed;
fig. 5 is a spectrum of an original transmitted signal without blocking interference, but with additive white gaussian noise superposition, fig. 6 is a spectrum of an in-band blocking interference signal, fig. 7 is a spectrum of a signal contaminated by blocking interference received by a receiver, and is a spectrum of a signal superimposed by fig. 6 and fig. 7, the signal being a spectrum of a signal received by an antenna of the receiver;
the time domain signal diagram of the blocked interference signal received by the receiver of fig. 7 is the upper antenna receiving signal and the lower antenna receiving signal respectively, in order to be close to the actual channel, the white noise power of the two paths of receiving signals is the same, and the useful signal power of the upper antenna and the narrow band interference signal power are different from the lower antenna;
the time domain diagrams of the two paths of separated signals are shown in fig. 9a and 9b through a blind source separation algorithm, wherein fig. 9a is a separated comb spectrum blocking interference signal, and fig. 9b is a separated effective signal; the spectrograms of the signals of fig. 9a are shown in fig. 10, and the spectrograms of the signals of fig. 9b are shown in fig. 11;
simulation results show that under the condition that comb spectrum blocking interference exceeds signal power by 10dB, interference can still be effectively restrained through an interference separation algorithm of blind source separation, and signals can be correctly demodulated.
The unmanned aerial vehicle communication signal processing system based on blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on blind source separation, as shown in fig. 15, and comprises a data transmission processing unit 100 and a data receiving processing unit 101;
the data transmission processing unit 100 is configured to encode, interleave and rate match the input information, perform spread spectrum and data mapping processing to obtain baseband data symbols, and perform radio frequency power amplification and filtering to transmit the baseband data symbols from an antenna;
the data receiving and processing unit 101 is configured to receive radio frequency signals by using an upper antenna and a lower antenna, perform LNA low noise amplification and filtering on the two signals, perform down mixing processing to obtain an intermediate frequency signal, perform blind source separation on the two intermediate frequency signals at the same time by using a frequency hopping frequency locked by a phase-locked loop, perform correlation capturing on the generated separated signals, perform despreading demodulation processing on one signal with a correlation peak exceeding a gate valve, perform frequency offset correction and phase correction, output pre-information for decoding and error correction, and output received data;
the invention adopts the idea of blind source separation, and based on a communication blind source separation anti-interference method of spatial pre-whitening, aiming at the short-time stationarity of signals in time slots and the non-vanishing time correlation of colored noise, interference signals and useful signals are separated from communication signals with comb spectrum blocking interference pollution, the useful communication signals are extracted, and analysis of actual signals shows that the method has better effect on unmanned plane communication interference signal processing, and also has better adaptability and robustness.
An example of an application is shown in fig. 16:
in a measurement and control system formed by ground-air communication of an unmanned aerial vehicle, the communication quality is seriously affected due to the interference of industrial interference, a transmitting station, an interphone, a hostile jammer and the like. In order to inhibit interference and ensure reliable communication efficiency, a good effect is exerted in the communication process of the ground-to-air unmanned aerial vehicle frequency hopping spread spectrum networking by a blind source separation processing method;
the communication distance between ground unmanned aerial vehicle 100KM, radio station transmitting power 20W, interference signal surpasses useful signal 10dB, and the interference radio station transmitting power is more than 640W in the communication bandwidth just can implement effective interference, considers that the interference radio station only can real-time effective interference just can be realized to the most in-band frequency channel that needs to cover, and required transmitting power can increase by multiple more, has greatly increased the interference cost. The blind source separation method is used for processing comb spectrum blocking comb spectrum hostile interference, and a good effect is achieved.
