CN113204021A - TOAF-based ISAR imaging method for complex target - Google Patents

TOAF-based ISAR imaging method for complex target Download PDF

Info

Publication number
CN113204021A
CN113204021A CN202110412307.2A CN202110412307A CN113204021A CN 113204021 A CN113204021 A CN 113204021A CN 202110412307 A CN202110412307 A CN 202110412307A CN 113204021 A CN113204021 A CN 113204021A
Authority
CN
China
Prior art keywords
signal
cubic phase
frequency
radar echo
representing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110412307.2A
Other languages
Chinese (zh)
Inventor
李艳艳
赵飞凯
周延
刘君一
汪霖
张万绪
陈晓璇
姜博
孟娜
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Northwestern University
Original Assignee
Northwestern University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Northwestern University filed Critical Northwestern University
Priority to CN202110412307.2A priority Critical patent/CN113204021A/en
Publication of CN113204021A publication Critical patent/CN113204021A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
    • G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
    • G01S13/904—SAR modes
    • G01S13/9064—Inverse SAR [ISAR]
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Radar Systems Or Details Thereof (AREA)

Abstract

The invention provides a TOAF-based ISAR imaging method for a complex moving target, which comprises the following steps: acquiring radar echo signals of scattering points of a target to be detected in each distance unit; carrying out distance walking compensation on the radar echo signal; modeling the radar echo signal after the distance walking compensation into a cubic phase signal; performing parameter estimation on the cubic phase signal by adopting a third-order autocorrelation function and a nonlinear frequency modulation technology to obtain an estimated parameter; according to the estimated parameters, performing Doppler frequency shift compensation on the square phase signal; and reconstructing to obtain an ISAR image of the target to be detected by using the cubic phase signal after Doppler frequency shift compensation. The invention can quickly complete the calculation process through complex multiplication, non-uniform fast Fourier transform and other operations, and can improve the anti-noise performance.

Description

TOAF-based ISAR imaging method for complex target
Technical Field
The invention belongs to the technical field of inverse synthetic aperture radar imaging, and particularly relates to a TOAF-based ISAR imaging method for a complex target.
Background
Inverse Synthetic Aperture Radar (ISAR), an effective non-cooperative target identification technique, has been extensively studied over the past decades. For targets with constant Doppler shift during imaging, the Range-Doppler (RD) ISAR imaging algorithm is an effective method. In practice, however, the target tends to be mobile and the doppler frequency is time varying. Therefore, in the prior art, a common imaging technique for the above situation is a Range-Instantaneous-Doppler (RID) technique.
With respect to RID imaging algorithms, there are generally two categories that can be classified: linear algorithms and non-linear algorithms. The linear algorithm mainly includes Maximum Likelihood (ML) and Modified discrete Fourier transform (MDCFT), and the nonlinear algorithm includes a Cubic Phase Function (CPF), Local Polynomial Wigner Distribution (LPWD), kernel time-linear modulation frequency Distribution (KTCRD), variable Scale Fourier Transform (SFT), Chirp-Quadratic modulation Rate Distribution (CRQCRD), Modified Lv's Distribution (MLVD ), and Modified Chirp-Quadratic modulation Rate Distribution (mcrq, etc.).
In the linear algorithm and the nonlinear algorithm, part of the algorithms have lower anti-noise performance, part of the algorithms have complex calculation processes, and the imaging effect is not good due to noise interference.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a TOAF-based ISAR imaging method for a complex target. The technical problem to be solved by the invention is realized by the following technical scheme:
the invention provides a TOAF-based ISAR imaging method for a complex moving target, which comprises the following steps:
acquiring radar echo signals of scattering points of a target to be detected in each distance unit;
carrying out distance walking compensation on the radar echo signal;
modeling the radar echo signal after the distance walking compensation into a cubic phase signal;
performing parameter estimation on the cubic phase signal by adopting a third-order autocorrelation function and a nonlinear frequency modulation technology to obtain an estimated parameter;
according to the pre-estimated parameters, performing Doppler frequency shift compensation on the cubic phase signal;
and reconstructing to obtain an ISAR image of the target to be detected by using the cubic phase signal after Doppler frequency shift compensation.
