CN110519003B - Unmanned aerial vehicle uplink and downlink communication link identification method and device based on signal characteristic difference - Google Patents
Unmanned aerial vehicle uplink and downlink communication link identification method and device based on signal characteristic difference Download PDFInfo
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Abstract
The invention discloses a method for identifying an uplink communication link and a downlink communication link of an unmanned aerial vehicle based on signal characteristic difference, which comprises the following sequential steps: an antenna of the broadband receiver receives electromagnetic wave signals and performs amplification, filtering, mixing and intermediate frequency processing; the processed signal is subjected to carrier frequency measurement, pulse detection is carried out by combining narrow-band filtering, and pulse repetition period and pulse width parameters of the signal are measured so as to predict whether the unmanned aerial vehicle signal exists or not; judging whether unmanned aerial vehicle signals exist according to the carrier frequency, the pulse repetition period and the pulse width parameters obtained through measurement; if no unmanned aerial vehicle signal is judged, returning to the previous step for continuous detection, and if the unmanned aerial vehicle signal is preliminarily judged, carrying out uplink and downlink signal detection judgment in the next step; and distinguishing an uplink signal and a downlink signal of the unmanned aerial vehicle according to the bandwidth and the frequency hopping rate of the unmanned aerial vehicle signal. The invention has strong concealment, does not generate interference and can work all day and all weather.
Description
Technical Field
The invention relates to the technical field of electronic countermeasure, in particular to an unmanned aerial vehicle uplink and downlink communication link identification method based on signal characteristic difference.
Background
In recent years, various civil unmanned aerial vehicles are developing hot flashes worldwide along with technological breakthroughs of unmanned aerial vehicles in low cavitation and miniaturization. Unmanned aerial vehicle's application demand in aspects such as commodity circulation transportation, geological survey, movie & TV shooting, agriculture and forestry operation, patrol monitoring, emergency rescue is growing fast, attracts more and more scientific enterprises to participate in emerging unmanned aerial vehicle industry. Meanwhile, consumer-grade civil micro unmanned aerial vehicle is generally accepted and touted by the masses due to the characteristics of simple operation, high price, strong freshness, entertainment and the like, and purchasing users are increasing day by day. In the future, the application field of unmanned aerial vehicles is necessarily wider, and the market cannot be estimated.
However, with the development of industry, accidents related to unmanned aerial vehicles are also frequently seen in the field of view of people. In 2017, 4 months, a plurality of unmanned aerial vehicle interference flight events occur in a half month of an adult airport, and social heat is induced; in 2015, law enforcement in the united states discovered that an unmanned plane intruded into the white womb; in addition, there is a phenomenon of criminal activity using unmanned aerial vehicles abroad, and the above various actions of illegally operating unmanned aerial vehicles are all "black fly" in nature. The black flying unmanned aerial vehicle brings about the concern and worry of various communities, and the need for effective management and control of the unmanned aerial vehicle is urgent. However, china is relatively lagged in managing the unmanned aerial vehicle industry, and the method is mainly characterized in that: the operator lacks the training of system, and the related laws of standard unmanned aerial vehicle development, sales and use are imperfect, and the detection monitoring management technical means is immature.
In the face of a 'black flying' unmanned plane, not only the specification and policy are set to realize 'no flying', but also the countermeasures are researched on the technical level to realize 'no flying'. The unmanned aerial vehicle is countered, and the communication link of the unmanned aerial vehicle is destroyed by mainly relying on high-power electromagnetic interference at present. The communication link of the unmanned aerial vehicle comprises an uplink and a downlink, wherein the uplink signal is mainly used for information transmission such as flight control instructions and the like, and the downlink signal is mainly used for information transmission such as unmanned aerial vehicle state parameters and video images and the like. Especially for high threat level military drones, the uplink and downlink operating frequencies are not consistent. In the process of countering the unmanned aerial vehicle, the key point of high-power electromagnetic interference is an uplink control command signal, so as to cut off the operation and control of threat personnel on the unmanned aerial vehicle. If the accurate sweep frequency suppression interference cannot be carried out on the working frequency point of the uplink, the equivalent radiation power of the electromagnetic interference signal reaching the unmanned aerial vehicle is insufficient, and the effect of countering the unmanned aerial vehicle cannot be achieved. Therefore, the first problem of the technical means for electromagnetic interference suppression is to sense the uplink and downlink signals of the unmanned aerial vehicle and distinguish the uplink and downlink signals.
At present, the technical method for detecting and positioning the unmanned aerial vehicle mainly comprises an active radar detection positioning method, a photoelectric detection recognition tracking method and a passive sound detection positioning method, wherein the methods detect radar target echoes of the unmanned aerial vehicle, infrared image characteristics of the unmanned aerial vehicle and sounds emitted by the unmanned aerial vehicle respectively, and cannot detect and judge uplink and downlink signals of the unmanned aerial vehicle, so that a high-power electromagnetic interference countering unmanned aerial vehicle cannot be guided accurately, only can be emitted circularly according to 5-6 frequency points where the unmanned aerial vehicle can work, electromagnetic interference is carried out at the same time, interference signal power is difficult to concentrate, and interference countering effect is not ideal.