The unmanned aerial vehicle communication signal processing equipment based on the blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on the blind source separation, wherein the unmanned aerial vehicle communication signal processing equipment based on the blind source separation comprises a transmitting end and a receiving end;
the transmitting end encodes, interweaves and rate matching punches the input information, and then changes the input information into baseband data symbols after spread spectrum and data mapping processing, and the baseband data symbols are transmitted by the antenna after radio frequency power amplification and filtering;
the receiving end adopts an upper path of antenna and a lower path of antenna to receive radio frequency signals, after LNA low noise amplification and filtration are carried out on the two paths of signals, intermediate frequency signals are obtained through down mixing, the mixed local frequency adopts frequency hopping frequency locked by a phase-locked loop, blind source separation is carried out on the two paths of intermediate frequency signals at the same time, the generated separated signals are respectively subjected to correlation capturing, one path of signal with a correlation peak exceeding a gate valve is selected to carry out despreading demodulation processing, and pre-information is output for decoding and error correction after frequency offset correction and phase correction, and received data is output;
the invention adopts the idea of blind source separation, and based on a communication blind source separation anti-interference method of spatial pre-whitening, aiming at the short-time stationarity of signals in time slots and the non-vanishing time correlation of colored noise, interference signals and useful signals are separated from communication signals with comb spectrum blocking interference pollution, the useful communication signals are extracted, and analysis of actual signals shows that the method has better effect on unmanned plane communication interference signal processing, and also has better adaptability and robustness.
It will be understood that modifications and variations will be apparent to those skilled in the art from the foregoing description, and it is intended that all such modifications and variations be included within the scope of the following claims.
Claims (8)
1. The unmanned aerial vehicle communication signal processing method based on blind source separation is characterized by comprising the following steps of:
the data transmission process comprises the following steps: the input information is converted into baseband data symbols through spread spectrum and data mapping processing after coding, interleaving and rate matching and punching, and the baseband data symbols are sent out by an antenna after radio frequency power amplification and filtering;
the data receiving process comprises the following steps: the method comprises the steps of receiving radio frequency signals by an upper antenna and a lower antenna, performing LNA low noise amplification and filtering on the two signals, performing down mixing treatment to obtain intermediate frequency signals, performing blind source separation on the two intermediate frequency signals at the same time by adopting frequency hopping frequency locked by a phase-locked loop, performing correlation capturing on the generated separated signals, performing despreading demodulation treatment on one signal with a correlation peak exceeding a gate valve, performing frequency offset correction and phase correction, outputting pre-information for decoding and correcting errors, and outputting received data;
the method for simultaneously carrying out blind source separation on the two paths of intermediate frequency signals comprises the following steps:
step one: initializing system parameters, and setting sampling rate, symbol rate, framing length, spread spectrum bit number, modulation mode and Doppler frequency offset parameters;
step two: according to the set frame format, the information source dataProcessed according to the data transmission process, emits a simulated radio frequency signal +.>；
Step three: radio frequency signalThrough simulation channel, gaussian white noise is added according to signal-to-noise ratio>Adding blocking interference according to the interference-to-signal ratio>The superimposed signals are respectively sent into two paths of receiving channels at the same time and respectively used as +.>The two paths of signal receiving signals are respectively and simultaneously processed from the fourth step to the tenth step; wherein (1)>The formula is adopted:
；
for receiving signals in two channels respectively and />A representation; a represents the influence of a wireless blocking interference channel on a signal; a is multiplied by>The method comprises the steps of expressing a signal which is received wirelessly and polluted by blocking interference;
step four: for two paths of signalsThe centering and whitening treatment are carried out to obtain +.>；
Step five: initializing: iteration number k=0, weight vector w (0) = {0}, convergence threshold；
Step six: k=k+1;
step seven: for separation matrixUpdating;
step eight: for separation matrixCarrying out normalization processing on the weight;
step nine: if it isIf the algorithm is not converged, returning to the step six;
w i (n+1) represents the weight, w, iterated to the n+1th time i (n) represents the weight iterated to the nth time,is a set convergence threshold value;
step ten: the algorithm converges stably to obtain independent componentsAt this time->The interfering signal has been separated.
2. The method for processing the communication signals of the unmanned aerial vehicle based on the blind source separation according to claim 1, wherein the method for simultaneously performing the blind source separation on the two paths of intermediate frequency signals further comprises the following steps:
step eleven: will bePerforming frequency offset correction and phase offset correction;
step twelve: de-interleaving, de-spreading, decoding the output information and comparing the output data with the originally transmitted dataCarrying out error rate statistics;
step thirteen: and (5) ending the simulation flow.
3. The unmanned aerial vehicle communication signal processing method based on blind source separation according to claim 1, wherein the separation matrix W is calculated by maximizing negative entropy using fast_ica algorithm, and the objective function is:
；
wherein ,for the number of independent components +.>Is positive constant, +.>Is a Gaussian random variable with zero mean and unit variance,/is>Representing mathematical expectation values +.>As a non-quadratic function.
