CN116699549A - Unmanned aerial vehicle identification and classification method, device, equipment and medium - Google Patents

Unmanned aerial vehicle identification and classification method, device, equipment and medium Download PDF

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Publication number
CN116699549A
CN116699549A CN202310694264.0A CN202310694264A CN116699549A CN 116699549 A CN116699549 A CN 116699549A CN 202310694264 A CN202310694264 A CN 202310694264A CN 116699549 A CN116699549 A CN 116699549A
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China
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echo
unmanned aerial
aerial vehicle
rotor
rotor wing
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范世琦
武姿言
申鑫
涂刚毅
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Nanjing University of Information Science and Technology
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Nanjing University of Information Science and Technology
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
    • G01S13/50—Systems of measurement based on relative movement of target
    • G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
    • G01S13/583—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets
    • G01S13/584—Velocity or trajectory determination systems; Sense-of-movement determination systems using transmission of continuous unmodulated waves, amplitude-, frequency-, or phase-modulated waves and based upon the Doppler effect resulting from movement of targets adapted for simultaneous range and velocity measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66—Radar-tracking systems; Analogous systems
    • G01S13/72—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723—Radar-tracking systems; Analogous systems for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726—Multiple target tracking
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/35—Details of non-pulse systems
    • G01S7/352—Receivers
    • G01S7/354—Extracting wanted echo-signals
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
    • G01S7/415—Identification of targets based on measurements of movement associated with the target
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/28—Determining representative reference patterns, e.g. by averaging or distorting; Generating dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2123/00—Data types
    • G06F2123/02—Data types in the time domain, e.g. time-series data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/08—Feature extraction
    • G06F2218/10—Feature extraction by analysing the shape of a waveform, e.g. extracting parameters relating to peaks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/12—Classification; Matching

Abstract

The invention discloses an unmanned aerial vehicle identification and classification method, device, equipment and medium, wherein the method comprises the following steps: acquiring environmental clutter acquired by an LFMCW radar when no unmanned aerial vehicle exists and detection echo acquired by the LFMCW radar when the unmanned aerial vehicle exists; echo simulation is carried out on each secondary detection point of the target unmanned aerial vehicle, and corresponding theoretical echo is obtained; the secondary detection points are detection points corresponding to the non-rotor wing; discretizing and matrix-combining the environmental clutter and the theoretical echo to generate a dictionary set; performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo; extracting Doppler characteristics of the rotor wing from the suppressed echoes and estimating the rotation speed and the blade length of the rotor wing; identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades; the method and the device can solve the technical problem that the target unmanned aerial vehicle cannot be identified due to the influence of clutter in a complex environment or a low-altitude environment.

Description

Unmanned aerial vehicle identification and classification method, device, equipment and medium
Technical Field
The invention relates to an unmanned aerial vehicle identification and classification method, device, equipment and medium, and belongs to the technical field of unmanned aerial vehicles.
Background
Unmanned aerial vehicles are widely used in the world today, and are highly attractive both in military and civil use. However, the civil black flying event is endlessly layered, and the interference of military use on the unmanned aerial vehicle is particularly important. The unmanned aerial vehicle of unknown model can be classified and identified remotely to carry out follow-up measure pertinently, not only can save spectral resources, but also can avoid electromagnetic interference in certain space.
The key characteristic parameters of the remote identification unmanned aerial vehicle often comprise the rotation speed of the blade, the length of the blade and the like, and from the characteristic parameters, the means for identifying the unmanned aerial vehicle becomes more and more important. Conventional radar systems face high resolution challenges in finely distinguishing small unmanned aerial vehicles. The micro Doppler is used as a micro dynamic effect of a small target, and has important significance in estimating the rotation speed and the length of the rotor blade of the unmanned aerial vehicle.
The linear frequency modulation continuous wave radar (Linear frequency modulated continuous wave, LFMCW) extracts and processes the frequency information of the echo signal by transmitting electromagnetic waves with specific waveforms to the target, thereby detecting the motion characteristics of the target, and has wide application requirements in the micro-motion target detection and estimation field.