Optionally, the cubic phase signal is:
Figure BDA0003024641890000021
wherein L represents the L-th range bin, L represents the total range bin number, tnDenotes the slow time, P denotes the number of scattering points in the l-th range bin, ApRepresenting the amplitude, alpha, of the radar echo signal at the p-th scattering pointpRepresenting the center frequency, beta, of the radar echo signal of the p-th scattering pointpFrequency modulation, gamma, of radar echo signal representing the p-th scattering pointpRepresenting the quadratic frequency of the radar echo signal of the p-th scattering point.
Wherein the pre-estimated parameters include: a frequency modulation rate estimate, a secondary frequency modulation rate estimate, a center frequency estimate, and an amplitude estimate.
Optionally, the performing parameter estimation on the cubic phase signal by using a third-order autocorrelation function and a demodulation chirp technique to obtain an estimated parameter includes:
processing the cubic phase signal by adopting a third-order autocorrelation function to obtain an estimation model of a radar echo signal of the scattering point;
the third order autocorrelation function is:
TOAF(tn,τn)＝x(tn+τn)x(tn-τn)x*(2tn)
wherein, taunRepresenting a delay variable, x*(2tn) Denotes x (2 t)n) Conjugation of (1);
the estimation model is as follows:
Figure BDA0003024641890000031
accumulating the signal energy along a delay axis by performing integral operation on the estimation model to obtain an integrated estimation model;
the integrated estimation model is as follows:
Figure BDA0003024641890000032
wherein,
Figure BDA0003024641890000033
representing a time-delay variable τnCorresponding frequency domain, δ (-) denotes the impulse function, A'pRepresenting an amplitude value;
performing time-varying phase term compensation on the integrated estimation model by using a compensation function to obtain a compensated estimation model;
the compensation function is:
Figure BDA0003024641890000041
compensated estimation model:
Figure BDA0003024641890000042
carrying out Hough transform on the compensated estimation model, and obtaining a frequency modulation rate estimation value and a secondary frequency modulation rate estimation value of the radar echo signal of the scattering point by a peak detection technology;
Figure BDA0003024641890000043
Figure BDA0003024641890000044
wherein, betapFrequency modulation estimate, gamma, of radar echo signal representing the p-th scattering pointpEstimated secondary frequency modulation value beta of radar echo signal representing p scattering pointsIndicating the search modulation frequency, gammasIndicating the search quadratic frequency, R (beta)s,γs) Representing the cubic phase signal x for the l-th range unitl(tn) The result after the Hough transform, A ″)pRepresenting the amplitude of the echo signal;
processing the cubic phase signal according to the frequency modulation rate estimation value and the secondary frequency modulation rate estimation value;
and based on the processed cubic phase signal, obtaining a central frequency estimation value and an amplitude estimation value of the radar echo signal of the scattering point by utilizing a de-chirp technology.
Optionally, processing the cubic phase signal according to the frequency modulation estimated value and the quadratic modulation frequency estimated value includes:
processing the cubic phase signal using a processing function;
wherein the processing function is:
Figure BDA0003024641890000045
optionally, the obtaining, based on the processed cubic phase signal, a center frequency estimated value and an amplitude estimated value of the radar echo signal of the scattering point by using a dechirp technique includes:
determining a calculation formula of a central frequency estimation value and an amplitude estimation value by utilizing a de-chirp technology based on the processed cubic phase signal;
calculating to obtain a central frequency estimated value and an amplitude estimated value of the radar echo signal of the scattering point by using a calculation formula for determining the central frequency estimated value and the amplitude estimated value;
the calculation formula of the central frequency estimated value and the amplitude estimated value is as follows:
Figure BDA0003024641890000051
wherein,
Figure BDA0003024641890000052
an amplitude estimate of the radar return signal representing the p-th scattering point,
Figure BDA0003024641890000053
representing the estimated value of the center frequency of the radar echo signal of the p-th scattering point, D' representing the peak amplitude of the signal obtained after fast Fourier transform,
Figure BDA0003024641890000055
indicating a slow time tnThe corresponding frequency domain.
