Target rotor component radar imaging method based on parameter estimation and phase matching

Abstract

The application discloses a target rotor component radar imaging method based on parameter estimation and phase matching, and comprises the following steps: obtaining a target radar echo and performing pretreatment such as sampling and pulse compression to construct a radar echo matrix; combining radar working parameters to construct a filter for suppressing clutter and target rigid body part echo; combining JEM characteristics of a rotor to estimate a target rotor rotating speed, determining a target rotor imaging projection plane, completing parameter search grid division, and constructing a phase matching item; based on rotor geometric structure characteristics, estimating a target rotor blade length and blade initial phase angle; and on the basis of the imaging projection plane grid, reconstructing a target rotor image. The application can image a rotor of a small unmanned aerial vehicle, especially when rotor echo only exists in one or several distance units, still can obtain reliable rotor radar image reconstruction results and relatively accurate rotor parameter estimation, and makes up for the deficiency of the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of radar data processing, in particular to a target rotor component radar imaging method based on parameter estimation and phase matching. Background Art

[0002] While the rapid development of drones has enriched people's lives, it has also brought serious risks to national defense and public safety. Active radar detection technology, as the dominant technology for airspace monitoring, has the advantages of all-day and all-weather operation, high detection accuracy, good reliability, and strong anti-interference capabilities. It has great potential in drone target detection tasks. Because drones are typical low-speed, small targets, traditional radar target detection methods are often difficult to work with. In addition, in complex urban environments, strong clutter and interference from various floating objects also pose a huge challenge to traditional radar detection methods. Inverse Synthetic Aperture Radar (ISAR) imaging technology is a powerful radar signal processing method that can use the relative motion of the target and the radar to achieve high-resolution imaging of non-cooperative targets and further extract the target's characteristic information. Therefore, it can be used to detect drone targets.

[0003] Conventional ISAR imaging methods treat target motion as rigid motion, while the rotors are classified as the micro-moving portion of the target. VCChen pioneered the study of micro-moving components using time-frequency analysis, terming them micro-Doppler signatures. Currently, methods for processing the rotor portion of a target can be categorized into three main types. The first type treats the rotor echo as interference. The rotor echo's most prominent characteristic is its time-varying Doppler frequency and large Doppler bandwidth, allowing it to be removed using a low-pass filter. This method is simple to implement, but it results in a certain loss of target characteristic information, and the residual low-frequency component still affects the rigid-body image. Therefore, the second type of method separates the echo signals of the micro-moving components from those of the rigid body and processes them separately. Representative methods include those based on short-time Fourier transform and L statistics, empirical mode decomposition, and low-frequency matched filtering. Furthermore, methods based on Hough transform and inverse Radon transform are also used for rotor imaging. The third method calls the echo characteristics of the rotor part the jet engine modulation (JEM) phenomenon, and derives the relationship between the echo azimuth spectrum characteristics and parameters such as rotor speed and length. However, this method cannot perform radar imaging of the rotor.

[0004] Overall, research on ISAR imaging of target rotors is still in the exploratory stage. The aforementioned methods often suffer from low resolution, insufficient robustness, and high computational complexity, making them inadequate for practical applications of ISAR imaging of target rotor components on small UAVs. Summary of the Invention

[0005] The object of the present invention is to provide a target rotor component radar imaging method based on parameter estimation and phase matching.

[0006] The technical solution for achieving the purpose of the present invention is: a radar imaging method for target rotor components based on parameter estimation and phase matching, comprising the following steps:

[0007] Step 1: Obtain target radar echo and complete signal sampling and pulse compression preprocessing;

[0008] Step 2: Combine the rotor component echo signal model and use the target radar echo in step 1 to construct a target radar echo matrix in units of one frame of data;

[0009] Step 3: Based on the radar operating parameters and the target radar echo matrix in step 2, a filter is constructed to suppress clutter and the rigid body part of the target, and the selected range unit echo is filtered;

[0010] Step 4: Select the range cell where the target rotor is located and perform fast Fourier transform to estimate the target rotor speed based on the spectrum JEM characteristics;

[0011] Step 5: Determine the target rotor imaging projection plane and divide the parameter search grid, and construct the phase matching term based on the rotor component echo signal model;