The existing parameter estimation means are most commonly used as methods such as empirical mode decomposition, and the methods have good detection effect in open areas, but cannot identify targets under complex environments or low-altitude environments due to the influence of clutter.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an unmanned aerial vehicle identification and classification method, device, equipment and medium, which solve the technical problem that targets cannot be identified due to the influence of clutter in a complex environment or a low-altitude environment by utilizing an orthogonal matching pursuit (Orthogonal Matching Pursuit, OMP).
In order to achieve the above purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides an unmanned aerial vehicle identification and classification method, including:
acquiring environmental clutter acquired by an LFMCW radar when no unmanned aerial vehicle exists and detection echo acquired by the LFMCW radar when the unmanned aerial vehicle exists;
echo simulation is carried out on each secondary detection point of the target unmanned aerial vehicle, and corresponding theoretical echo is obtained; the secondary detection points are detection points corresponding to the non-rotor wing;
discretizing and matrix-combining the environmental clutter and the theoretical echo to generate a dictionary set;
performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
extracting Doppler characteristics of the rotor wing from the suppressed echoes and estimating the rotation speed and the blade length of the rotor wing;
and identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades.
Optionally, the theoretical echo S ri (t) is:
wherein S is ri (t) is the theoretical echo of the secondary probe point i, A ri For the amplitude of echo signal of radar receiver to non-detection point i, j is imaginary unit, f c For signal carrier frequency, c is light speed, K is frequency modulation slope of linear modulation signal, t is time, τ i For the echo time delay of radar receiver to non-detection point i, tau i ＝2R i (t)/c，R i (t) is the distance from the radar receiver to the non-detection point I at time t, I being the number of secondary detection points.
Optionally, the discretizing and matrix combining the environmental clutter and the theoretical echo to generate the dictionary set includes:
will clutter the environment S r ′ (t) and theoretical echo S ri (t) discretizing into a vector S r ′ 、S ri ：
S r ′ ＝[S r ′ (1),S r ′ (2),…S r ′ (n)…,S r ′ (N)] T
S ri ＝[S ri (1),S ri (2),…S r ′ i (n)…,S ri (N)] T ；i＝1,2,…,I
Wherein S is r ′ (n)、S r ′ i (N) is the value of the theoretical echo of the environmental clutter and the secondary detection point i at a sampling point N, wherein N is the number of the sampling points;
vector S r ′ 、S ri Performing matrix combination as atoms to generate a dictionary set D:
D＝S r1 ∪S r2 ∪,…,∪S rI ∪S r ′
wherein I is the number of sub-detection points.
Optionally, performing clutter suppression on the detected echo by using OMP algorithm based on the dictionary set to generate a suppressed echo includes:
will detect echo S r (t) discretizing into a vector S r ：
S r ＝[S r (1),S r (2),…S r (n)…,S r (N)] T
Wherein S is r (N) is the value of the detection echo at a sampling point N, wherein N is the number of the sampling points;
creating a null matrix D new Initializing residual r=s r Sequence number k=1, number p=1;
repeating steps S1-S4 until p > I+1:
s1, calculating each atom and vector S in dictionary set D r Contribution degree alpha of (2) q ：
α q ＝<D q ,S r >
Wherein D is q Q=1, 2, …, I, i+1 for the q-th atom in dictionary set D; alpha q Is atom D q And matrix S r Is used for the degree of contribution of (a),<D q ,S r >to calculate D q 、S r Is the vector inner product of (2);
s2, acquiring contribution degree alpha q The corresponding atom at maximum value is marked as D m M=1, 2, …, I, i+1; taking it as a space matrix D new Adding the k-th column vector of (2) to the empty matrix D new In (3), atom D m Deleting from the dictionary set D;
s3, according to the empty matrix D new Vector S r Calculating the minimum value of residual error r by adopting least square method, and obtaining atom D m Vector S r Is related lambda of (1) m ：
S4, let the sequence number k=k+1, the number p=p+1;
according to the vector S r Atom D m Correlation lambda m Calculating post-suppression echo
In the formula II 2 Is a two-norm.
