CN115754469B - Unmanned aerial vehicle weak signal detection and extraction method, system, equipment, medium and terminal - Google Patents
Unmanned aerial vehicle weak signal detection and extraction method, system, equipment, medium and terminal Download PDFInfo
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Abstract
The invention belongs to the technical field of unmanned aerial vehicle signal processing, and discloses a civil unmanned aerial vehicle weak signal detection and extraction method based on time-frequency double-envelope characteristics, which comprises the following steps: performing time-frequency transformation on the filtered signal by using self-adaptive time domain window width calculation, and extracting a time-frequency energy double-envelope curve; and separating and extracting the pulse signals by using a pulse detection and extraction method based on a time-frequency double-envelope characteristic curve, a normalized time-frequency double-envelope characteristic curve double-threshold calculation method and a processing method of time domain scaling separation pulse signals. According to the invention, weak image transmission signals and remote control signals of the unmanned aerial vehicle are intercepted as the grippers, so that detection and separation of unmanned aerial vehicle pulse signals in a complex electromagnetic environment are realized, and technical support is provided for subsequent unmanned aerial vehicle signal parameter estimation and individual identification.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle signal processing, and particularly relates to a method, a system, equipment, a medium and a terminal for detecting and extracting weak signals of an unmanned aerial vehicle.
Background
At present, with the gradual increase of the market share of the small and medium unmanned aerial vehicle, the living fun of people is enriched, and meanwhile, a plurality of problems are brought to the management level of the unmanned aerial vehicle. The black flight of the small civil unmanned aerial vehicle causes the flight stop and delay for many times, and part of important security places are also frequently affected by the black flight, so that the problem is commonly solved by means of a system and technology in the social aspect for the management and control of the small civil unmanned aerial vehicle. Because unmanned aerial vehicle adopts PVC synthetic material to make, the volume is less, exists the shielding of building in urban environment, and traditional active radar detects the false alarm rate in practical application to civilian medium and small-size unmanned aerial vehicle too high. The image transmission signal and the remote measurement signal transmitted by the unmanned aerial vehicle are passively intercepted and received, and are analyzed, so that the unmanned aerial vehicle is found, identified, positioned and tracked.
Passive detection means are means commonly used in the unmanned aerial vehicle management and control industry. Its advantages mainly include: firstly, the situation monitoring in full time can be realized, the resource consumption is low, and the management and control of the key places are facilitated; secondly, passive detection equipment does not actively radiate electromagnetic waves, does not influence the surrounding electromagnetic environment, does not interfere the military and civil electromagnetic industry, does not have the problem of frequency point resource conflict, and is convenient for the supervision of local related departments. However, the electromagnetic properties of the civilian drone itself present a significant challenge to passive detection. The civil small and medium-sized unmanned aerial vehicle has low radiation power, the power level of the image transmission signal and the remote measurement signal is milliwatt level, surrounding high-power WiFi signals, communication signals and other frequency band interference signals are more, and the detection is difficult to be carried out by adopting a conventional envelope detection method.
At present, unmanned aerial vehicle detection units and enterprises in the market generally adopt passive detection technical routes, most of the unmanned aerial vehicle detection and discovery are concentrated on short-distance (less than 3 km) unmanned aerial vehicle detection and discovery, the distance is increased, the complexity of surrounding electromagnetic environment is improved, and the detection and discovery efficiency of the unmanned aerial vehicle is reduced. With the rapid development of the current civil unmanned aerial vehicle technology, the control and detection challenges for unmanned aerial vehicles are greatly increased. The accurate identification of unmanned aerial vehicle electrical signals of different models and manufacturers is a precondition for subsequent processing, and a large technical barrier exists at present.
Through the above analysis, the problems and defects existing in the prior art are as follows: the existing conventional envelope detection method cannot be used for finding and detecting a remote unmanned aerial vehicle; unmanned aerial vehicle pulse signals separated from received frame data in the prior art are inaccurate and have more interference.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a method, a system, equipment, a medium and a terminal for detecting and extracting weak signals of an unmanned aerial vehicle, and particularly relates to a method for detecting and extracting weak signals of a civil unmanned aerial vehicle based on time-frequency double-wrapping characteristics.
The invention discloses a weak signal detection and extraction method of an unmanned aerial vehicle, which comprises the following steps: performing time-frequency transformation on the filtered signal by using self-adaptive time domain window width calculation, and extracting a time-frequency energy double-envelope curve; and separating and extracting the pulse signals by using a pulse detection and extraction method based on a time-frequency double-envelope characteristic curve, a normalized time-frequency double-envelope characteristic curve double-threshold calculation method and a processing method of time domain scaling separation pulse signals.
Further, the unmanned aerial vehicle weak signal detection and extraction method comprises the following steps:
step one, bandpass processing is carried out on received intermediate frequency data by using a bandpass filter through setting parameters, and time-frequency conversion is carried out on filtered signals by using an adaptive time domain window width measuring and calculating method to automatically set parameters;
step two, processing the time-frequency conversion spectrum of the signal, and extracting a time-frequency energy double-envelope curve; processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
step three, pulse detection is carried out by utilizing a double-threshold pulse detection algorithm; and performing scale equivalent transformation on the pulse signals to obtain actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data.