[0012] Step 6: Estimate the target rotor blade length and blade initial phase angle; use the filter in step 2 to filter the phase matching term constructed in step 5, and then perform phase matching on the filtered range unit echo and calculate the maximum value Max of the spectrum amplitude in the range of f∈[-0.2PRF,0.2PRF] i , PRF is the radar pulse repetition frequency; use the target rotor geometry information to guide the parameter search direction and draw the Max i Based on the changing trend of the target rotor blade length and the initial phase angle of the blade, the target rotor blade length and the initial phase angle are estimated;

[0013] Step 7: Based on the target rotor blade parameter estimation in step 6, calculate the Max at each point of the parameter grid i The value is calculated and smoothing filtering is completed to obtain the radar image of the target rotor component.

[0014] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for radar imaging a target rotor component based on parameter estimation and phase matching is implemented.

[0015] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned target rotor component radar imaging method based on parameter estimation and phase matching.

[0016] A computer program product includes a computer program, which implements the above-mentioned target rotor component radar imaging method based on parameter estimation and phase matching when executed by a processor.

[0017] Compared with the existing technology, the present invention has the following significant advantages: 1) The present invention can image the rotor of a small UAV, especially when the rotor echo only exists in one or several distance units, and can still obtain reliable rotor radar image reconstruction results and more accurate rotor parameter estimation, which makes up for the shortcomings of the existing technology; 2) The phase matching method proposed in the present invention is not sensitive to the Doppler aliasing phenomenon of the rotor echo spectrum and can still be used under low PRF conditions; 3) The rotor parameter estimation method proposed in the present invention makes full use of the geometric structure information of the target and has a small amount of calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of the target rotor component radar imaging method based on parameter estimation and phase matching of the present invention.

[0019] Figure 2 Schematic diagram of the radar echo matrix of the measured data in Example 1.

[0020] Figure 3 This is the target range Doppler image of the measured data in Example 1.

[0021] Figure 4 Schematic diagram of the JEM characteristics of the measured target rotor echo in Example 1.

[0022] Figure 5 The measured data in Example 1 is Max under the assumption of 2 blades. i Value The curve of value change.

[0023] Figure 6 The measured data in Example 1 is Max under the assumption of 3 blades. i Value The curve of value change.

[0024] Figure 7 is the measured data Max in Example 1 i Value The curve of value change.

[0025] Figure 8 This is the radar image of the target rotor component in the measured data of Example 1. DETAILED DESCRIPTION

[0026] The present invention will be further described below with reference to the accompanying drawings.

[0027] Small rotor UAVs are typical “low, slow, and small” targets, and such targets have always been a huge challenge for traditional radar detection technology. The rotor is a key feature of a UAV, and imaging the rotor of a UAV can significantly improve the radar’s detection, classification, and recognition performance of the UAV. Due to hardware and algorithm limitations, imaging of UAV rotors, especially small UAV rotors, is still in the exploratory stage. The present invention can image the rotors of small UAVs at a relatively high resolution and estimate the rotor parameters; based on the target echo signal model, the present invention innovatively constructs a phase matching term for the rotor, and introduces rotor geometry prior information to layer and guide the parameter search process, effectively reducing the algorithm complexity and computational burden, while ensuring a relatively high-quality rotor image reconstruction result.

[0028] Combine Figure 1 , a target rotor component radar imaging method based on parameter estimation and phase matching, comprising the following steps:

[0029] Step 1: Acquire target signal. Use radar to acquire the radar echo signal of the slow-moving small rotor UAV target and complete preprocessing such as signal sampling and pulse compression.