Optionally, the extracting the doppler characteristic of the rotor from the suppressed echo and estimating the rotation speed and the blade length of the rotor includes:
performing time-frequency joint analysis on the suppressed echo to obtain a Doppler time-frequency diagram of the rotor wing;
counting the frequency peak times in the Doppler time-frequency diagram of the rotor, and estimating the rotation speed omega of the rotor according to the frequency peak times:
in the method, in the process of the invention,for the frequency peak number, +.>For the number of rotors>The number of blades per rotor;
doppler frequency shift of a Doppler time-frequency chart of the rotor is obtained, and the length L of blades of the rotor is estimated by combining the rotation speed of the rotor:
wherein f c For signal carrier frequency, c is light speed, f d Is the Doppler shift.
Optionally, the identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blade includes:
acquiring the rotating speed and the blade length of a rotor wing of the classical unmanned aerial vehicle;
comparing the target unmanned aerial vehicle with the classical unmanned aerial vehicle based on the rotation speed and the blade length of the rotor wing to obtain a comparison index J (x a ,x b )：
Wherein x is a ,x b The target unmanned plane of the a-th frame and the classical unmanned plane of the b-th frame are adopted; x is x a,μ 、μ b,μ The characteristic parameter is the mu characteristic parameter of the a-th target unmanned aerial vehicle and the b-th classical unmanned aerial vehicle, wherein when mu=1, the characteristic parameter is the rotation speed of the rotor wing, and when mu=2, the characteristic parameter is the blade length of the rotor wing;
taking comparison index J (x) a ,x b ) X at minimum a 、x b Judgment target unmanned aerial vehicle x a And classical unmanned aerial vehicle x b Are of the same class.
In a second aspect, the present invention provides an unmanned aerial vehicle recognition and classification device, the device comprising:
the echo acquisition module is used for acquiring environmental clutter acquired by the LFMCW radar when the unmanned aerial vehicle exists and detection echo acquired by the unmanned aerial vehicle exists;
the echo simulation module is used for carrying out echo simulation on each secondary detection point of the target unmanned aerial vehicle to obtain a corresponding theoretical echo; the secondary detection points are detection points corresponding to the non-rotor wing;
the dictionary set module is used for discretizing and combining the environment clutter and the theoretical echo to generate a dictionary set;
the echo suppression module is used for performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
the rotor estimation module is used for extracting Doppler characteristics of the rotor after the suppression of the echo and estimating the rotation speed and the blade length of the rotor;
and the identification and classification module is used for identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades.
In a third aspect, the present invention provides an electronic device, including a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative according to the instructions to perform steps according to the method described above.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the above method.
Compared with the prior art, the invention has the beneficial effects that:
according to the unmanned aerial vehicle identification and classification method, device, equipment and medium, the method adopts an orthogonal matching pursuit method (Orthogonal Matching Pursuit, OMP) to separate and reduce noise of environmental clutter in detected echoes and secondary detection point echoes, and accuracy of rotor parameter estimation is improved; estimating the rotation speed and the blade length of the rotor wing based on the Doppler characteristics, and further identifying and classifying the target unmanned aerial vehicle; the technical problem that a target unmanned aerial vehicle cannot be identified due to the influence of clutter in a complex environment or a low-altitude environment is solved; the device, the equipment and the medium can realize the same technical effect by adopting the method.
Drawings
Fig. 1 is a flowchart of a method for identifying and classifying unmanned aerial vehicles according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method for generating suppressed echo by performing clutter suppression on a detected echo using an OMP algorithm according to a first embodiment of the present invention;
fig. 3 is a doppler time-frequency diagram of a rotor according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment one:
as shown in fig. 1, an embodiment of the present invention provides an unmanned aerial vehicle identification and classification method, which includes the following steps:
1. acquiring environmental clutter acquired by an LFMCW radar when no unmanned aerial vehicle exists and detection echo acquired by the LFMCW radar when the unmanned aerial vehicle exists;
the present embodiment uses an AD9361 Radio Frequency (RF) agile transceiver and a broadband horn antenna to achieve transmission and reception of continuous wave signals.
2. Echo simulation is carried out on each secondary detection point of the target unmanned aerial vehicle, and corresponding theoretical echo is obtained; the secondary detection points are detection points corresponding to the non-rotor wing;
theoretical echo S ri (t) is:
wherein S is ri (t) is the theoretical echo of the secondary probe point i, A ri For the amplitude of echo signal of radar receiver to non-detection point i, j is imaginary unit, f c For signal carrier frequency, c is light speed, K is frequency modulation slope of linear modulation signal, t is time, τ i For the echo time delay of radar receiver to non-detection point i, tau i ＝2R i (t)/c，R i (t) is the distance from the radar receiver to the non-detection point I at time t, I being the number of secondary detection points.