Optionally, the performing doppler shift compensation on the cubic phase signal according to the pre-estimated parameter includes:
compensating the cubic phase signals of scattering points in each distance unit by using a Doppler frequency shift compensation formula according to the estimated parameters to obtain the cubic phase signals after Doppler frequency shift compensation;
the Doppler frequency shift compensation formula is as follows:
Figure BDA0003024641890000054
compared with the prior art, the invention has the beneficial effects that:
according to the TOAF-based ISAR imaging method for the complex target, provided by the invention, the three-order autocorrelation function is used, the time delay variable is integrated, the signal energy accumulation is realized, the time delay phase term is compensated, and the anti-noise performance and the estimation accuracy of the algorithm are effectively improved.
The TOAF-based ISAR imaging method for the complex target provided by the invention adopts Hough Transform, reduces energy loss, and finally completes the calculation process through operations such as complex multiplication, Non-uniform Fast Fourier Transform (NUFFT) and the like.
The foregoing description is only an overview of the technical solutions of the present invention, and in order to make the technical means of the present invention more clearly understood, the present invention may be implemented in accordance with the content of the description, and in order to make the above and other objects, features, and advantages of the present invention more clearly understood, the following preferred embodiments are described in detail with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic diagram of an ISAR imaging model of a complex moving object based on TOAF according to an embodiment of the present invention;
FIG. 2 is a flowchart of an ISAR imaging method for a complex moving object based on TOAF according to an embodiment of the present invention;
fig. 3 is a graph of signal energy distribution over a slow time-delay plane provided by an embodiment of the invention;
FIG. 4 is a histogram of relatively slow time provided by an embodiment of the invention;
FIG. 5 is a graph showing a simulation of a CR-QCR profile provided by an embodiment of the present invention;
fig. 6 is a simulation diagram of a CR-QCR according to an embodiment of the present invention;
FIG. 7 is a diagram of frequency modulation rate estimation performance simulation results based on LPWD, SFT, CRQCRD, and TOAF, respectively, according to an embodiment of the present invention;
FIG. 8 is a diagram of performance simulation results of second-order frequency estimation based on LPWD, SFT, CRQCRD, and TOAF, respectively, according to an embodiment of the present invention;
FIG. 9 is a diagram of simulation results of computational complexity based on SFT, CRQCRD, and TOAF, respectively, according to an embodiment of the present invention;
FIG. 10 is a diagram of a set of ideal scatterer models for wave vessel modeling provided by an embodiment of the present invention;
fig. 11 is a diagram illustrating simulation results of an RD method without doppler frequency compensation according to an embodiment of the present invention;
FIG. 12 is a diagram of simulation results after Doppler frequency compensation according to an embodiment of the present invention;
FIG. 13 is a diagram illustrating the simulation results of Doppler shift compensation based on LPWD, SFT, CRQCRD, and TOAF, respectively, when the input SNR is-1 dB according to an embodiment of the present invention;
FIG. 14 is a diagram of simulation results of Doppler shift compensation based on LPWD, SFT, CRQCRD, and TOAF, respectively, when the input signal-to-noise ratio provided by the embodiment of the present invention is-6 dB;
FIG. 15 is a graph of the results of LPWD-based ISAR imaging provided by embodiments of the present invention;
FIG. 16 is a graph of the results of SFT-based ISAR imaging provided by embodiments of the present invention;
FIG. 17 is a graph of the results of CRQCRD-based ISAR imaging provided by embodiments of the present invention;
fig. 18 is a diagram of the result of TOAF-based ISAR imaging provided by an embodiment of the present invention.
Detailed Description
In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined object, the following describes in detail a TOAF-based ISAR imaging method for a complex moving object according to the present invention with reference to the accompanying drawings and the detailed description.
On the basis of a nonlinear algorithm, on the basis that the anti-noise performance and cross term suppression can be further improved by considering a low-order autocorrelation function method, the invention provides a TOAF-based ISAR imaging method for a complex moving target, and imaging is realized on the basis of a third-order autocorrelation function (TOAF) parameter estimation method.