[0030] Step 2: Construct the radar echo matrix. Construct the radar echo matrix according to a certain format using the target radar echo signal obtained in step 1. The radar echo matrix satisfies the following formula:

[0031] s(τ,t)=s R (τ,t)+s M (τ,t)

[0032]

[0033]

[0034] Where τ represents fast time, t represents slow time, B is the bandwidth of the transmitted signal, and f c is the carrier frequency, c is the speed of light; s R (τ, t) represents the echo of the rigid part of the target, which is composed of L scattering points. R,l represents the echo amplitude of the lth scattering point, represents the distance from the scattering point l to the radar at time t, is the projection of the target's velocity in the direction of the radar's line of sight; s M (τ, t) represents the echo of the micro-motion part of the target, i.e., the rotor. The rotor part consists of K scattering points. A M,k represents the echo amplitude of the kth scattering point. Assuming the rotor blade rotation center is Q, the distance from the rotor scattering point k to the radar at time t can be expressed as where R 0,Q is the distance from point Q to the radar at the initial moment, L is the distance between the scattering point l and Q, α and β are the azimuth and elevation angles of point Q respectively, θ0 is the initial phase angle of the blade rotation, ω r is the blade rotation angular velocity.

[0035] Step 3: Construct a filter to suppress clutter and the target rigid body echo. For slow small rotor UAV targets, based on the radar echo matrix in step 2, select the range unit s0(t) where the target rotor component is located, and construct a high-pass filter Hpf(t) in combination with the given radar pulse repetition frequency (PRF). This filter can filter out the target rigid body echo while minimizing the energy loss of the target rotor echo. The filtered signal can be expressed as

[0036] Step 4: Estimate the target rotor speed parameters. Perform fast Fourier transform on the raw data of the distance unit selected in step 3, select the spectral lines with periodic distribution in the spectrum, and estimate the target rotor blade rotation angular velocity according to the formula of JEM theory. in is the estimated value of the rotor blade rotational angular velocity, f T is the periodic line spectrum interval, N is the number of blades, P = 1 for even-numbered propellers, and P = 2 for odd-numbered propellers, which means that two propellers or a single propeller passes vertically through the radar line of sight at the same time.

[0037] Step 5: Determine the target rotor imaging projection plane and divide the parameter search grid to construct the phase matching term. The rotor structure of small drones commonly found on the market has distinct characteristics. Its blade distribution is symmetrical, and the blade lengths range from a few centimeters to tens of centimeters. Therefore, based on the above prior knowledge, the rotor parameter search grid can be determined. Furthermore, based on the target rotor speed parameter estimated in step 4, the phase matching term e(t) is constructed to satisfy:

[0038]

[0039] in It represents the length of the target rotor blade on the imaging projection plane, which differs from the physical length of the blade by a coefficient cosβ; It represents the initial phase angle of the blade on the imaging projection surface during the imaging observation time.

[0040] Step 6: Estimate the target rotor blade length and blade initial phase angle. Based on the phase matching term in step 5, combined with the radar bandwidth and the number of range gates spanned by the rotor echo in the target radar echo matrix, let A smaller value, such as Where Δr is the length of a range gate; take an appropriate step length like make The phase matching term can be expressed as Use the filter Hpf(t) constructed in step 3 to match the phase term e i (t) is filtered, that is, Then the echo signal after filtering in step 3 is Perform phase matching and perform fast Fourier transform, that is, Finally, near the 0 Doppler frequency, such as in the range of [-0.2PRF, 0.2PRF], calculate and record the maximum amplitude of the spectrum, that is, For different Repeat the above steps and draw Max i The change curve of the curve peak corresponds to This is the estimated value of the initial phase angle of each blade of the rotor. Based on the appropriate step size, such as According to the above process Search, according to Max i The amplitude change of the value can complete the Estimates.

[0041] Step 7: Reconstruct the radar image of the target rotor component. Based on the target rotor blade parameter estimation in step 6, construct the polar coordinate grid of the target rotor imaging projection plane and calculate the Max i The radar image of the target rotor component can be obtained by performing smoothing filtering.

[0042] The present invention will be further described below in conjunction with the embodiments:

[0043] Example

[0044] Combine Figure 1 , a target rotor component radar imaging method based on parameter estimation and phase matching, comprising the following steps:

[0045] Step 1: Acquire the target signal. Use the radar to acquire the radar echo signal of the slow-moving small rotor UAV target and complete preprocessing tasks such as signal sampling and pulse compression. The radar used in this embodiment is an FMCW system. The signal form of the transmitted waveform is a frequency-modulated continuous sawtooth wave with a starting frequency of 77 GHz, a bandwidth of 3.6 GHz, a range gate length of Δr = 0.0417 m, a PRF of 10 kHz, and a total of 2048 chirp signals per frame. The UAV used is a DJI mini2 small UAV.