3. Discretizing and matrix-combining the environmental clutter and the theoretical echo to generate a dictionary set;
the generating of the dictionary set specifically comprises:
will clutter the environment S r ′ (t) and theoretical echo S ri (t) discretizing into a vector S r ′ 、S ri ：
S r ′ ＝[S r ′ (1),S r ′ (2),…S r ′ (n)…,S r ′ (N)] T
S ri ＝[S ri (1),S ri (2),…S r ′ i (n)…,S ri (N)] T ；i＝1,2,…,I
Wherein S is r ′ (n)、S r ′ i (N) is the value of the theoretical echo of the environmental clutter and the secondary detection point i at a sampling point N, wherein N is the number of the sampling points;
vector S r ′ 、S ri Performing matrix combination as atoms to generate a dictionary set D:
D＝S r1 ∪S r2 ∪,…,∪S rI ∪S r ′
wherein I is the number of sub-detection points.
4. Performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
as shown in fig. 2, the generation of the post-suppression echo specifically includes:
will detect echo S r (t) discretizing into a vector S r ：
S r ＝[S r (1),S r (2),…S r (n)…,S r (N)] T
Wherein S is r (N) is the value of the detection echo at a sampling point N, wherein N is the number of the sampling points;
creating a null matrix D new Initializing residual r=s r Sequence number k=1, number p=1;
repeating steps S1-S4 until p > I+1:
s1, calculating each atom and vector S in dictionary set D r Contribution degree alpha of (2) q ：
α q ＝<D q ,S r >
Wherein D is q Q=1, 2, …, I, i+1 for the q-th atom in dictionary set D; alpha q Is atom D q And matrix S r Is used for the degree of contribution of (a),<D q ,S r >to calculate D q 、S r Is the vector inner product of (2);
s2, acquiring contribution degree alpha q The corresponding atom at maximum value is marked as D m M=1, 2, …, I, i+1; taking it as a space matrix D new Adding the k-th column vector of (2) to the empty matrix D new In (3), atom D m Deleting from the dictionary set D;
s3, according to the empty matrix D new Vector S r Calculating the minimum value of residual error r by adopting least square method, and obtaining atom D m Vector S r Is related lambda of (1) m ：
S4, let the sequence number k=k+1, the number p=p+1;
according to the vector S r Atom D m Correlation lambda m Calculating post-suppression echo
In the formula II 2 Is a two-norm.
5. Extracting Doppler characteristics of the rotor wing from the suppressed echoes and estimating the rotation speed and the blade length of the rotor wing;
estimating the rotational speed and blade length of the rotor specifically includes:
performing time-frequency joint analysis on the suppressed echo to obtain a Doppler time-frequency diagram of the rotor wing;
counting the frequency peak times in the Doppler time-frequency diagram of the rotor, and estimating the rotation speed omega of the rotor according to the frequency peak times:
in the method, in the process of the invention,for the frequency peak number, +.>For the number of rotors>The number of blades per rotor;
doppler frequency shift of a Doppler time-frequency chart of the rotor is obtained, and the length L of blades of the rotor is estimated by combining the rotation speed of the rotor:
wherein f c For signal carrier frequency, c is light speed, f d Is the Doppler shift.
As shown in fig. 3, a doppler time-frequency plot of a rotor is provided, the rotor comprising two blades, 4 flashes (frequency peak times) of blade No. 1 and blade No. 2, with ω=4 rad/s, clearly visible from the plot.
6. Identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades;
the embodiment provides an identification classification mode based on the thought of a K-means clustering algorithm, which comprises the following steps:
acquiring the rotating speed and the blade length of a rotor wing of the classical unmanned aerial vehicle;
comparing the target unmanned aerial vehicle with the classical unmanned aerial vehicle based on the rotation speed and the blade length of the rotor wing to obtain a comparison index J (x a ,x b )：
Wherein x is a ,x b The target unmanned plane of the a-th frame and the classical unmanned plane of the b-th frame are adopted; x is x a,μ 、μ b,μ The characteristic parameter is the mu characteristic parameter of the a-th target unmanned aerial vehicle and the b-th classical unmanned aerial vehicle, wherein when mu=1, the characteristic parameter is the rotation speed of the rotor wing, and when mu=2, the characteristic parameter is the blade length of the rotor wing;
taking comparison index J (x) a ,x b ) X at minimum a 、x b Judgment target unmanned aerial vehicle x a And classical unmanned aerial vehicle x b Are of the same class.