Further, the performing time-frequency conversion on the filtered signal by using the automatic setting parameter of the adaptive time-domain window width measuring and calculating method includes:
and automatically setting parameters by using a self-adaptive time domain window width measuring and calculating method according to the time domain length and time and frequency resolution requirements of the signals, and performing time window processing on the filtered signals.
Further, the time-frequency conversion spectrum of the signal is processed, and a time-frequency energy double-envelope curve is extracted; processing the time-frequency energy double-envelope curve with improved envelope detection includes:
(1) Performing envelope detection on the time domain envelope of the frame signal, and extracting a time domain envelope; calculating the time-frequency window width according to the resolution required by the time domain, and performing time-frequency conversion on the frame signal to obtain a frame signal time-frequency conversion matrix;
(2) Performing averaging treatment on the frame signal time-frequency transformation matrix in a time dimension to obtain a frequency domain energy distribution curve of each moment point of the frame signal;
(3) Envelope detection is carried out on the frame signal frequency domain energy distribution curve, the frame signal time domain envelope curve and the frame signal frequency domain energy distribution curve are mapped by using the time-frequency window width, time-frequency alignment is carried out, a time-frequency double-envelope characteristic curve of the frame signal is obtained, and a time-frequency double-envelope characteristic curve of the frame signal is obtained.
Further, the pulse detection using the dual threshold pulse detection algorithm includes:
calculating a pulse detection threshold by adopting a double-threshold method, and judging whether a pulse signal exists or not; if the pulse signal exists, the starting and ending time points of the pulse are determined, and the pulse signal is separated from the frame signal.
Further, the pulse detection by using the double-threshold pulse detection algorithm comprises the following steps:
1) Calculating the mean value M and the average energy V of the double-envelope characteristic curve, selecting a higher threshold value T1 on the time-frequency double-envelope characteristic curve for initial judgment, judging that the time is higher than the threshold value T1, and determining that the time is a voice signal, wherein the pulse starting point is positioned outside the time point corresponding to the threshold value and the short-time energy envelope foot pad;
2) And determining a lower threshold T2 on the average energy V, performing traversing search on the left end and the right end by taking T1 as the center, and respectively finding out the time points of the short-time energy envelope intersecting with the threshold T2, wherein the time points of the short-time energy envelope intersecting with the threshold T2 are pulse signal starting and ending points.
Another object of the present invention is to provide an unmanned aerial vehicle weak signal detection and extraction system for implementing the unmanned aerial vehicle weak signal detection and extraction method, the unmanned aerial vehicle weak signal detection and extraction system comprising:
the signal filtering processing module is used for carrying out band-pass processing on the received intermediate frequency data by using a band-pass filter through setting parameters;
the time-frequency conversion processing module is used for automatically setting parameters to perform time-frequency conversion on the filtered signals by using the self-adaptive time domain window width measuring and calculating method;
the time-frequency energy double-envelope curve extraction module is used for processing the time-frequency conversion spectrum of the signal and extracting a time-frequency energy double-envelope curve;
the curve processing module is used for processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
the pulse detection module is used for carrying out pulse detection by utilizing a double-threshold pulse detection algorithm;
the pulse signal extraction and separation module is used for performing scale equivalent transformation on the pulse signals, obtaining actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data.
Another object of the present invention is to provide a computer device, which includes a memory and a processor, where the memory stores a computer program, and the computer program when executed by the processor causes the processor to perform the steps of the unmanned aerial vehicle weak signal detection extraction method, system, device, medium and terminal.
Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to perform the steps of the unmanned aerial vehicle weak signal detection extraction method, system, device, medium and terminal.
The invention further aims to provide an information data processing terminal which is used for realizing the civil unmanned aerial vehicle weak signal detection and extraction system based on the time-frequency double-envelope characteristic.
In combination with the technical scheme and the technical problems to be solved, the technical scheme to be protected has the following advantages and positive effects:
first, aiming at the technical problems in the prior art and the difficulty in solving the problems, the technical problems solved by the technical proposal of the invention are analyzed in detail and deeply by tightly combining the technical proposal to be protected, the results and data in the research and development process, and the like, and some technical effects brought after the problems are solved have creative technical effects. The specific description is as follows:
according to the invention, weak image transmission signals and remote control signals of the unmanned aerial vehicle are intercepted as the grippers, so that detection and separation of unmanned aerial vehicle pulse signals in a complex electromagnetic environment are realized, and technical support is provided for subsequent unmanned aerial vehicle signal parameter estimation and individual identification. The invention provides a time-frequency double-envelope characteristic curve-based method, which can still realize weak signal detection and extraction when the distance of an unmanned aerial vehicle is more than 15km under the same radiation power condition.
According to the method, the time domain processing window width is automatically adjusted according to the time domain resolution required by a user, and the extraction of a double-envelope curve for acquiring the time domain envelope and the frequency domain energy of a signal is completed based on an interactive time-frequency transformation processing method; the invention carries out secondary treatment on the extracted double-envelope curve based on the treatment means of envelope detection, and eliminates the interference influence of high-frequency signals such as noise and the like; the local scale of the signal is used for exchanging global detection efficiency; the invention is based on the double-threshold self-adaptive threshold detection algorithm of the time-frequency double-envelope curve, and sets the threshold according to the characteristics of signal energy distribution and noise energy distribution to finish the signal detection; the invention completes the extraction and separation of signals from the received frame data by time domain processing window width mapping transformation.