Example one
Referring to fig. 1, fig. 1 is a schematic diagram of an ISAR imaging model of a complex moving object based on TOAF according to an embodiment of the present invention, as shown in fig. 1, a center O of a cartesian coordinate system XYZ is a target rotation center, a vector R represents a unit vector in a line of sight (LOS) direction of a radar, and v is a unit vectorrAnd vector w represents the translational and rotational components of the object motion, respectively. Wherein the vector w can be decomposed into components w parallel to RRAnd a component w perpendicular to Re，wRAnd weIn (1) only weContributes to ISAR imaging, therefore, weReferred to as the effective rotation vector.
Scattering point p (x)p,yp,zp) Is any scattering point on the object, vector rpIs the direction vector from the center point O to the scattering point p. Taking into account the translational component vrThen its radial movement velocity is vr+(rp×we) R, '×' and '·' denote the outer and inner products, respectively. From the radial motion velocity, the doppler frequency of the scattering point p can be found:
Figure BDA0003024641890000081
in the formula, λ represents the wavelength of the radar transmission signal. For complex moving objects, vrAnd weExpressed as:
Figure BDA0003024641890000082
wherein v is0Representing the initial velocity, alpha, of the translational component0Acceleration, r, representing a translational component0Jerk representing a translational component, a represents a rotational velocity vector weB represents a rotational velocity vector weK denotes a rotational velocity vector weThe second order term of (2).
Referring to fig. 2, fig. 2 is a flowchart of an ISAR imaging method for a complex moving object based on TOAF according to an embodiment of the present invention, and as shown in fig. 2, the ISAR imaging method for a complex moving object based on TOAF according to the embodiment includes:
s1: acquiring radar echo signals of scattering points of a target to be detected in each distance unit;
the distance units are divided according to the distance resolution of the radar, and in the radar image, when two targets are located at the same azimuth angle but different from the radar in distance, the minimum distance between the two targets and the radar is the distance resolution. It is generally defined that the distance between two targets is the distance resolution when the falling edge of the echo pulse of a closer target just coincides with the rising edge of the echo of a farther target, as the limit of the resolution.
S2: and performing distance walking compensation on the radar echo signal.
The radar is supposed to transmit a linear frequency modulation signal as follows;
Figure BDA0003024641890000091
the baseband echo signal of the scatterer can be written as:
Figure BDA0003024641890000092
wherein R isp(tm) The sinc function is the echo envelope and exp is the echo phase for the instantaneous distance between the target and the radar.
As can be seen from the above formula (4), since Rp(tm) There is a speed of the target that causes the envelope to walk, which is reflected in the distance cell, which is a distance walk phenomenon that causes a loss of energy accumulation, so it is necessary to compensate for the distance walk.
S3: modeling the radar echo signal with the walking compensation distance as a cubic phase signal;
assuming that the radar transmits a chirp signal, the radar echo signal of the scattering point p after being subjected to pulse compression is:
Figure BDA0003024641890000093
where t denotes the fast time, tnRepresents the slow time, δpRepresenting a constant amplitude of the signal, B representing the transmitted signal bandwidth, c representing the speed of light, z (t)n) Representing complex white Gaussian noise, aa(tn) Denotes the observation time, Rp(tn) Is shown at tnThe distance from the scattering point p to the radar at the moment is expressed as follows:
Figure BDA0003024641890000101
wherein R isp(t0) Is shown at an initial time t0Distance of scattering point p to the radar.
If the number of scattering points in the ith distance unit is P, the cubic phase signal is modeled after distance walk compensation and phase error correction caused by walk:
Figure BDA0003024641890000102
wherein L represents the L-th range bin, L represents the total range bin number, tnDenotes the slow time, P denotes the number of scattering points in the l-th range bin, ApRepresenting the amplitude, alpha, of the radar echo signal at the p-th scattering pointpDenotes the Center Frequency (CF), β, of the radar echo signal at the p-th scattering pointpIndicating the frequency modulation (CR), gamma, of the radar echo signal at the p-th scattering pointpRepresenting the Quadratic frequency (QCR ) of the radar echo signal at the p-th scattering point, z (t)n) Representing additive complex white gaussian noise.