Disclosure of Invention
In order to solve the problems, the invention aims to provide a method for identifying an uplink and downlink communication link of a unmanned aerial vehicle based on signal characteristic difference, which utilizes a radio detection means to identify the uplink and downlink communication link of the unmanned aerial vehicle under a non-cooperative condition, guides accurate interference, blocks an uplink unmanned aerial vehicle control signal and provides conditions for the next unmanned aerial vehicle to take over; the small unmanned aerial vehicle refers to a non-cooperative target which does not provide identity information and unknown transmission information under the non-cooperative receiving condition.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
a method for identifying an uplink communication link and a downlink communication link of an unmanned aerial vehicle based on signal characteristic differences comprises the following steps:
s1, an antenna of a broadband receiver receives electromagnetic wave signals and performs amplification, filtering, mixing and intermediate frequency processing;
s2, carrying out carrier frequency CF measurement on the signal processed in the step S1, carrying out pulse detection by combining with narrow-band filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal so as to pre-judge whether the unmanned aerial vehicle signal exists or not;
s3, judging whether unmanned aerial vehicle signals exist or not
According to the carrier frequency CF, the pulse repetition period PRI and the pulse width PW parameters obtained by measurement in the step S2, whether unmanned aerial vehicle signals exist or not can be judged; if no unmanned aerial vehicle signal is judged, returning to the step S2 for continuous detection, and if the unmanned aerial vehicle signal is preliminarily judged, carrying out uplink and downlink signal detection judgment in the step S4;
s4, carrying out uplink and downlink signal detection and judgment on unmanned aerial vehicle signals
After the unmanned aerial vehicle signal is identified, accurately measuring and estimating the bandwidth and the frequency hopping rate of the unmanned aerial vehicle signal;
first, the bandwidth of the signal is estimated accurately, the following operations are performed:
1) Power spectrum
Dividing data x (N) with length of N (n=0, 1, L, N-1) into L segments, each segment having M data, wherein L is the length of each data segment when not overlapped, L=N/M, and ith segment data x i (n) =x (n+im-M), and then a window function ω (n) is added to each data segment to find a periodic chart of each segment, the periodic chart of the i-th segment
Wherein, U is a normalization factor,
approximate mutual uncorrelation between the periodograms of each segment, and final power spectrum estimation is as follows
2) Wavelet decomposition to obtain smooth power spectrum
Performing wavelet decomposition on the estimated power spectrum, separating a detail part from a rough part of the signal, extracting coefficients of the rough part, reconstructing the power spectrum by the coefficients, and eliminating high-frequency details in a power spectrum waveform to obtain a smooth waveform;
3) Computing a mobile covariance of a smoothed power spectrum
After obtaining a smooth power spectrum, extracting a starting point and an ending point of a passband to estimate the bandwidth; calculating a mobile covariance:
r(k,k+1)＝cov(d(k),d(k+1))
(k＝1,2,L,N-1) (15)
wherein: d (k) is a reconstruction coefficient of the rough part extracted after wavelet decomposition, all data points are traversed by using the method, and the movement covariance between any adjacent points is obtained; the value of each p points is changed, so the following formula is adopted for calculation
Where the choice of k=1, 1+p, l, n-p, p values is determined by the nature of the actual data point, and when p=1, the equivalent is the formula;
4) Estimating bandwidth
The positions a and b where the maximum 2 mobile covariance values are located are extracted, and are respectively regarded as a starting point and a cut-off point of the bandwidth, and the difference value |b-a-1| is regarded as an estimated bandwidth;
setting the circulation times, repeating the steps 1) to 4) for L segments of data with the length of N, calculating L bandwidths, then averaging, and obtaining the statistical average value of the estimated bandwidths, namely the accurate bandwidth estimated value of the unmanned aerial vehicle signal;
then, the frequency hopping rate is accurately estimated, and the following operations are performed: and analyzing the signal by utilizing short-time Fourier transform to obtain time-frequency representation thereof, extracting edge information of the time-frequency representation by utilizing wavelet transform, and estimating the frequency hopping rate by utilizing spectrum analysis.
Further, in the step S2, the method for detecting and predicting the unmanned aerial vehicle signal includes the following steps:
s2a, carrier frequency CF estimation
Firstly, carrying out coarse estimation on signal carrier frequency by adopting spectrum peak value detection operation, and determining a frequency range; then the spectrum is further refined by using ZFFT; finally, eliminating frequency offset brought by a fence effect by using a quadratic interpolation method;
for the signal processed in the step S1, firstly judging whether a carrier exists or not through threshold detection, and roughly estimating the position of a peak value; in x n Representing baseband signals of unmanned aerial vehicle, and setting signal sequences of unmanned aerial vehicle to be estimated as
Wherein A is 0 And
a is set for the amplitude and the initial phase of the carrier signal respectively 0 ＝1、/>
f 0 And f s The carrier frequency and the data sampling rate of the signal to be estimated are respectively, N is the number of sampling points, r (N) is Gaussian white noise, and the variance is delta r ；
First, the signal is subjected to FFT to obtain the spectrum as follows:
X(k)＝FFT{x(n)},k＝0,1,...N-1 (2)
then, the ratio of the signal intensity of each frequency point to the average value of the signal intensities of the adjacent frequency points is calculated as follows:
wherein the method comprises the steps of
m is the number of left and right neighbors, if the ratio q j If the frequency point value exceeds the set threshold value T, judging that a carrier signal exists, and then taking the frequency point value and the adjacent next largest frequency point value as a carrier frequency range; let the corresponding frequency of this frequency point and its adjacent next largest frequency point be f1 and f2 respectively, let f1 < f2, then have |f 1 -f 2 |＝f s /N；[f1，f2]Is the carrier frequency range of the signal to be estimated;