Secondly, the technical scheme is regarded as a whole or from the perspective of products, and the technical scheme to be protected has the following technical effects and advantages:
according to the invention, the distance of detection and discovery of unmanned aerial vehicles in the current market is improved, and experimental data prove that the unmanned aerial vehicle signal detection and discovery system can intercept unmanned aerial vehicle signals beyond 15km, and can effectively increase the preparation time of a countering unmanned aerial vehicle; according to the invention, unmanned aerial vehicle pulse signals are extracted and separated from the intercepted frame data through scale transformation, so that a reliable data base is provided for subsequent parameter measurement, unmanned aerial vehicle positioning and signal sample database establishment; the invention improves the robustness and reliability of the system by adopting the self-adaptive threshold setting without intervention of too many human factors.
The feasibility of the invention is mainly represented by the characteristic that the algorithm can be embedded, transplanted, replaced and compatible in the actual equipment, is the analysis of the fusion capability of the algorithm and the actual engineering equipment, and mainly comprises the following aspects:
firstly, stability of algorithm design; the algorithm adopts a conventional signal processing algorithm, and carries out targeted improvement and self-grinding, so that the stability of the actual measurement data analysis algorithm is better, the problems of program logic errors, data format errors and the like are not generated in the test process, the stability of the algorithm is better, and the algorithm is suitable for engineering application;
secondly, the feasibility of algorithm conversion; the algorithm adopts conventional digital logic, and the related signal processing algorithm is mature in FPGA development, so that the algorithm can be completely used for hardware development of the FPGA, the algorithm performance is improved, and engineering application is satisfied;
thirdly, compatibility with other software of the equipment after algorithm conversion; the algorithm is used for upgrading the conventional signal processing algorithm, and the problems of incompatibility and coexistence of other signal software are solved, and the result of the algorithm can be used for target positioning, parameter estimation and database establishment and perfection, so that the algorithm is completely compatible with other software of equipment in engineering application.
Thirdly, as inventive supplementary evidence of the claims of the present invention, the following important aspects are also presented:
the expected benefits and commercial values after the technical scheme of the invention is converted are as follows: the invention can realize the function of remotely detecting and intercepting weak unmanned aerial vehicle signals. The efficiency of the anti-unmanned aerial vehicle equipment in the current market can be greatly improved after conversion; the invention adopts a deep learning algorithm to extract the pulse, provides accurate pulse signals for the subsequent identification and positioning of 'black flight', and solves the problem that the pulse detection in multi-station time difference positioning and single-station direction finding positioning cannot be realized; according to the unmanned aerial vehicle signal analysis method, the unmanned aerial vehicle pulse signals are intercepted, corresponding equipment is assisted to analyze the unmanned aerial vehicle signals, an unmanned aerial vehicle signal parameter sample library is built, and an accurate data base is provided for database building.
Drawings
Fig. 1 is a schematic diagram of a weak signal detection and extraction method of an unmanned aerial vehicle provided by an embodiment of the invention;
fig. 2 is a schematic diagram of a civil unmanned aerial vehicle weak signal detection method based on time-frequency double-envelope characteristics provided by the embodiment of the invention;
fig. 3 is a flowchart of a method for detecting and extracting weak signals of an unmanned aerial vehicle according to an embodiment of the invention;
FIG. 4 is a time domain distribution of an intercepted frame pulse signal provided by an embodiment of the present invention;
FIG. 5 is a time-frequency distribution of a frame pulse signal according to an embodiment of the present invention;
FIG. 6 is a time domain distribution of a frame pulse signal after noise addition according to an embodiment of the present invention;
FIG. 7 is a time-frequency distribution of a frame pulse signal after noise addition according to an embodiment of the present invention;
FIG. 8 is a time-frequency normalized double-envelope characteristic provided by an embodiment of the present invention;
FIG. 9 is a normalized time-frequency double-envelope characteristic curve and a detection threshold distribution diagram of an actual measurement frame signal according to an embodiment of the present invention;
FIG. 10 is a normalized time-frequency double-envelope characteristic curve and a detection threshold distribution diagram of the measured frame signal plus noise provided by the embodiment of the invention;
FIG. 11 is a time domain distribution of an actual intercepted frame signal with a signal-to-noise ratio of 0.8dB provided by an embodiment of the present invention;
FIG. 12 is a time-frequency characteristic distribution of an actual intercepted frame signal with a signal-to-noise ratio of 0.8dB provided by an embodiment of the present invention;
FIG. 13 is a normalized time-frequency double-envelope characteristic curve of an actual intercepted frame signal with a signal-to-noise ratio of 0.8dB provided by an embodiment of the present invention;
FIG. 14 is a time domain distribution of an actual captured frame signal at a signal-to-noise ratio of 0dB provided by an embodiment of the present invention;
FIG. 15 is a time-frequency characteristic distribution of an actual intercepted frame signal when the signal-to-noise ratio provided by the embodiment of the invention is 0 dB;
FIG. 16 is a normalized time-frequency double-envelope characteristic curve of an actual intercepted frame signal when the signal-to-noise ratio provided by the embodiment of the invention is 0 dB;
FIG. 17 is a time domain distribution of an actual captured frame signal at a signal-to-noise ratio of-2 db provided by an embodiment of the present invention;
FIG. 18 is a time-frequency characteristic distribution of an actual captured frame signal when the signal-to-noise ratio is-2 db provided by the embodiment of the present invention;
FIG. 19 is a normalized time-frequency double-envelope characteristic curve of an actual intercepted frame signal when the signal-to-noise ratio is-2 db provided by the embodiment of the invention;
FIG. 20 is a time domain distribution of an actual captured frame signal at a signal-to-noise ratio of-4 db provided by an embodiment of the present invention;
FIG. 21 is a time-frequency characteristic distribution of an actual captured frame signal when the signal-to-noise ratio is-4 db according to an embodiment of the present invention;
FIG. 22 is a normalized time-frequency double-envelope characteristic curve of an actual intercepted frame signal when the signal-to-noise ratio is-4 db according to the embodiment of the present invention;
FIG. 23 is a time domain distribution of an actual captured frame signal at a signal-to-noise ratio of-5 db provided by an embodiment of the present invention;
FIG. 24 is a time-frequency characteristic distribution of an actual captured frame signal when the signal-to-noise ratio provided by the embodiment of the invention is-5 db;
fig. 25 is a normalized time-frequency double-envelope characteristic curve of an actual intercepted frame signal when the signal-to-noise ratio is-5 db according to the embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
1. The embodiments are explained. In order to fully understand how the invention may be embodied by those skilled in the art, this section is an illustrative embodiment in which the claims are presented for purposes of illustration.