[0046] Step 2: Construct radar echo matrix. Construct radar echo matrix according to a certain format using the target radar echo signal obtained in step 1, such as Figure 2 As shown, each column is a chirp echo signal. The radar echo matrix satisfies the following formula:

[0047] s(τ,t)=s R (τ,t)+s M (τ,t)

[0048]

[0049]

[0050] Where τ represents fast time, t represents slow time, B is the bandwidth of the transmitted signal, and f c is the carrier frequency, c is the speed of light; s R (τ, t) represents the echo of the rigid part of the target, which is composed of L scattering points. R,l represents the echo amplitude of the lth scattering point, represents the distance from the scattering point l to the radar at time t, is the projection of the target's velocity in the direction of the radar's line of sight; s M (τ, t) represents the echo of the micro-motion part of the target, i.e., the rotor. The rotor part consists of K scattering points. A M,k represents the echo amplitude of the kth scattering point. Assuming the rotor blade rotation center is Q, the distance from the rotor scattering point k to the radar at time t can be expressed as where R 0,Q is the distance from point Q to the radar at the initial moment, L is the distance between the scattering point l and Q, α and β are the azimuth and elevation angles of point Q respectively, θ0 is the initial phase angle of the blade rotation, ω r is the blade rotation angular velocity.

[0051] Step 3: Construct a filter to suppress clutter and the echo of the target rigid body. For the slow small rotor UAV target, based on the radar echo matrix in step 2, select the distance unit s0(t) where the target rotor component is located. Figure 3The target range-Doppler image shown shows that the rotor component is located in the range unit of approximately 82-88, and the clutter and the rigid part of the target fuselage are located near the 0 Doppler frequency. A high-pass filter Hpf(t) is constructed based on the given radar pulse repetition frequency (PRF) to filter out the target rigid part while minimizing the energy loss of the target rotor part echo. The filtered signal can be expressed as

[0052] Step 4: Estimate the target rotor speed parameters. Perform fast Fourier transform on the raw data of the distance unit selected in step 3, and the result is as follows: Figure 4 As shown in the figure, the periodically distributed spectrum lines in the spectrum are selected, and the target rotor blade rotation angular velocity is estimated according to the formula of JEM theory. in is the estimated value of the rotor blade rotational angular velocity, f T is the periodic line spectrum interval, N is the number of blades, P = 1 for even-numbered propellers, and P = 2 for odd-numbered propellers, which means that both propellers or a single propeller pass through the radar line of sight vertically at the same time. Figure 3 We can know f T =61×(10 4 / 2048)=297.8516Hz. Since most small drones have two or three blades, the possible value of the target rotor speed can be calculated based on this prior information. (corresponding to 2 blades) and (Corresponding to 3 blades).

[0053] Step 5: Determine the target rotor imaging projection plane and divide the parameter search grid to construct the phase matching term. The rotor structure of small drones commonly found on the market has distinct characteristics. Its blade distribution is symmetrical, and the blade lengths range from a few centimeters to tens of centimeters. Therefore, based on the above prior knowledge, the rotor parameter search grid can be determined. Furthermore, based on the target rotor speed parameter estimated in step 4, the phase matching term e(t) is constructed to satisfy:

[0054]

[0055] in It represents the length of the target rotor blade on the imaging projection plane, which differs from the physical length of the blade by a coefficient cosβ; It represents the initial phase angle of the blade on the imaging projection surface during the imaging observation time.

[0056] Step 6: Estimate the target rotor blade length and blade initial phase angle. Based on the phase matching term in step 5, combined with the radar bandwidth and the number of range gates spanned by the rotor echo in the target radar echo matrix, let Take the step length make The phase matching term can be expressed as Use the filter Hpf(t) constructed in step 3 to match the phase term e i (t) is filtered, that is, Then the echo signal after filtering in step 3 is Perform phase matching and perform fast Fourier transform, that is, Finally, near the 0 Doppler frequency, such as in the range of [-0.2PRF, 0.2PRF], calculate and record the maximum amplitude of the spectrum, that is, For different Repeat the above steps and draw Max i The change curve of the curve peak corresponds to This is the estimated value of the initial phase angle of each rotor blade. Under the 2-blade assumption, Max i The change curve is as follows Figure 5 As shown, Figure 5 The curve has two obvious peaks, and the interval is about π, which is in line with expectations. Under the 3-blade assumption, Max i The change curve is as follows Figure 6 As shown, Figure 6 The curve has multiple irregular peaks with uncertain intervals between them. Therefore, it can be determined that the target rotor has two blades, and the initial phase angles of the blades during the imaging observation time are approximately 2.48 rad and 5.62 rad, respectively.