Figure BDA0003024641890000103
Wherein, γT＝[(γpyRz-γpzRy),(γpzRx-γpxRz),(γpxRy-γpyRx)]，γpxRepresents gammapComponent along the x-axis, γpyRepresents gammapComponent along the y-axis, γpzRepresents gammapComponent along the z-axis, RxRepresenting the component of R along the x-axis, RyRepresenting the component of R along the y-axis, RzRepresenting the component of R along the z-axis.
As can be seen from equations (7) and (8), the existence of CR and QCR in the Cubic Phase Signal (CPS) causes doppler shift, so that the conventional RD method is no longer applicable, and in order to obtain ISAR imaging with good focusing, parameter estimation needs to be performed on the CPS.
S4: performing parameter estimation on the cubic phase signal by adopting a Third Order Autocorrelation Function (TOAF) and a linear frequency modulation technology to obtain an estimated parameter;
wherein the pre-estimated parameters include: a frequency modulation rate estimate, a secondary frequency modulation rate estimate, a center frequency estimate, and an amplitude estimate.
Specifically, the method comprises the following steps:
s41: processing the cubic phase signal by a third-order autocorrelation function to obtain a frequency modulation rate estimation value and a secondary frequency modulation rate estimation value of the radar echo signal of the scattering point;
specifically, the method comprises the following steps:
s411: and processing the cubic phase signal by adopting a third-order autocorrelation function to obtain an estimation model of the radar echo signal of the scattering point.
The third order autocorrelation function is:
TOAF(tn,τn)＝x(tn+τn)x(tn-τn)x*(2tn) (9)
the estimation model is as follows:
R0(tn,τn)＝Ap 3 exp[-j2π(βp+γptn)tn 2]exp[-j2π(βp+γptn)τn 2] (10)
wherein, taunRepresenting a delay variable, x*(2tn) Denotes x (2 t)n) Conjugation of (1).
S412: accumulating the signal energy along a delay axis by performing integral operation on the estimation model to obtain an integrated estimation model;
the integral over the delay variable is expressed as:
Figure BDA0003024641890000111
the integrated estimation model is:
Figure BDA0003024641890000112
wherein,
Figure BDA0003024641890000121
representing a time-delay variable τnCorresponding frequency domain, δ representing impulse function, A'pRepresenting the amplitude.
S413: and performing time-varying phase term compensation on the integrated estimation model to obtain a compensated estimation model.
The compensation function is:
Figure BDA0003024641890000125
compensated estimation model:
R1(tn,Fτn)＝A′pδ[Fτn-(βp+γptn)] (14)
carrying out Hough transform on the compensated estimation model, and obtaining the frequency modulation rate estimation value and the secondary frequency modulation rate estimation value of the radar echo signal of the scattering point by a peak detection technology, wherein,
Figure BDA0003024641890000122
Figure BDA0003024641890000123
wherein, betapFrequency modulation estimate, gamma, of radar echo signal representing the p-th scattering pointpEstimated secondary frequency modulation value beta of radar echo signal representing p scattering pointsIndicating the search modulation frequency, gammasIndicating a search for a quadratic frequency. R (beta)s,γs) Representing the cubic phase signal s for the l-th range celll(tn) The result after the Hough transform, A ″)pRepresenting the amplitude of the echo signal.
S414: processing the cubic phase signal according to the frequency modulation rate estimation value and the secondary frequency modulation rate estimation value;
specifically, the method comprises the following steps:
processing the cubic phase signal using a processing function;
wherein the processing function is:
Figure BDA0003024641890000124
s415: and based on the processed cubic phase signal, obtaining a central frequency estimation value and an amplitude estimation value of the radar echo signal of the scattering point by utilizing a de-chirp technology.