Embodiment two:
the embodiment of the invention provides an unmanned aerial vehicle identification and classification device, which comprises:
the echo acquisition module is used for acquiring environmental clutter acquired by the LFMCW radar when the unmanned aerial vehicle exists and detection echo acquired by the unmanned aerial vehicle exists;
the echo simulation module is used for carrying out echo simulation on each secondary detection point of the target unmanned aerial vehicle to obtain a corresponding theoretical echo; the secondary detection points are detection points corresponding to the non-rotor wing;
the dictionary set module is used for discretizing and combining the environment clutter and the theoretical echo to generate a dictionary set;
the echo suppression module is used for performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
the rotor estimation module is used for extracting Doppler characteristics of the rotor after the suppression of the echo and estimating the rotation speed and the blade length of the rotor;
and the identification and classification module is used for identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades.
Embodiment III:
based on the first embodiment, the embodiment of the invention provides electronic equipment, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is operative according to the instructions to perform steps according to the method described above.
Embodiment four:
based on the first embodiment, the embodiment of the present invention provides a computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the above method.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (9)

1. The unmanned aerial vehicle identification and classification method is characterized by comprising the following steps of:
acquiring environmental clutter acquired by an LFMCW radar when no unmanned aerial vehicle exists and detection echo acquired by the LFMCW radar when the unmanned aerial vehicle exists;
echo simulation is carried out on each secondary detection point of the target unmanned aerial vehicle, and corresponding theoretical echo is obtained; the secondary detection points are detection points corresponding to the non-rotor wing;
discretizing and matrix-combining the environmental clutter and the theoretical echo to generate a dictionary set;
performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
extracting Doppler characteristics of the rotor wing from the suppressed echoes and estimating the rotation speed and the blade length of the rotor wing;
and identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades.
2. The unmanned aerial vehicle identification classification method of claim 1, wherein the theoretical echo S ri (t) is:
wherein S is ri (t) is the theoretical echo of the secondary probe point i, A ri For the amplitude of echo signal of radar receiver to non-detection point i, j is imaginary unit, f c For signal carrier frequency, c is light speed, K is frequency modulation slope of linear modulation signal, t is time, τ i For the echo time delay of radar receiver to non-detection point i, tau i ＝2R i (t)/c，R i (t) is the distance from the radar receiver to the non-detection point I at time t, I being the number of secondary detection points.
3. The unmanned aerial vehicle recognition classification method of claim 1, wherein discretizing and matrix combining the environmental clutter and the theoretical echoes to generate a dictionary set comprises:
will clutter the environment S r ′ (t) and theoretical echo S ri (t) discretizing into a vector S r ′ 、S ri ：
S r ′ ＝[S r ′ (1),S r ′ (2),…S r ′ (n)…,S r ′ (N)] T
S ri ＝[S ri (1),S ri (2),…S r ′ i (n)…,S ri (N)] T ；i＝1,2,…,I
Wherein S is r ′ (n)、S r ′ i (N) is the value of the theoretical echo of the environmental clutter and the secondary detection point i at a sampling point N, wherein N is the number of the sampling points;
vector S r ′ 、S ri Performing matrix combination as atoms to generate a dictionary set D:
D＝S r1 ∪S r2 ∪,…,∪S rI ∪S r ′
wherein I is the number of sub-detection points.