s2b, ultra-narrow low-pass filtering is carried out, and the specific processing process is as follows:
the FFT operation is used for obtaining the frequency range of [ f1, f2], and the local oscillator complex signal frequency of complex modulation frequency shift is calculated as
The frequency shift signal obtained by complex modulation is shown as (6), the original frequency f e When the zero frequency is shifted, the original frequency points f1 and f2 are positioned at the two sides of the zero frequency and are symmetrical about the zero frequency;
the signal is subjected to low-pass filtering by utilizing integral operation, meanwhile, data extraction is combined, data segmentation summation is carried out, and the average value of each segment of data is taken to form an extracted signal sequence; setting the extraction rate as D, taking the average value of D data points as an extraction result, and simultaneously realizing low-pass filtering and data extraction in a segmented summation and average value extraction mode; the sampling rate of the extracted signal becomes f' s ＝f s and/D, filtering and extracting results are as follows:
finally, envelope detection and pulse detection
After the carrier frequency of the signal is obtained, the corresponding signal is extracted through an envelope detection method, so that pulse detection and parameter measurement are realized; the envelope detection step is similar to the previous step, namely, firstly, down-converting a signal to zero intermediate frequency, and then, simultaneously realizing low-pass filtering and data extraction in a mode of summing and averaging by sections, thereby realizing narrow-band filtering and data volume reduction; i.e. f in (5) e Is changed into
Then repeating the steps of (6)) and (7), the result obtained being expressed as x 2 (n) taking the envelope, i.e. for x 2 (n) taking the modulus to obtain x 3 (n)＝|x 2 (n)|；
S2c, pulse detection is carried out on the envelope waveform and relevant parameter values are measured
The pulse detection mainly comprises the steps of detecting rising edges and falling edges of an envelope waveform, and taking an intersection point of the envelope and a threshold as the rising edges or the falling edges by adopting a dynamic threshold detection method; the dynamic threshold method divides the envelope signal into M segments of signals, each segment of signal being x 4m (n), m=1,.. i.e. dividing the signal into a plurality of uniform windows, obtaining local maximum x of signals in each window max (m)＝max(x 4m (n)) and then generates a corresponding threshold g m ＝αx max (m)；
The rising and falling edge positions of the pulses obtained within the single detection time width are respectively n up And n down The rising edge and the falling edge of the pulse width adopt a linear fitting interpolation mode to obtain the rising edge and the falling edge positions; the specific algorithm is as follows:
wherein t is up 、t down B is respectively corresponding to the time of the rising edge and the falling edge d For decimating the bandwidth after filtering; the rising edge or the falling edge is required to be shifted and adjusted according to the condition of the negative pulse width value; judging K up The sum of rising edges K down After each falling edge, the time difference between adjacent rising edges or falling edges isPulse repetition periods, i.e.
PRI＝t up (k+1)-t up (k),k＝1,2,...,K (10)
Subtracting the rising edge time from the paired falling edge time to obtain pulse width, i.e
PW＝t down (k)-t up (k),k＝1,2,...,K (11)
In addition, the rising edge of each pulse can be taken as its arrival time;
after the pulse related parameters are estimated, unmanned aerial vehicle signals can be identified and extracted according to the measured signal carrier frequency CF, the pulse repetition period PRI and the pulse width PW parameters.
Further, in the step S4, the step of accurately estimating the frequency hopping rate is as follows:
1) Calculating a short-time Fourier transform STFT of the received signal x (t) x ；
First assume a jump speed estimate
As known a priori, the time-frequency representation of the short-time fourier transform of the received signal:
where h (τ -t) is a window function, where (τ -t) e [ - Δ/2, Δ/2]When 1, the other are 0, set as
To ensure that there is at most one frequency jump in the time region covered by the window function;
2) Extraction of STFT x Time-frequency ridge line f of (2) x (t)；
To perform a secondary process on the time-frequency representation of the FH signal, its time-frequency ridge needs to be extracted, as in equation (18):
3) Calculating f x (t) wavelet transform W (a, t);
using wavelet transforms
Wherein a is a scale parameter;
is a Haar wavelet;
setting the dimension a to be equal to the width delta of the time-frequency window, and ensuring that at most one frequency jump exists in the coverage time range of the wavelet function;
4) Calculating the amplitude sequence Abs [ W (a, t) ];
computing the amplitude sequence Abs of W (a, t) [ W (a, t) ]]The amplitude sequence is a pseudo-random sequence, i.e. frequency hopping occurs at nT H (n.epsilon.Z) time;
5) Calculating a fourier transform FFT of Abs [ W (a, t) ];
6) The interval of the spectral peaks, i.e. the corresponding estimation of the jump rate, is detected: the wavelet transformation amplitude sequence of the time-frequency ridge line has discrete spectral lines at the jump rate position, and the accurate estimation of the jump speed can be realized by detecting the discrete spectral lines.
The unmanned aerial vehicle uplink and downlink communication link identification device based on signal characteristic difference comprises a broadband receiver for receiving electromagnetic wave signals and amplifying, filtering, mixing and intermediate frequency processing the signals; the unmanned aerial vehicle signal detection pre-judging module is used for carrying out carrier frequency CF estimation on the signal processed by the broadband receiver, carrying out pulse detection by combining with narrowband filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal; the unmanned aerial vehicle signal identification module is used for identifying pulse repetition period PRI and pulse width PW parameters of the unmanned aerial vehicle signals; and the uplink and downlink signal detection judging module is used for accurately measuring and estimating the bandwidth and the frequency hopping rate of the unmanned aerial vehicle.