Specifically, the method comprises the following steps:
s415 a: determining a calculation formula of a central frequency estimation value and an amplitude estimation value by utilizing a de-chirp technology based on the processed cubic phase signal;
s415 b: calculating to obtain a central frequency estimated value and an amplitude estimated value of the radar echo signal of the scattering point by using a calculation formula for determining the central frequency estimated value and the amplitude estimated value;
the calculation formula of the central frequency estimated value and the amplitude estimated value is as follows:
Figure BDA0003024641890000131
wherein,
Figure BDA0003024641890000132
an amplitude estimate of the radar return signal representing the p-th scattering point,
Figure BDA0003024641890000133
representing the estimated value of the center frequency of the radar echo signal of the p-th scattering point, D' representing the peak amplitude of the signal obtained after fast Fourier transform,
Figure BDA0003024641890000135
indicating a slow time tnThe corresponding frequency domain.
S5: according to the pre-estimated parameters, performing Doppler frequency shift compensation on the cubic phase signal;
the method specifically comprises the following steps:
compensating the cubic phase signals of scattering points in each distance unit by using a Doppler frequency shift compensation formula according to the estimated parameters to obtain the cubic phase signals after Doppler frequency shift compensation;
the Doppler frequency shift compensation formula is as follows:
Figure BDA0003024641890000134
s6: and reconstructing to obtain an ISAR image of the target to be detected by using the cubic phase signal after Doppler frequency shift compensation.
S7: and obtaining the ISAR image of the target to be measured by using the cubic phase signal after Doppler frequency shift compensation and adopting a range-Doppler imaging technology of parameter estimation.
In the TOAF-based ISAR imaging method for the complex target, Hough transform is adopted, energy loss is reduced, and finally, the calculation process is completed through complex multiplication, NUFFT and other operations; the three-order autocorrelation function is used, the time delay variable is integrated, the signal energy accumulation is realized, the time delay phase term is compensated, and the anti-noise performance and the estimation accuracy of the algorithm are effectively improved.
Example two
The present embodiment is a simulation experiment of the ISAR imaging method of the complex moving object based on TOAF in the first embodiment.
Simulation 1
Referring to fig. 3-6, fig. 3-6 are graphs showing simulation results of cross term suppression performance according to an embodiment of the present invention. The simulation parameters are as follows: consider a signal with three CPS' S identified by S1, S2, and S3, respectively, with a zero-mean white Gaussian noise in the simulated signal. The effective length of the emulated signal is 2s and the sampling rate of the emulated signal is 128 Hz.
A in Signal S11＝1.0,αp,1＝10Hz,βp,1＝15Hz/s,γp,1＝30Hz/s2(ii) a A in Signal S22＝0.9,αp,2＝10Hz,βp,2＝-20Hz/s,γp,2＝30Hz/s2(ii) a A in Signal S33＝0.8,αp,3＝10Hz,βp,3＝15Hz/s,γp,3＝-40Hz/s2. The simulation experiment proves the cross interference item inhibition performance of the TOAF algorithm.
The distribution of signal energy on the slow time-delay plane after the operation of equation (9) is shown in fig. 3. Due to the definition of equation (9) and the use of redundant information, the shape of the distribution is now a square instead of a diamond, which is beneficial to the output signal-to-noise ratio. Integration operation is then performed along the delay axis to obtain a frequency distribution of relatively slow time as shown in fig. 4, and it can be seen that three self terms have been accumulated on three diagonals. Since QCR is the same, S1 is parallel to S2. Furthermore, due to the same CR, S1 and S3 intersect at point (0, 15 Hz/S).
After phase compensation and coherent accumulation, a CR-QCR distribution as shown in fig. 5 is obtained. As can be seen from the corresponding perspective view of fig. 6, three well-focused peaks occur, so that the target is easily detected. From the peak coordinates on the perspective, the CR and QCR of the three-component CPS can be accurately estimated.
The simulation experiment proves the suppression performance of the cross interference item of the TOAF algorithm, and the three-dimensional simulation result chart in fig. 6 can show that when the multi-component CPS exists in the simulation signal, the integration process in the algorithm only accumulates the signal energy of the items S1, S2 and S3, and the (beta) items S1, S2 and S3 of the radar simulation signal can be obtained through the peak detection technologyp,1,γp,1)、(βp,2,γp,2) And (beta)p,3,γp,3) Are respectively (beta)p,1＝15Hz/s,γp,1＝30Hz/s2)、(βp,2＝-20Hz/s,γp,1＝30Hz/s2) And (beta)p,3＝15Hz/s,γp,3＝-40Hz/s2) And moreover, the energy of the cross interference term is not accumulated like the self term, so that the TOAF algorithm does not interfere the parameter estimation performed on the self terms S1, S2 and S3 in the process of energy accumulation.
