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Radar micro-Doppler signal extraction method of rotor unmanned aerial vehicle

A micro-Doppler signal, unmanned rotor technology, applied in radio wave measurement systems, instruments and other directions, can solve problems such as difficult parameter estimation and large computational load, and achieve the effect of improving frequency estimation accuracy and detection probability.

Active Publication Date: 2019-10-29
ZHONGBEI UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Civilian UAVs are divided into two types: rotor and fixed-wing. The echo signal of the fixed-wing UAV is similar to the echo signal of the ordinary target. The echo signal of the rotor UAV includes the Doppler spectrum generated by the body. It also includes the micro-Doppler spectrum generated by the rotor. The echo signal strength of the micro-Doppler signal is generally lower than the echo signal strength generated by the airframe. It is difficult to perform accurate parameter estimation if time-frequency analysis is performed directly on it.
[0003] At present, the commonly used method is filtering after Doppler compensation, but it needs to search for the speed and acceleration information of the target, and the calculation is very heavy.

Method used

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  • Radar micro-Doppler signal extraction method of rotor unmanned aerial vehicle

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Experimental program
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Embodiment 1

[0055] Such as Figure 1 to Figure 4 The radar micro-Doppler signal extraction method of a kind of rotor drone shown, comprises the following steps:

[0056] S1: The discrete form of the detected UAV radar echo signal is:

[0057]

[0058] In formula (1), A 0 ,A p ,δ p Represents the amplitude of different components of the signal, in volts, where p∈[1,P], P is the total number of targets; f 0 is the frequency of the body echo signal, in Hz; f p is the micro-Doppler frequency of different rotor echo signals, the unit is Hz; k is the modulation frequency of the airframe echo signal, the unit is Hz / s; n is the number of signal sampling points, n∈[1,N], N is the total number of points, The unit is one; T s is the signal sampling interval in seconds; s noise is Gaussian white noise; j represents the sign of the imaginary part.

[0059] In the present embodiment one, P=1; A 0 =5,A 1 =1,δ 1 =3, the unit is volts; f 0 = 10Hz, f 1 = 2Hz; k = 0.5 Hz / s; N = 2000; T s is...

Embodiment 2

[0090] The radar micro-Doppler signal extraction method of a kind of rotor unmanned aerial vehicle that present embodiment two provides, specific situation is by figure 1 , Figure 5 , Figure 6 Jointly shown.

[0091] The specific implementation steps of the radar micro-Doppler signal extraction method of a rotor UAV in the second embodiment are as described in the first embodiment, including eight steps of S1 to S8, and the detection process is also as follows figure 1 A total of 8 processes described in the present embodiment will not be repeated in this embodiment two.

[0092] On the basis of Embodiment 1, this Embodiment 2 further verifies the effect of Δn on the micro-Doppler signal frequency estimation effect in the method of the present invention. The differences between the signal extraction method of Embodiment 2 and Embodiment 1 are as follows:

[0093] 1. The step S1 in A 0 =5,A 1 =2,δ 1 = 4;

[0094] 2. In step 4, Δn is 5 and 10 respectively;

[0095] 3. ...

Embodiment 3

[0099] The radar micro-Doppler signal extraction method of a kind of rotor UAV that present embodiment three provides, specific situation is by figure 1 , Figure 7 Jointly shown.

[0100] The specific implementation steps of the radar micro-Doppler signal extraction method of a rotor UAV in the third embodiment are as described in the first embodiment. The eight steps of S1 to S8 are in total, and the detection process is also as follows figure 1 A total of 8 processes described in the present embodiment will not be repeated in the third embodiment.

[0101] On the basis of Embodiment 1 and Embodiment 2, this Embodiment 3 further verifies the detection situation of the multi-component signal by the method of the present invention. The differences between this Embodiment 3 and the signal extraction method of Example 1 and Embodiment 2 are as follows:

[0102] 1. The number of target signals in the step S1 is p=2, the micro-Doppler frequency of signal 1 is 2 Hz, and the micro...

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Abstract

The invention relates to a radar micro-Doppler signal extraction method of a rotor unmanned aerial vehicle, belongs to the technical field of target signal detection, and aims to perform time-frequency analysis on echo signals of the rotor unmanned aerial vehicle and improve the operation efficiency. The method comprises the following steps: firstly, carrying out fractional order Fourier transformprocessing on a signal, and then filtering the signal; secondly, performing inverse fractional order Fourier transform, natural logarithm transform and Fourier transform on a processed result to estimate frequency parameters of a micro-Doppler signal; and finally, realizing the detection of the multi-component micro-Doppler signal by using different sampling interval processing methods. The method can greatly reduce impacts on the rotor micro-Doppler frequency from the Doppler frequency of a vehicle body, can complete the quick detection of a plurality of micro-Doppler component signals, andis higher in application value in the classification and recognition of unmanned aerial vehicles.

Description

technical field [0001] The invention belongs to the technical field of target signal detection, and specifically relates to a method for extracting characteristics of radar micro-Doppler signals of a rotor drone by using micro-Doppler signals. Background technique [0002] Civilian UAVs are divided into two types: rotor and fixed-wing. The echo signal of the fixed-wing UAV is similar to the echo signal of the ordinary target. The echo signal of the rotor UAV includes the Doppler spectrum generated by the body. It also includes the micro-Doppler spectrum generated by the rotor. The echo signal strength of the micro-Doppler signal is generally lower than the echo signal strength generated by the airframe. It is difficult to perform accurate parameter estimation if time-frequency analysis is performed directly on it. [0003] At present, the commonly used method is filtering after Doppler compensation, but it needs to search for the speed and acceleration information of the tar...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01S7/41G01S7/292
CPCG01S7/41G01S7/292
Inventor 庞存锁侯慧玲韩焱聂鹏飞任福明
Owner ZHONGBEI UNIV
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