As shown in fig. 1 to 3, the method for detecting and extracting weak signals of an unmanned aerial vehicle provided by the embodiment of the invention comprises the following steps:
s101, carrying out band-pass processing on received intermediate frequency data by using a band-pass filter through setting parameters, and carrying out time-frequency conversion on filtered signals by using an adaptive time domain window width measuring and calculating method;
s102, processing a time-frequency conversion spectrum of a signal, and extracting a time-frequency energy double-envelope curve; processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
s103, pulse detection is carried out by using a double-threshold pulse detection algorithm; and performing scale equivalent transformation on the pulse signals to obtain actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data.
The unmanned aerial vehicle weak signal detection and extraction system provided by the embodiment of the invention comprises the following components:
the signal filtering processing module is used for carrying out band-pass processing on the received intermediate frequency data by using a band-pass filter through setting parameters;
the time-frequency conversion processing module is used for automatically setting parameters to perform time-frequency conversion on the filtered signals by using the self-adaptive time domain window width measuring and calculating method;
the time-frequency energy double-envelope curve extraction module is used for processing the time-frequency conversion spectrum of the signal and extracting a time-frequency energy double-envelope curve;
the curve processing module is used for processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
the pulse detection module is used for carrying out pulse detection by utilizing a double-threshold pulse detection algorithm;
the pulse signal extraction and separation module is used for performing scale equivalent transformation on the pulse signals, obtaining actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data.
On the basis of conventional signal processing, the normalized time-frequency double-envelope characteristic curve is extracted, and the self-adaptive double-threshold detection algorithm is designed, as shown in fig. 1, the logic flow of the invention is as follows:
(1) band-pass filter: setting parameters to carry out band-pass processing on the received intermediate frequency data and inhibit out-of-band noise;
(2) the self-adaptive time domain window width measuring and calculating technology comprises the following steps: according to the time domain length, time and frequency resolution requirements of the signals, automatically setting parameters by an algorithm, and performing time-frequency conversion on the filtered signals;
(3) extracting a normalized time-frequency double-envelope characteristic curve: and processing the time-frequency conversion spectrum of the signal, and extracting a time-frequency energy double-envelope curve which is used as basic data for detecting the weak signal. The algorithm is a core algorithm of the invention, and the detection performance of the algorithm can be verified in measured data;
(4) envelope detection processing: in order to restrain the influence of noise in the extracted time-frequency double-envelope characteristic curve, the curve is processed by adopting improved envelope detection, and the process is equivalent to the algorithm idea of windowing and weighting. However, the improved envelope detection algorithm is far more than the conventional algorithm idea of windowing and weighting in operation time;
(5) double threshold detection algorithm: considering that in practical engineering application, the robustness of the system to environmental uncertainty is increased, the algorithm is based on the characteristic distribution of strong signal correlation and weak noise correlation, and the analog voice signal detection principle, the double-threshold pulse detection algorithm is researched, and support is provided for pulse extraction and separation;
(6) and (3) performing scale transformation processing: in the signal time-frequency conversion processing, the global detection efficiency and the operation efficiency are improved at the cost of local scale reduction and time domain resolution reduction of the signal, after the initial position of the pulse signal is detected, the scale equivalent conversion is carried out, the actual initial position point and the actual end position point of the pulse signal are obtained, and the pulse signal is extracted and separated from the received frame data.
The algorithm of the self-adaptive time-frequency window width provided by the embodiment of the invention is as follows:
the signal analysis is premised on extracting the pulse signal from frame data intercepted by the receiver. Along with the improvement of acquisition equipment and the process thereof, the signal sampling rate is higher and higher, the signal characterization is finer and finer, and meanwhile, the complexity of the later signal extraction is inevitably brought. To meet processing timeliness, the acquired intermediate frequency signals may be converted according to a desired time domain resolution.