By adopting the technical scheme, the invention has the following advantages:
according to the method for identifying the uplink and downlink communication links of the unmanned aerial vehicle based on the signal characteristic difference, a radio detection technology is adopted to identify the uplink and downlink communication links of the unmanned aerial vehicle under a non-cooperative condition, electromagnetic signals are not radiated by the radio detection technology, and the wave path of signal transmission is half of the wave path of radar detection, so that the unmanned aerial vehicle has a longer detection distance; the radio detection does not actively emit electromagnetic waves, has strong concealment, does not generate interference, can work all day and all weather, and simultaneously has lower cost compared with an active detection method; firstly, judging whether the unmanned aerial vehicle signal exists according to the fact that the unmanned aerial vehicle signal has certain inherent characteristics in the time domain and the frequency domain and the difference with other interference signals in the same frequency band, then, identifying uplink and downlink according to the characteristic difference of the uplink and downlink signals of the unmanned aerial vehicle, measuring the frequency range of the uplink and downlink signals, and further, concentrating dominant power to pertinently suppress and interfere the uplink signal of the unmanned aerial vehicle. The small unmanned aerial vehicle refers to a 'black flying' civil unmanned aerial vehicle, a wireless signal radiation source and the like which are submerged in important facilities such as military areas, test sites, command posts, security areas and the like within a range of 2km, and the weight of the unmanned aerial vehicle is usually not more than 15kg, and the take-off weight is not more than 25kg.
Drawings
FIG. 1 is a flow chart of a method for identifying an uplink and downlink communication link of an unmanned aerial vehicle based on signal characteristic differences;
FIG. 2 is a waveform and spectrogram of an uplink signal of the unmanned aerial vehicle;
fig. 3 is a waveform and spectrogram of a downstream signal of the unmanned aerial vehicle;
fig. 4 is a time-frequency distribution diagram of an uplink signal of the unmanned aerial vehicle;
fig. 5 is a time-frequency distribution diagram of a downstream signal of the unmanned aerial vehicle.
Detailed Description
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings and examples.
As shown in fig. 1, the method for identifying the uplink and downlink communication links of the unmanned aerial vehicle based on signal characteristic difference comprises the following specific steps:
s1, an antenna of a broadband receiver receives electromagnetic wave signals and performs amplification, filtering, mixing and intermediate frequency processing; so as to facilitate the next detection and pre-judgment of the signal;
s2, carrying out carrier frequency CF measurement on the signal processed in the step S1, carrying out pulse detection by combining with narrow-band filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal so as to pre-judge whether the unmanned aerial vehicle signal exists or not; the method comprises the following specific steps:
s2a, carrier frequency CF estimation
Firstly, carrying out coarse estimation on signal carrier frequency by adopting spectrum peak detection operation to determine a frequency range, then utilizing ZFFT (ZFFT is also called Zoom-FFT, is called thinned fast Fourier transform, is also called band selection fast Fourier transform, is a complex modulation spectrum thinning method) to further refine a frequency spectrum, greatly reducing data quantity while improving frequency resolution, and finally utilizing a quadratic interpolation method to eliminate frequency offset brought by a fence effect;
for the signal processed in the step S1, firstly judging whether a carrier exists or not through threshold detection, and roughly estimating the position of a peak value; in x n Representing baseband signals of unmanned aerial vehicle, and setting signal sequences of unmanned aerial vehicle to be estimated as
Wherein A is 0 And
the amplitude and the initial phase of the carrier signal are respectively, and A is set for simplifying operation 0 ＝1、/>
f 0 And f s The carrier frequency and the data sampling rate of the signal to be estimated are respectively, N is the number of sampling points, r (N) is Gaussian white noise, and the variance is delta r ；
First, the signal is subjected to FFT to obtain the spectrum as follows:
X(k)＝FFT{x(n)},k＝0,1,...N-1 (2)
then, the ratio of the signal intensity of each frequency point to the average value of the signal intensities of the adjacent frequency points is calculated as follows:
wherein the method comprises the steps of
m is the number of left and right neighbors, taking the actual adjacent frequency interval as a reference, and generally taking the frequency range of about 50 kHz; if the ratio q j If the frequency point value exceeds the set threshold value T, and the T is 0.28-0.3, judging that a carrier signal exists, and then taking the frequency point value and the adjacent next-largest frequency point value as a carrier frequency range; let the corresponding frequency of this frequency point and its adjacent next largest frequency point be f1 and f2 respectively, let f1 < f2, then have |f 1 -f 2 |＝f s N; due to the presence of the "fence effect", the actual frequency of the signal is between f1 and f2, i.e. [ f1, f2]]Is the carrier frequency range of the signal to be estimated;
s2b, ultra-narrow low-pass filtering is carried out, and the specific processing process is as follows:
the FFT operation is used for obtaining the frequency range of [ f1, f2], and the local oscillator complex signal frequency of complex modulation frequency shift is calculated as
The frequency shift signal obtained by complex modulation is shown as (6), the original frequency f e When the zero frequency is shifted, the original frequency points f1 and f2 are positioned at the two sides of the zero frequency and are symmetrical about the zero frequency;