[0057] The estimated initial phase angle of one blade Based on the step size According to the above process Search and draw Max i The amplitude of the value changes as Figure 7 As shown, from Figure 7 Available The estimated value is about 0.11m.

[0058] Step 7: Reconstruct the radar image of the target rotor component. Based on the target rotor blade parameter estimation in step 6, combined with the polar coordinate grid of the target rotor imaging projection plane, calculate the Max i The radar image of the target rotor component can be obtained by performing smoothing filtering. The result is as follows Figure 8 shown.

[0059] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A radar imaging method for target rotor components based on parameter estimation and phase matching, characterized in that: The following steps are involved: Step 1: Obtain target radar echo and complete signal sampling and pulse compression preprocessing; Step 2: Combine the rotor component echo signal model and use the target radar echo in step 1 to construct a target radar echo matrix in units of one frame of data; Step 3: Based on the radar operating parameters and the target radar echo matrix in step 2, a filter is constructed to suppress clutter and the rigid body part of the target, and the selected range unit echo is filtered; Step 4: Select the range cell where the target rotor is located and perform fast Fourier transform to estimate the target rotor speed based on the spectrum JEM characteristics; Step 5: Determine the target rotor imaging projection plane and divide the parameter search grid, and construct the phase matching term based on the rotor component echo signal model; The phase matching term constructed by combining the rotor component echo signal model is expressed as in It represents the length of the target rotor blade on the imaging projection plane, which differs from the physical length of the blade by a coefficient cosβ; represents the initial phase angle of the blade on the imaging projection surface during the imaging observation time, f c is the carrier frequency, c is the speed of light, is the estimated value of the rotor blade rotational angular velocity; Step 6: Estimate the target rotor blade length and blade initial phase angle; use the filter in step 2 to filter the phase matching term constructed in step 5, and then perform phase matching on the filtered range unit echo and calculate the maximum value Max of the spectrum amplitude in the range of f∈[-0.2PRF,0.2PRF] i , PRF is the radar pulse repetition frequency; use the target rotor geometry information to guide the parameter search direction and draw the Max i Based on the changing trend of the target rotor blade length and the initial phase angle of the blade, the target rotor blade length and the initial phase angle are estimated; The estimation method of the target rotor blade length and the blade initial phase angle is as follows: based on the phase matching term, combined with the radar bandwidth and the number of range gates spanned by the rotor echo in the target radar echo matrix, let for Where Δr is the length of a range gate; take the step length make The phase matching term is expressed as Use the filter Hpf(t) constructed in step 3 to match the phase term e i (t) is filtered, that is, Then the echo signal after filtering in step 3 is Perform phase matching and perform fast Fourier transform, that is, Finally, in the frequency domain f∈[-0.2PRF,0.2PRF], calculate and record the maximum amplitude of the spectrum, that is, For different Repeat the above steps and draw Max i The change curve of the curve peak corresponds to That is the estimated value of the initial phase angle of each blade of the rotor; at the estimated value of the initial phase angle of any blade Based on the step size According to the above process Search, according to Max i The amplitude change of the value completes the estimates; Step 7: Based on the target rotor blade parameter estimation in step 6, calculate the Max at each point of the parameter grid i The value is calculated and smoothing filtering is completed to obtain the radar image of the target rotor component.

2. The target rotor component radar imaging method based on parameter estimation and phase matching according to claim 1, characterized in that: The target rotor speed estimation value obtained in step 4 based on the spectrum JEM characteristics is calculated as follows: f T is the periodic line spectrum interval, N is the number of blades, P=1 for even-numbered propellers, and P=2 for odd-numbered propellers.

3. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 2 are implemented.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 2 are implemented.

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