Assuming that the sampling frequency fs of the receiver is 50MHz, the data length 6144000 acquired by acquiring 0.1229 seconds is 20ns, the time-domain resolution is 20ns, the time-frequency change and pulse separation take 1.458 seconds, but the time-domain resolution of the actual processing pulse detection and separation is microsecond, the time-domain resolution of the time-frequency envelope curve is lower, becauseThis may be done by time window processing the pulse signal in a time frequency processing module. Assuming that the sampling rate of the receiver is fs, the time domain resolution required by the time-frequency envelope curve extraction processing is tus(conventionally, the value range of t is 2-12us, and the value interval is the empirical value which meets the algorithm after a plurality of actual data tests).
The calculation of the time-frequency window width value is related to the time-domain resolution, the signal sampling rate and the data analysis length required by the time-frequency envelope curve, and the higher the sampling frequency is, the smaller the time-domain resolution of the sampled data is, and the longer the data length is. If a higher time-domain resolution is required, the smaller the time-frequency window width value, the longer the program running time. Although the pulse detection requirement can be met, the running time is long, the real-time processing capacity is limited, the time domain resolution with lower antisense corresponds to a larger time-frequency window width value, and the shorter the running time of the program is. On the basis of hardware: the evaluation of the calculation efficiency of the adaptive time-frequency window width algorithm is specifically shown in the following table, wherein the RAM is 16GB Core (TM) i7-7700CPU, the sampling frequency is 50MHz, the time domain resolution (us) of sampled data is 20ns, the data length is 6144000:
table 1 adaptive time-frequency window width algorithm operation efficiency evaluation table
| Time-frequency envelope curve required time-domain resolution (us) | Time-frequency window width value (N) | Run time(s) | Whether or not the pulse detection requirement is satisfied |
| 0.02 | 1 | >50s | Satisfy the following requirements |
| 2.56 | 256 | 2.61 | Satisfy the following requirements |
| 5.12 | 512 | 1.38 | Satisfy the following requirements |
| 10.24 | 1024 | 0.773 | Satisfy the following requirements |
| 20.48 | 2048 | 0.48 | Does not satisfy |
The pulse detection and extraction algorithm based on the time-frequency double-envelope characteristic curve provided by the embodiment of the invention is as follows:
the rapid detection and extraction of pulse signals in a complex electromagnetic environment are the basis for carrying out target parameter estimation, identification and positioning. In an environment where the signal-to-noise ratio is low, the time domain envelope of the signal is "buried" in noise and difficult to extract effectively, and although it can be determined from the frequency domain that a pulse signal does exist, it cannot be separated from the time domain. Aiming at the practical technical problem, the invention provides a pulse detection algorithm based on a time-frequency double-envelope characteristic curve based on the time-frequency distribution characteristic of a pulse signal, and the algorithm mainly comprises the following design flow:
(1) performing envelope detection on the time domain envelope of the frame signal, and extracting a time domain envelope;
(2) calculating the time-frequency window width according to the resolution ratio required by the time domain, and performing time-frequency transformation on the frame signal to obtain a time-frequency transformation matrix of the frame signal;
(3) performing averaging treatment on the frame signal time-frequency transformation matrix in a time dimension to obtain a frequency domain energy distribution curve of each moment point of the frame signal;
(4) envelope detection is carried out on the frame signal frequency domain energy distribution curve, and errors caused by high-frequency jitter are removed;
(5) mapping the frame signal time domain envelope curve and the frame signal frequency domain energy distribution curve by using the time-frequency window width, and performing time-frequency alignment to obtain a time-frequency double-envelope characteristic curve of the frame signal and a time-frequency double-envelope characteristic curve of the frame signal;
(6) calculating a pulse detection threshold by adopting a double-threshold method, and judging whether a pulse signal exists or not;
(7) if the pulse signal exists, determining the starting and ending time points of the pulse, and separating from the frame signal to finish pulse detection and separation.
To check the robustness of the algorithm, a certain frame signal which is actually intercepted is processed:
from fig. 4-5 it can be seen that the intercepted frame signal has four pulses in total, of which two wide pulses and two narrow pulses are numbered 1, 2, 3, 4 in sequence from left to right, of which No. 2 and No. 4 are wide pulses and No. 1 and No. 3 are narrow pulses. The time-frequency distribution characteristics of the frame signals are obvious, and the time-frequency distribution characteristics of the frame signals are in self-adaptive time domain resolution ratio: 20.48us, time window width value 2048, time consuming: 0.4286s, the time domain distribution parameters are shown in the following table:
table 2 pulse time domain parameter statistics table for frame signal separation
| Pulse | Pulse | 1 | |
Pulse 3 | |
| Start time(s) | 0.0512 | 0.0533 | 0.1023 | 0.1048 | |
| Termination time(s) | 0.0522 | 0.0573 | 0.1039 | 0.1088 | |
| Width of pulse width(s) | 0.0010 | 0.0040 | 0.0016 | 0.0041 |
And carrying out Gaussian noise on the frame signal. As shown in fig. 6: the narrow pulses 1 and 3 have been "submerged" in the background noise, and the wide pulses 2 and 4 have only a partial envelope, the time-frequency distribution profile of which is shown in fig. 7.