on the basis of not influencing the filtering effect, in order toThe operation is simplified, the efficiency is improved, the signal is subjected to low-pass filtering by utilizing integral operation, meanwhile, data extraction is combined, data are summed in a segmented mode, and the average value of each segment of data is taken to form an extraction signal sequence; setting the extraction rate as D, taking the average value of D data points as an extraction result, and simultaneously realizing low-pass filtering and data extraction in a segmented summation and average value extraction mode, thereby improving the processing speed; the sampling rate of the extracted signal becomes f' s ＝f s and/D, filtering and extracting results are as follows:
finally, envelope detection and pulse detection
After the carrier frequency of the signal is obtained, the corresponding signal is extracted through an envelope detection method, so that pulse detection and parameter measurement are realized; the envelope detection step is similar to the previous step, namely, firstly, down-converting a signal to zero intermediate frequency, and then, simultaneously realizing low-pass filtering and data extraction in a mode of summing and averaging by sections, thereby realizing narrow-band filtering and data volume reduction; i.e. f in (5) e Is changed into
Then repeating the steps of (6)) and (7), the result obtained being expressed as x 2 (n) taking the envelope, i.eFor x 2 (n) taking the modulus to obtain x 3 (n)＝|x 2 (n)|；
S2c, pulse detection is carried out on the envelope waveform and relevant parameter values are measured
The pulse detection mainly comprises the steps of detecting rising edges and falling edges of an envelope waveform, and taking an intersection point of the envelope and a threshold as the rising edges or the falling edges by adopting a dynamic threshold detection method; the dynamic threshold method divides the envelope signal into M segments of signals, each segment of signal being x 4m (n), m=1,.. i.e. dividing the signal into a plurality of uniform windows, obtaining local maximum x of signals in each window max (m)＝max(x 4m (n)) and then generates a corresponding threshold g m ＝αx max (m), generally taking α=0.5;
the rising and falling edge positions of the pulses obtained within the single detection time width are respectively n up And n down In order to further improve the measurement accuracy of the pulse width, a linear fitting interpolation mode is adopted on the rising edge and the falling edge of the pulse width, so that the rising edge position and the falling edge position are more accurately obtained; the specific algorithm is as follows:
wherein t is up 、t down B is respectively corresponding to the time of the rising edge and the falling edge d For decimating the bandwidth after filtering; in practice, the rising edge and the falling edge may be paired with errors, so that the pulse width is negative, and therefore, shift adjustment is required to be performed on the rising edge or the falling edge according to the condition of the negative pulse width; judging K up The sum of rising edges K down After each falling edge, the time difference between adjacent rising edges or falling edges is the pulse repetition period, namely
PRI＝t up (k+1)-t up (k),k＝1,2,...,K (10)
Subtracting the rising edge time from the paired falling edge time to obtain pulse width, i.e
PW＝t down (k)-t up (k),k＝1,2,...,K (11)
In addition, the rising edge of each pulse may be taken as its arrival time;
after the pulse related parameters are estimated, unmanned aerial vehicle signals can be identified and extracted according to the measured signal carrier frequency CF, the pulse repetition period PRI and the pulse width PW parameters.
S3, judging whether unmanned aerial vehicle signals exist or not
Judging whether unmanned aerial vehicle signals exist according to the carrier frequency, the pulse repetition period and the pulse width parameters obtained by measurement in the steps S1 and S2; if no unmanned aerial vehicle signal is judged, returning to the step S2 for continuous detection, and if the unmanned aerial vehicle signal is preliminarily judged, carrying out uplink and downlink signal detection judgment in the step S4;
if there is a pulse repetition period PRI of 14ms, the pulse width PW is 1ms, 2ms, or 10ms; the existence of signals with the frequency spectrum center CF of 2.4065GHz or 5.8GHz can be judged preliminarily that unmanned aerial vehicle signals exist;
s4, detecting and judging uplink and downlink signals
After the unmanned aerial vehicle signal is identified according to the step S3, the bandwidth and the frequency hopping rate of the unmanned aerial vehicle signal need to be accurately measured and estimated;
first, the bandwidth of the signal is estimated accurately, the following operations are performed:
1) Power spectrum
Dividing data x (N) with length of N (n=0, 1, L, N-1) into L segments, each segment having M data, wherein L is the length of each data segment when not overlapped, L=N/M, and ith segment data x i (n) =x (n+im-M), and then a window function ω (n) is added to each data segment to find a periodic chart of each segment, the periodic chart of the i-th segment
Wherein, U is a normalization factor,
approximate mutual uncorrelation between the periodograms of each segment, and final power spectrum estimation is as follows
2) Wavelet decomposition to obtain smooth power spectrum
Although the power spectrum estimated above is much smoother, but still contains some high-frequency components, the wavelet transformation can extract high-frequency details in the signal, so that the estimated power spectrum is subjected to wavelet decomposition to separate the detail part and the rough part of the signal, coefficients of the rough part are extracted, and the power spectrum is reconstructed by the coefficients, so that the high-frequency details in the power spectrum waveform can be eliminated, and a smooth waveform can be obtained;
3) Computing a mobile covariance of a smoothed power spectrum
After obtaining the smooth power spectrum, the starting point and the ending point of the passband are required to be extracted to estimate the bandwidth, and the mobile covariance is calculated:
r(k,k+1)＝cov(d(k),d(k+1))
(k＝1,2,L,N-1) (15)
wherein: d (k) is a reconstruction coefficient of the rough part extracted after wavelet decomposition, all data points are traversed by using the method, and the movement covariance between any adjacent points is obtained; the value of each p points is changed, so the following formula is adopted for calculation
Where the choice of k=1, 1+p, l, n-p, p values is determined by the nature of the actual data point, and when p=1, the equivalent is the formula;
4) Estimating bandwidth