4. The unmanned aerial vehicle recognition classification method of claim 3, wherein the performing clutter suppression on the detected echo using OMP algorithm based on the dictionary set to generate the suppressed echo comprises:
will detect echo S r (t) discretizing into a vector S r ：
S r ＝[S r (1),S r (2),…S r (n)…,S r (N)] T
Wherein S is r (N) is the value of the detection echo at a sampling point N, wherein N is the number of the sampling points;
creating a null matrix D new Initializing residual r=s r Sequence number k=1, number p=1;
repeating steps S1-S4 until p > I+1:
s1, calculating each atom and vector S in dictionary set D r Contribution degree alpha of (2) q ：
α q ＝<D q ,S r >
Wherein D is q Q=1, 2, …, I, i+1 for the q-th atom in dictionary set D; alpha q Is atom D q And matrix S r Is used for the degree of contribution of (a),<D q ,S r >for the purpose of measuringCalculation D q 、S r Is the vector inner product of (2);
s2, acquiring contribution degree alpha q The corresponding atom at maximum value is marked as D m M=1, 2, …, I, i+1; taking it as a space matrix D new Adding the k-th column vector of (2) to the empty matrix D new In (3), atom D m Deleting from the dictionary set D;
s3, according to the empty matrix D new Vector S r Calculating the minimum value of residual error r by adopting least square method, and obtaining atom D m Vector S r Is related lambda of (1) m ：
S4, let the sequence number k=k+1, the number p=p+1;
according to the vector S r Atom D m Correlation lambda m Calculating post-suppression echo
In the formula II 2 Is a two-norm.
5. The unmanned aerial vehicle identification classification method of claim 1, wherein the extracting doppler features of the rotor from the suppressed echoes and estimating the rotational speed and blade length of the rotor comprises:
performing time-frequency joint analysis on the suppressed echo to obtain a Doppler time-frequency diagram of the rotor wing;
counting the frequency peak times in the Doppler time-frequency diagram of the rotor, and estimating the rotation speed omega of the rotor according to the frequency peak times:
in the method, in the process of the invention,for the frequency peak number, +.>For the number of rotors>The number of blades per rotor;
doppler frequency shift of a Doppler time-frequency chart of the rotor is obtained, and the length L of blades of the rotor is estimated by combining the rotation speed of the rotor:
wherein f c For signal carrier frequency, c is light speed, f d Is the Doppler shift.
6. The unmanned aerial vehicle identification and classification method of claim 1, wherein the identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor and the blade length comprises:
acquiring the rotating speed and the blade length of a rotor wing of the classical unmanned aerial vehicle;
comparing the target unmanned aerial vehicle with the classical unmanned aerial vehicle based on the rotation speed and the blade length of the rotor wing to obtain a comparison index J (x a ,x b )：
Wherein x is a ,x b The target unmanned plane of the a-th frame and the classical unmanned plane of the b-th frame are adopted; x is x a,μ 、μ b,μ Target unmanned plane for a frame and bA mu-th characteristic parameter of the classical unmanned aerial vehicle, wherein when mu=1, the characteristic parameter is the rotation speed of the rotor wing, and when mu=2, the characteristic parameter is the blade length of the rotor wing;
taking comparison index J (x) a ,x b ) X at minimum a 、x b Judgment target unmanned aerial vehicle x a And classical unmanned aerial vehicle x b Are of the same class.
7. An unmanned aerial vehicle discernment sorter, characterized in that the device includes:
the echo acquisition module is used for acquiring environmental clutter acquired by the LFMCW radar when the unmanned aerial vehicle exists and detection echo acquired by the unmanned aerial vehicle exists;
the echo simulation module is used for carrying out echo simulation on each secondary detection point of the target unmanned aerial vehicle to obtain a corresponding theoretical echo; the secondary detection points are detection points corresponding to the non-rotor wing;
the dictionary set module is used for discretizing and combining the environment clutter and the theoretical echo to generate a dictionary set;
the echo suppression module is used for performing clutter suppression on the detected echo by adopting an OMP algorithm based on the dictionary set to generate a suppressed echo;
the rotor estimation module is used for extracting Doppler characteristics of the rotor after the suppression of the echo and estimating the rotation speed and the blade length of the rotor;
and the identification and classification module is used for identifying and classifying the target unmanned aerial vehicle according to the rotation speed of the rotor wing and the length of the blades.
8. An electronic device, comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor being operative according to the instructions to perform the steps of the method according to any one of claims 1-6.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any of claims 1-6.
CN202310694264.0A 2023-06-13 2023-06-13 Unmanned aerial vehicle identification and classification method, device, equipment and medium Pending CN116699549A (en)

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