The pulse signals are difficult to be detected and separated quickly and effectively by means of frequency domain or time domain energy distribution, and the subsequent signal processing is influenced. And processing the frame signal after noise addition by adopting a pulse detection algorithm of a time-frequency double-envelope characteristic curve. Based on the frequency domain energy of the frame signal, the time domain envelope characteristic is combined, and a time-frequency normalized double-envelope characteristic curve is obtained as shown in fig. 8.
Secondly, based on a time-frequency normalized double-envelope characteristic curve, time-frequency alignment is carried out, the time point of a pulse signal is determined, and the self-adaptive time domain resolution ratio is obtained: 20.48us, time window width value 2048, time consuming: 0.5244s, the pulse signal time domain distribution parameters are shown in the following table:
| pulse | Pulse | 1 | |
Pulse 3 | |
| Start time(s) | 0.0512 | 0.0533 | 0.1022 | 0.1047 | |
| Error of start time (us) | 37.12 | 16.62 | 37.12 | 37.12 | |
| Termination time(s) | 0.0523 | 0.0573 | 0.1039 | 0.1089 | |
| Termination time error (us) | 16.62 | 57.6 | 37.12 | 16.64 | |
| Width of pulse width(s) | 0.0011 | 0.0041 | 0.0016 | 0.0041 |
The analysis shows that the pulse detection algorithm based on the time-frequency normalized double-envelope characteristic curve can effectively detect and separate pulse signals, and under the condition that the self-adaptive time domain resolution is 20.48us parameters, the pulse start time error and the pulse end time error can be effectively controlled within 3 time-frequency window widths, and the pulse start time order of magnitude and the pulse time error order of magnitude are above 1300. The algorithm meets the requirement of pulse separation in consideration of errors caused by 3dB threshold interception.
The measured data prove that the algorithm for detecting and separating the pulses based on the time-frequency normalized double-envelope characteristic curve has stronger adaptability and robustness under the low signal-to-noise ratio environment.
The normalized time-frequency double-envelope characteristic curve double-threshold calculation algorithm provided by the embodiment of the invention comprises the following steps:
the double-threshold method is proposed based on short-time average energy and short-time zero-crossing rate, and is applied to the field of voice detection and recognition. The method is characterized in that when the unmanned aerial vehicle remotely transmits a graph signal, the signal power attenuation characteristic is the same as the voice attenuation characteristic mechanism characteristic in noise, so that after a normalized time-frequency double-envelope characteristic curve is calculated, a pulse detection threshold is calculated by taking the normalized time-frequency double-envelope characteristic curve as input. The method mainly comprises the following steps:
(1) and (5) first-stage judgment. Calculating the mean value M and the energy V of the double-envelope characteristic curve, selecting a higher threshold value T1 on the time-frequency double-envelope characteristic curve for roughing, judging that the voice signal is a voice signal if the voice signal is higher than the threshold value T1, and determining that a pulse starting point is beyond a time point corresponding to the threshold value and the short-time energy envelope foot pad;
(2) and a second level decision. And determining a lower threshold T2 on the average energy V, performing traversing search on the left and right ends by taking T1 as the center, and respectively finding out the time points when the short-time energy envelope intersects with the threshold T2, thereby obtaining the starting and stopping point positions of the pulse signals.
Taking the measured frame signal as an example, a pulse detection threshold of a normalized time-frequency double-envelope characteristic curve is calculated. Fig. 9 is a normalized time-frequency double-envelope characteristic curve and a detection threshold distribution diagram of an actual measurement frame signal, and fig. 10 is a normalized time-frequency double-envelope characteristic curve and a detection threshold distribution diagram of a frame signal after noise addition.
As can be seen from comparison of fig. 9 and 10: the normalized time-frequency double-envelope characteristic curve energy base is obviously raised under the influence of noise. The double-threshold detection algorithm can be well adapted to the influence caused by noise, and accurately detects the existence of the pulse and calculates the starting and ending time points of the pulse.
2. Application example. In order to prove the inventive and technical value of the technical solution of the present invention, this section is an application example on specific products or related technologies of the claim technical solution.
The invention is mainly applied to detection of civil unmanned aerial vehicle. The feasibility of the technical algorithm theory and the convertibility of practical engineering application are verified through practical equipment tests at present, and the method has good process application efficiency. It should be noted that the embodiments of the present invention can be realized in hardware, software, or a combination of software and hardware. The hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those of ordinary skill in the art will appreciate that the apparatus and methods described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The device of the present invention and its modules may be implemented by hardware circuitry, such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., as well as software executed by various types of processors, or by a combination of the above hardware circuitry and software, such as firmware.
3. Evidence of the effect of the examples. The embodiment of the invention has a great advantage in the research and development or use process, and has the following description in combination with data, charts and the like of the test process.
The actual measurement data verification mainly takes actual detection data as a sample, takes an actual electromagnetic environment as background noise, is based on a radar formula derivation theory, simulates attenuation of signal power of the unmanned aerial vehicle along with increase of distance from the unmanned aerial vehicle to an antenna by a Gaussian noise adding method, and carries out pulse signal detection and separation on the actual detection data by the method. Two main aspects are verified: firstly, the unmanned aerial vehicle signal rapid detection and separation capability verification under the low signal-to-noise ratio environment; and secondly, verifying timeliness of real detection data processing.