The positions a and b where the maximum 2 mobile covariance values are located are extracted, and are respectively regarded as a starting point and a cut-off point of the bandwidth, and the difference value |b-a-1| is regarded as an estimated bandwidth; setting the circulation times, and repeating the steps to obtain a statistical average value of the estimated bandwidth, namely an accurate bandwidth estimated value of the unmanned aerial vehicle signal;
then, the frequency hopping rate is estimated accurately
The estimation of the frequency hopping rate is to analyze the signal by utilizing short-time Fourier transform to obtain the time-frequency representation thereof, extract the edge information of the time-frequency representation by utilizing wavelet transform, and further estimate the frequency hopping rate by utilizing spectrum analysis; the detailed steps are as follows:
1) Calculating a short-time Fourier transform STFT of the received signal x (t) x ；
First assume a jump speed estimate
As known a priori, the time-frequency representation of the short-time fourier transform of the received signal:
where h (τ -t) is a window function, where (τ -t) e [ - Δ/2, Δ/2]When 1, the other are 0, set as
To ensure that there is at most one frequency jump in the time region covered by the window function;
2) Extraction of STFT x Time-frequency ridge line f of (2) x (t)；
To perform a secondary process on the time-frequency representation of the FH signal, its time-frequency ridge needs to be extracted, as in equation (18):
3) Calculating f x The wavelet transform W (a, t) of (t);
to take into account the recognition of the pulse edges, a wavelet transform is used,
wherein a is a scale parameter;
is a Haar wavelet;
setting the dimension a to be equal to the width delta of the time-frequency window, and ensuring that at most one frequency jump exists in the coverage time range of the wavelet function;
4) Calculating the amplitude sequence Abs [ W (a, t) ];
computing the amplitude sequence Abs of W (a, t) [ W (a, t) ]]The amplitude sequence is a pseudo-random sequence, i.e. frequency hopping occurs at nT H Time (n.epsilon.Z) and at nT H The time (n epsilon Z) does not necessarily have frequency hopping, and whether the hopping occurs or not is random.
5) Calculating a fourier transform FFT of Abs [ W (a, t) ];
6) Detecting the interval of spectrum peak, namely the estimation of corresponding jump speed, wherein the wavelet transformation amplitude sequence of the time-frequency ridge line has discrete spectrum lines at the jump speed position, and the accurate estimation of the jump speed can be realized by detecting the discrete spectrum lines.
The uplink signal and the downlink signal of the unmanned aerial vehicle are distinguished according to two characteristic quantities, namely a signal bandwidth BW and a frequency hopping rate HP: the uplink signal is an uplink signal, and has small general bandwidth of 1.2 MHz-2.8 MHz, narrow pulse width, frequency hopping and faster frequency hopping rate; the bandwidth of the downlink signal is large and is 9.8 MHz-10.2 MHz, the pulse width is wider, the frequency is stable in the bandwidth, and the frequency hopping rate is slower.
Fig. 2 is a waveform and spectrogram of a remote control signal (uplink signal) of one embodiment of the unmanned aerial vehicle, according to the method for identifying an uplink communication link and a downlink communication link of the unmanned aerial vehicle based on signal characteristic difference of the present invention, the periodic pulse characteristics of the remote control signal can be extracted, and the pulse repetition period is estimated to be about 14ms, and the pulse width is estimated to be about 1ms and 2.17ms respectively.
Fig. 3 is a waveform and a spectrogram of a signal (downlink signal) of one embodiment of an unmanned aerial vehicle, and according to the method for identifying an uplink communication link and a downlink communication link of an unmanned aerial vehicle based on signal characteristic difference of the present invention, periodic pulse characteristics of the signal can be extracted, the downlink signal of the unmanned aerial vehicle also has obvious periodic pulse signal characteristics, the pulse repetition period is about 14ms, the pulse width is about 10ms, and the spectrum center is about 2.4065GHz.
Fig. 4 and fig. 5 are time-frequency distribution diagrams of an uplink signal and a downlink signal of an unmanned aerial vehicle respectively, and according to the method for identifying an uplink communication link and a downlink communication link of an unmanned aerial vehicle based on signal characteristic differences, the time-frequency diagram of the uplink signal can be obtained, and the center frequency point of the uplink signal can be known to continuously hop, and the frequency hopping point changes regularly; according to the unmanned aerial vehicle uplink and downlink communication link identification method based on the signal characteristic difference, the frequency hopping rate of an uplink signal is 2.14, the frequency hopping rate of a downlink signal is 0.71, the signal bandwidth of the uplink is 1.2MHz, and the signal bandwidth of the downlink is 10MHz. Therefore, according to the bandwidth and the frequency hopping rate of the signal to be estimated, a decision threshold is flexibly designed, so that the uplink signal and the downlink signal can be well distinguished and identified.
The invention also discloses an unmanned aerial vehicle uplink and downlink communication link identification device based on the signal characteristic difference, which comprises a broadband receiver for receiving electromagnetic wave signals and amplifying, filtering, mixing and intermediate frequency processing the signals; the unmanned aerial vehicle signal detection pre-judging module is used for carrying out carrier frequency CF estimation on the signal processed by the broadband receiver, carrying out pulse detection by combining with narrowband filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal; the unmanned aerial vehicle signal identification module is used for identifying pulse repetition period PRI and pulse width PW parameters of the unmanned aerial vehicle signals; and the uplink and downlink signal detection judging module is used for accurately measuring and estimating the bandwidth and the frequency hopping rate of the unmanned aerial vehicle.
The present invention is not limited to the above-mentioned embodiments, but can be modified in various ways without departing from the spirit and scope of the invention.