4. A method of processing unmanned aerial vehicle communication signals based on blind source separation as claimed in claim 3, wherein the followingThe method comprises the following steps:
assume thatWith zero mean and unit variance, the source signals are super-gaussian and sub-gaussian signals:
；
wherein ,is a constant; cosh is a hyperbolic cosine function, cos (ix) =cosh (x), i is the imaginary signal of the complex signal;
thenThe derivative of (2) is:
；
solving a separation matrixSo that the separated estimated signal +.>Enable function->Reaching the maximum;
specifying variance of independent componentsThe separated estimation signal +.>Enable function->Under the constraint that the maximum conversion is reached, +.>Is the maximum value of (2); let Lagrangian multiplier be +.>The following steps are:
；
upper pair of rollersDeriving, and enabling the derivative to be zero, and then:
；
let the upper left side beThe gradient is as follows:
；
obtained by Newton iteration methodIs defined by the iterative formula:
；
in the above-mentioned method, the step of,for the number of iterations->Values after the X whitening treatment;
；
after each iteration of the separation matrix, it is normalized:
。
5. the unmanned aerial vehicle communication signal processing method based on blind source separation according to any one of claims 1 to 4, wherein the set frame format employs:
pilot sequence 0 uses 4 pseudo codes 256 long as the synchronization header;
the pilot frequency sequence 1 adopts 4 pseudo codes with 256 lengths for indicating the format of a data transmission frame, and carries out self-adaptive multi-rate communication;
the pilot sequence 2 adopts 2 pseudo codes with 128 lengths, and is used for accurately correcting the phase offset of each section of data packet of useful signals obtained by signal blind source separation under the condition of low signal-to-noise ratio sensitivity.
6. The blind source separation based drone communication signal processing method of claim 5 wherein the data structure of the data transmission time slot has a total of 13 bursts, wherein:
burst 0 consists of RU segment, pilot sequence 0 and pilot sequence 1;
burst 1 through burst 12 are composed of 3 parts: data segment 0, pilot sequence 2, data segment 1.
7. A blind source separation-based unmanned aerial vehicle communication signal processing system applied to realizing the blind source separation-based unmanned aerial vehicle communication signal processing method according to any one of claims 1 to 6, wherein the system comprises a data transmission processing unit and a data reception processing unit;
the data transmission processing unit is used for carrying out coding, interleaving and rate matching perforation on input information, converting the input information into baseband data symbols after spread spectrum and data mapping treatment, and transmitting the baseband data symbols by an antenna after radio frequency power amplification and filtering;
the data receiving and processing unit is used for receiving radio frequency signals by adopting an upper path of antenna and a lower path of antenna, performing LNA low noise amplification and filtration on the two paths of signals, performing down mixing treatment to obtain intermediate frequency signals, performing blind source separation on the two paths of intermediate frequency signals at the same time by adopting frequency hopping frequency locked by a phase-locked loop, performing correlation capturing on the generated separated signals respectively, performing despreading demodulation processing on one path of signals with correlation peaks exceeding a gate valve, outputting pre-information for decoding and correcting errors after frequency offset correction and phase correction, and outputting received data.
8. The unmanned aerial vehicle communication signal processing device based on blind source separation is applied to the unmanned aerial vehicle communication signal processing method based on blind source separation according to any one of claims 1 to 6, and is characterized in that the unmanned aerial vehicle communication signal processing device based on blind source separation comprises a transmitting end and a receiving end;
the transmitting end encodes, interweaves and rate matching punches the input information, and then changes the input information into baseband data symbols after spread spectrum and data mapping processing, and the baseband data symbols are transmitted by an antenna after radio frequency power amplification and filtering;
the receiving end adopts an upper path of antenna and a lower path of antenna to receive radio frequency signals, after LNA low noise amplification and filtration are carried out on the two paths of signals, intermediate frequency signals are obtained through down mixing, the mixed local frequency adopts frequency hopping frequency locked by a phase-locked loop, blind source separation is carried out on the two paths of intermediate frequency signals at the same time, the generated separated signals are respectively subjected to correlation capturing, one path of signal with a correlation peak exceeding a gate valve is selected for despreading demodulation processing, and after frequency offset correction and phase correction, pre-information is output for decoding and error correction, and received data is output.
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