Introduction of acquisition environment: the sampling rate of the acquisition equipment is 50MHz, the unmanned aerial vehicle is 6km away from the receiver, the signal-to-noise ratio of the signal after being processed by the radio frequency front end is 0.8dB, the signal acquisition time length is 0.4096 seconds, and specific processing parameters are shown in the following table:
| sampling rate | Unmanned plane distance | Signal to noise ratio | Duration of signal acquisition | Duration of signal processing | Number of pulses | Pulse width |
| 50MHZ | 6km | 0.8dB | 0.4096s | 10.22s | 8 pieces of | 0.0042s |
The time domain waveform distribution, the time-frequency energy distribution and the normalized time-frequency double-envelope characteristic curves are shown in fig. 11 to 13.
Analysis shows that the characteristic distribution of the time-frequency domain of the intercepted pulse signals is obvious, and the total number of the pulse signals is 8. Under the condition that the external environment and the receiving equipment are unchanged, the relation between the signal-to-noise ratio of the frame signal and the distance is deduced, and the following table is shown:
| signal-to-noise ratio (dB) | 0.8 | 0 | -0.5 | -1 | -2 | -3 | -4 | -5 |
| Distance (km) | 6.16 | 7.53 | 8.95 | 10.35 | 13.79 | 18.40 | 24.53 | 32.71 |
1. The unmanned aerial vehicle signal rapid detection and separation capability under the low signal-to-noise ratio environment is verified as follows:
and carrying out Gaussian noise addition on the real detection data, and simulating the condition that the signal power of the unmanned aerial vehicle is attenuated along with the increase of the distance. Considering that the civil unmanned aerial vehicle image transmission distance is not more than 15km when the market is on sale, the lowest signal to noise ratio is set to-5 dB. The efficiency of pulse signal detection and separation under different signal-to-noise ratios is calculated as shown in the following table:
TABLE 3 effectiveness of pulse Signal detection and separation under different Signal-to-noise ratio (distance) algorithms
| Signal-to-noise ratio (dB) | 0.8 | 0 | -1 | -2 | -3 | -4 | -5 |
| Unmanned plane distance (km) | 6 | 7.53 | 10.35 | 13.79 | 18.40 | 24.53 | 32.71 |
| Frame signal duration(s) | 0.4096s | 0.4096s | 0.4096s | 0.4096s | 0.4096s | 0.4096s | 0.4096s |
| Number of |
8 pieces of | 8 pieces of | 8 pieces of | 8 pieces of | 8 pieces of | 8 pieces of | 0 pieces of |
| Pulse width(s) | 0.0042 | 0.0042 | 0.0041 | 0.0041 | 0.0040 | 0.0040 | -- |
| Can detect whether separation is possible | Separable (separable) | Separable (separable) | Separable (separable) | Separable (separable) | Separable (separable) | Separable (separable) | Inseparable and separable |
Fig. 14 to 25 disclose pulse signal time domain waveforms and normalized time-frequency double-envelope characteristic curves at partial signal-to-noise ratio (distance):
(1) When the signal-to-noise ratio is 0db, the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is 7.53km, 8 pulse signals are intercepted, and the pulse width is 0.0042s, which is consistent with the actual situation. Indicating that the algorithm has pulse signal detection and separation capabilities over this distance.
(2) When the signal-to-noise ratio is-2 db, the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is 13.79km, 8 pulse signals are intercepted, and the pulse width is 0.0041s, which is consistent with the actual situation. Indicating that the algorithm has pulse signal detection and separation capabilities over this distance.
(3) And when the signal to noise ratio is-4 db, the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is 23.54km, 8 pulse signals are intercepted, and the pulse width is 0.0040s, so that the unmanned aerial vehicle is consistent with the actual situation. Indicating that the algorithm has pulse signal detection and separation capabilities over this distance.
(4) When the signal-to-noise ratio is-5 db, the distance between the unmanned aerial vehicle and the unmanned aerial vehicle is 32.71km, and because the signal-to-noise ratio is low, the frequency domain and the time domain features are completely "submerged" by noise, and pulse signals cannot be detected and identified.
Analysis shows that: when the distance between the unmanned aerial vehicle and the target is more than 7.53km and less than 24km, although the time domain waveform and the time domain envelope can not be completely detected, the time-frequency double-envelope characteristic curve can still detect and separate the pulse signals, and when the distance is more than 30km, the current unmanned aerial vehicle detection equipment intercepts the pulse signal frequency domain and time domain characteristics and is completely "submerged" by noise, and can not be effectively detected and separated. Meanwhile, when the image transmission distance of the civil unmanned aerial vehicle sold in the market is not more than 15km, the algorithm can finish the signal detection of the unmanned aerial vehicle in the range of 24km, the moving range of the civil unmanned aerial vehicle is covered, and the stability of the algorithm is stronger through actual data test.