Claims (2)
1. A method for identifying an uplink communication link and a downlink communication link of an unmanned aerial vehicle based on signal characteristic differences is characterized by comprising the following steps: which comprises the following steps:
s1, an antenna of a broadband receiver receives electromagnetic wave signals and performs amplification, filtering, mixing and intermediate frequency processing;
s2, carrying out carrier frequency CF measurement on the signal processed in the step S1, carrying out pulse detection by combining narrowband filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal; the method comprises the following steps:
s2a, carrier frequency CF estimation
Firstly, carrying out coarse estimation on the signal carrier frequency by adopting spectrum peak value detection operation to determine a frequency range; then the spectrum is further refined by using ZFFT; finally, eliminating frequency offset brought by the fence effect by using a secondary interpolation method;
for the signals processed in the step S1, firstly judging whether the carrier exists or not through threshold detection, and roughly estimating the position of the peak value; in x n Representing baseband signals of unmanned aerial vehicle, and setting signal sequences of unmanned aerial vehicle to be estimated as
Wherein A is 0 And
a is set for the amplitude and the initial phase of the carrier signal respectively 0 ＝1、/>
f 0 And f s The carrier frequency and the data sampling rate of the unmanned aerial vehicle signal to be estimated are respectively, N is the number of sampling points, r (N) is Gaussian white noise, and the variance is delta r ；
First, the aforementioned signal is subjected to FFT conversion, and the spectrum thereof is obtained as follows:
X(k)＝FFT{x(n)},k＝0,1,...N-1 (2)
then, the ratio of the signal intensity of each frequency point to the average value of the signal intensities of the adjacent frequency points is calculated as follows:
wherein the method comprises the steps of
m is the number of left and right neighbors, if the ratio q j If the frequency point value exceeds the set threshold value T, judging that a carrier signal exists, and then taking the frequency point value and the adjacent next largest frequency point value as a carrier frequency range; let the corresponding frequency of this frequency point and its adjacent next largest frequency point be f1 and f2 respectively, let f1 < f2, then have |f 1 -f 2 |＝f s /N；[f1，f2]The signal carrier frequency range of the unmanned aerial vehicle to be estimated;
s2b, ultra-narrow low-pass filtering is carried out, and the specific processing process is as follows:
the carrier frequency ranges [ f1, f2] obtained by the FFT operation in the step S2a calculate the local oscillation complex signal frequency of the complex modulation frequency shift as
The frequency shift signal obtained by complex modulation is shown as (6), the original frequency f e When the zero frequency is shifted, the original frequency points f1 and f2 are positioned at the two sides of the zero frequency and are symmetrical about the zero frequency;
the low-pass filtering is carried out on the signals by utilizing integral operation, meanwhile, data extraction is combined, data segmentation summation is carried out, and the average value of each segment of data is taken to form an extraction signal sequence; the extraction rate is set as D, the average value of D data points is taken as an extraction result, and the average value is obtained by sectional summation in the same wayLow-pass filtering and data extraction are realized; the sampling rate of the signal becomes f 'after extraction' s ＝f s and/D, filtering and extracting results are as follows:
finally, envelope detection and pulse detection
After the carrier frequency of the signals is obtained, the corresponding signals are extracted through an envelope detection method, so that pulse detection and parameter measurement are realized; the step of envelope detection is similar to the previous step, namely, the signals are firstly subjected to down-conversion to zero intermediate frequency, then low-pass filtering and data extraction are simultaneously realized in a mode of sectionally summing and averaging, and narrowband filtering and data volume reduction are realized; i.e. f in (5) e Is changed into
Then repeating the steps of (6) and (7), and the obtained result is expressed as x 2 (n) taking the envelope, i.e. for x 2 (n) taking the modulus to obtain x 3 (n)＝|x 2 (n)|；
S2c, pulse detection is carried out on the envelope waveform and relevant parameter values are measured
The pulse detection is to detect rising edges and falling edges of the envelope waveform, and adopts a dynamic threshold detection method, and takes the intersection point of the envelope and the threshold as the rising edge or the falling edge; the dynamic threshold method divides the envelope signal into M segments of signals, each segment of signal being x 4m (n), m=1,.. i.e. dividing the signal into a plurality of uniform windows, obtaining local maximum x of signals in each window max (m)＝max(x 4m (n)) and then generates a corresponding threshold g m ＝αx max (m)；
The rising and falling edge positions of the pulses obtained within the single detection time width are respectively n up And n down The rising edge and the falling edge of the pulse width adopt a linear fitting interpolation mode to obtain the rising edge and the falling edge positions; the specific algorithm is as follows:
wherein t is up 、t down B is respectively corresponding to the time of the rising edge and the falling edge d For decimating the bandwidth after filtering; according to the condition of the negative pulse width value, carrying out shift adjustment on the rising edge or the falling edge; judging K up The sum of rising edges K down After each falling edge, the time difference between adjacent rising edges or falling edges is the pulse repetition period, namely
PRI＝t up (k+1)-t up (k)，k＝1，2，...，K up (10)
Subtracting the rising edge time from the paired falling edge time to obtain pulse width, namely
PW＝t down (k)-t up (k)，k＝1，2，...，K down (11)
In addition, the rising edge of each pulse acts as its arrival time;
after the pulse related parameter values are estimated, the unmanned aerial vehicle signals are identified and extracted according to the measured signal carrier frequency CF, pulse repetition period PRI and pulse width PW parameters;