2. The unmanned aerial vehicle signal rapid detection and separation timeliness analysis under the low signal-to-noise ratio environment is as follows:
in the detection of an actual unmanned plane, pulse signals are accurately detected and separated, and the requirement on invalidation is high. Taking the above intercepted pulse data as an example, processing is performed on an AD9371 chip. Under a certain environment of a hardware environment, factors influencing the aging of hardware processing mainly comprise the processing efficiency of an algorithm and the requirements of pulse resolution. Defining an aging ratio TB:
aging ratio tb=data duration (s)/treatment duration(s)
The smaller the aging ratio TB, the faster the hardware processing speed, the higher the real-time processing of the algorithm, and the stronger the aging. At 20480000 sampled data points, the duration is 0.4096s, the signal pulse width is 0.0042s, and the processing speed of the AD9371 chip under different time domain resolution requirements is calculated:
| time resolution (us) | Time-frequency window width value (N) | Run time(s) | Ageing ratio |
| 2.56 | 256 | 1.92 | 4.67 |
| 5.12 | 512 | 1.00 | 2.41 |
| 10.24 | 1024 | 0.536 | 1.30 |
| 20.48 | 2048 | 0.308s | 0.75 |
Analysis shows that: with the reduction of the time domain resolution requirement, the hardware processing time is reduced, the timeliness of the algorithm is enhanced, the whole operation is stable, and the actual engineering equipment requirement is met.
The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, but any modifications, equivalents, improvements and alternatives falling within the spirit and principle of the present invention as those skilled in the art will readily fall within the scope of the present invention.
Claims (5)
1. The unmanned aerial vehicle weak signal detection and extraction method is characterized by comprising the following steps of:
step one, bandpass processing is carried out on received intermediate frequency data by using a bandpass filter through setting parameters, and time-frequency conversion is carried out on filtered signals by using an adaptive time domain window width measuring and calculating method to automatically set parameters;
step two, processing the time-frequency conversion spectrum of the signal, and extracting a time-frequency energy double-envelope curve; processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
step three, pulse detection is carried out by utilizing a double-threshold pulse detection algorithm; performing scale equivalent transformation on the pulse signals to obtain actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data;
the automatic parameter setting by using the adaptive time domain window width measuring and calculating method for performing time-frequency conversion on the filtered signal comprises the following steps:
according to the time domain length and time and frequency resolution requirements of the signals, automatically setting parameters by using a self-adaptive time domain window width measuring and calculating method, and performing time window processing on the filtered signals;
processing the time-frequency conversion spectrum of the signal, and extracting a time-frequency energy double-envelope curve; processing the time-frequency energy double-envelope curve with improved envelope detection includes:
(1) Performing envelope detection on the time domain envelope of the frame signal, and extracting a time domain envelope; calculating the time-frequency window width according to the resolution required by the time domain, and performing time-frequency conversion on the frame signal to obtain a frame signal time-frequency conversion matrix;
(2) Performing averaging treatment on the frame signal time-frequency transformation matrix in a time dimension to obtain a frequency domain energy distribution curve of each moment point of the frame signal;
(3) Envelope detection is carried out on the frame signal frequency domain energy distribution curve, the frame signal time domain envelope curve and the frame signal frequency domain energy distribution curve are mapped by using the time-frequency window width, time-frequency alignment is carried out, a time-frequency double-envelope characteristic curve of the frame signal is obtained, and a time-frequency double-envelope characteristic curve of the frame signal is obtained;
the pulse detection by using the double-threshold pulse detection algorithm comprises the following steps:
calculating a pulse detection threshold by adopting a double-threshold method, and judging whether a pulse signal exists or not; if the pulse signal exists, determining the starting and ending time points of the pulse, and separating from the frame signal;
the pulse detection by using the double-threshold pulse detection algorithm comprises the following steps:
1) Calculating the mean value M and the average energy V of the double-envelope characteristic curve, selecting a higher threshold value T1 on the time-frequency double-envelope characteristic curve for initial judgment, judging that the time is higher than the threshold value T1, and determining that the time is a voice signal, wherein the pulse starting point is positioned outside a time point corresponding to the intersection point of the threshold value and the short-time energy envelope;
2) And determining a lower threshold T2 on the average energy V, performing traversing search on the left end and the right end by taking T1 as the center, and respectively finding out the time points of the short-time energy envelope intersecting with the threshold T2, wherein the time points of the short-time energy envelope intersecting with the threshold T2 are pulse signal starting and ending points.
2. A weak signal detection and extraction system for an unmanned aerial vehicle for implementing the weak signal detection and extraction method according to claim 1, wherein the weak signal detection and extraction system for an unmanned aerial vehicle comprises:
the signal filtering processing module is used for carrying out band-pass processing on the received intermediate frequency data by using a band-pass filter through setting parameters;
the time-frequency conversion processing module is used for automatically setting parameters to perform time-frequency conversion on the filtered signals by using the self-adaptive time domain window width measuring and calculating method;
the time-frequency energy double-envelope curve extraction module is used for processing the time-frequency conversion spectrum of the signal and extracting a time-frequency energy double-envelope curve;
the curve processing module is used for processing the time-frequency energy double-envelope curve by adopting improved envelope detection;
the pulse detection module is used for carrying out pulse detection by utilizing a double-threshold pulse detection algorithm;
the pulse signal extraction and separation module is used for performing scale equivalent transformation on the pulse signals, obtaining actual starting and ending position points of the pulse signals, and extracting and separating the pulse signals from the received frame data.
3. A computer device comprising a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the unmanned aerial vehicle weak signal detection extraction method of claim 1.
4. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, causes the processor to perform the steps of the unmanned aerial vehicle weak signal detection extraction method according to claim 1.
5. An information data processing terminal, wherein the information data processing terminal is configured to implement the unmanned aerial vehicle weak signal detection and extraction system according to claim 2.
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