s3, judging whether unmanned aerial vehicle signals exist or not
Judging whether the unmanned aerial vehicle signal exists or not according to the carrier frequency CF, the pulse repetition period PRI and the pulse width PW parameters obtained by measurement in the step S2; if no unmanned aerial vehicle signal is judged, returning to the step S2 for continuous detection, and if the unmanned aerial vehicle signal is preliminarily judged, carrying out unmanned aerial vehicle signal detection judgment in the step S4;
s4, detecting and judging unmanned aerial vehicle signals
After the unmanned aerial vehicle signal is identified, accurately measuring and estimating the bandwidth and the frequency hopping rate of the unmanned aerial vehicle signal;
firstly, accurately estimating the bandwidth of a unmanned aerial vehicle signal, and performing the following operations:
1) Power spectrum
Dividing data x (N) with length of N, n=0, 1, …, N-1 into L segments, each segment having M data, wherein L is the length of each data segment when not overlapping, L=NM, i-th segment data x i (n) =x (n+im-M), and then a window function ω (n) is added to each data segment to find a periodic chart of each segment, the periodic chart of the i-th segment
Wherein, U is a normalization factor,
the periodic patterns of each segment are approximately uncorrelated with each other, and the final power spectrum is estimated as
2) Wavelet decomposition to obtain smooth power spectrum
Performing wavelet decomposition on the estimated power spectrum, separating a detail part from a rough part of the signal, extracting coefficients of the rough part, reconstructing the power spectrum by the coefficients, and eliminating high-frequency details in a power spectrum waveform to obtain a smooth waveform;
3) Computing a mobile covariance of a smoothed power spectrum
After obtaining a smooth power spectrum, extracting a starting point and an ending point of a passband to estimate the bandwidth; calculating a mobile covariance:
r(k,k+1)＝cov(d(k),d(k+1))，k＝1,2,…,N-1 (15)
wherein: d (k) is a reconstruction coefficient of the rough part extracted after wavelet decomposition, all data points are traversed by using the method, and the movement covariance between any adjacent points is obtained; the value of each p points is changed, so the following formula is adopted for calculation
Where k=1, 1+p, …, N-p, p values are chosen depending on the nature of the actual data point, and when p=1, the equivalent is formula (15);
4) Estimating bandwidth
The positions a and b where the maximum 2 mobile covariance values are located are extracted, and are respectively regarded as a starting point and a cut-off point of the bandwidth, and the difference value |b-a-1| is regarded as an estimated bandwidth;
setting the circulation times, repeating the steps 1) to 4) for L segments of data x (N), n=0, 1, …, N-1 and the length of N, calculating L bandwidths, then averaging, and obtaining a statistical average value of estimated bandwidths, namely the accurate bandwidth estimated value of the unmanned aerial vehicle signal; then, the frequency hopping rate is accurately estimated, and the following operations are performed: analyzing the signal by utilizing short-time Fourier transform to obtain time-frequency representation thereof, extracting edge information of the time-frequency representation by utilizing wavelet transform, and estimating the frequency hopping rate by utilizing spectrum analysis; the step of accurately estimating the frequency hopping rate is as follows:
1) Calculating short-time Fourier transform STFT of the unmanned aerial vehicle signal x ；
First assume a hopping rate estimate
As known a priori, the time-frequency representation of the short-time fourier transform of the received signal is:
where h (τ -t) is a window function, where (τ -t) e [ - Δ/2, Δ/2]When 1, the others are 0; delta is the width of the time-frequency window; setting h (τ -t) to be
To ensure that there is at most one frequency jump in the time region covered by the window function;
2) Extraction of STFT x Time-frequency ridge line f of (2) x (t)；
To STFT x Performing secondary processing on the time-frequency representation of (t, f), and extracting a time-frequency ridge line of the time-frequency representation, wherein the time-frequency ridge line is represented by a formula (18):
3) Calculating f x (t) wavelet transform W (a, t);
using wavelet transforms
Wherein a is a scale parameter;
is a Haar wavelet;
setting a scale parameter a to be equal to the width delta of a time-frequency window, and ensuring that at most one frequency jump exists in the coverage time range of the wavelet function;
4) Calculating the amplitude sequence Abs [ W (a, t) ];
computing the amplitude sequence Abs of W (a, t) [ W (a, t) ]]The amplitude sequence is a pseudo-random sequence, i.e. frequency hopping occurs at nT H (n.epsilon.Z) time;
5) Calculating a fourier transform FFT of Abs [ W (a, t) ];
6) Detecting an interval of the spectral peaks, the interval corresponding to an estimation of the frequency hopping rate: the wavelet transformation amplitude sequence of the time-frequency ridge line has discrete spectral lines at the position of the hopping rate, and accurate estimation of the hopping rate is realized by detecting the discrete spectral lines.
2. A signal feature difference-based unmanned aerial vehicle uplink and downlink communication link identification device for implementing the method of claim 1, characterized in that: the broadband receiver is used for receiving electromagnetic wave signals and amplifying, filtering, mixing and intermediate frequency processing the signals; the unmanned aerial vehicle signal detection pre-judging module is used for carrying out carrier frequency CF estimation on the signal processed by the broadband receiver, carrying out pulse detection by combining with narrowband filtering, and measuring pulse repetition period PRI and pulse width PW parameters of the signal; the unmanned aerial vehicle signal identification module is used for identifying pulse repetition period PRI and pulse width PW parameters of the unmanned aerial vehicle signals; and the uplink and downlink signal detection judging module is used for accurately measuring and estimating the bandwidth and the frequency hopping rate of the unmanned aerial vehicle.
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