Simulation 2
Referring to fig. 7-8, fig. 7-8 are graphs comparing simulation results with estimated performance provided by embodiments of the present invention. Mean Square Errors (MSEs) are typically used to characterize the accuracy of the estimate. The simulation parameters are as follows: CF. CR and QCR are respectively: 18Hz, 5Hz/s, 10Hz/s2. The sampling frequency and the effective signal length are respectively 128Hz and 2s, the signal is added with Gaussian white noise, and the input signal-to-noise ratio is [ -12:1:2 [)]dB. Each input signal-to-noise ratio value was tested 500 times. Both TOAF and CRQCRD based methods use 2s redundancy information, while neither SFT and LPWD based methods use redundancy information. Thus, N and 2N samples are given for the Cramer-Rao lower bound (CRLBs). Fig. 7 and 8 show MSEs for CR and QCR, respectively. It can be seen that both CR and QCR have MSEs close to CLRBs when the input signal-to-noise ratio is greater than a certain threshold. The signal-to-noise ratio threshold values based on the TOAF, CRQCRD, SFT and LPWD methods are respectively-7 dB, -4dB, -3dB and-2 dB. In addition, the advantage of redundant informationWith the TOAF and CRQCRD methods, both CRs and QCRs based MSEs are lower than SFT and LPWD methods.
Simulation 3
Referring to fig. 9, fig. 9 is a graph illustrating the comparison simulation result of the computation complexity based on LPWD, SFT, CRQCRD, and TOAF according to an embodiment of the present invention. Assuming that the effective length of the signal is N, the calculated amounts of LPWD, SFT, CRQCRD, TOAF and the corresponding signal-to-noise ratio thresholds are shown in table 1, and the abscissa in fig. 12 represents the number of pulses and the ordinate represents the calculated amount.
TABLE 1 calculated quantities and SNR thresholds
Figure BDA0003024641890000161
Simulation 4
Referring to fig. 10-14, fig. 10 is a diagram illustrating a set of ideal scatterer models for wave ship modeling according to an embodiment of the present invention; in FIG. 10, two scattering points T1 and T2 in the 19 th range bin are adjacent, and their CRs and QCRs are (12, 13) and (26, 26), respectively. Simulation results of the RD method without doppler frequency compensation are shown in fig. 11, and from the view point, the two scattering points cannot be identified. Fig. 12 is a diagram showing simulation results after doppler frequency compensation, in which T1 and T2 can be clearly distinguished. ISAR imaging is carried out on the ship in the simulation experiment, and simulation parameters are as follows: the carrier frequency of the radar is 5GHz, the bandwidth of a radar transmitting signal is 100MHz, the pulse repetition frequency of the radar signal is 128Hz, the effective pulse number of the radar signal is 256, and the sampling rate of the radar signal is 100 MHz. Table 2 shows the motion parameters of the ship model.
TABLE 2 Ship model motion parameters
Figure BDA0003024641890000171
Due to doppler shift caused by CR and QCR, the conventional RD method cannot obtain well-focused ISAR images. Therefore, the doppler shift needs to be estimated to compensate for the doppler shift. Simulation results show that the TOAF is basedThe method can effectively compensate the Doppler frequency shift. Now complex white Gaussian noise is added to the signal, defining the input signal-to-noise ratio as SNRIN＝10log10(Ps/Pn) (wherein P issAnd PnRepresenting the input signal power and the noise power, respectively) and first set the input signal-to-noise ratio to-1 dB. After compensating for the doppler shift in the 19 th range cell based on the LWPD, SFT, CRQCRD and TOAF methods, respectively, it is apparent from fig. 13 that these four methods are effective. In the lower signal-to-noise ratio environment shown in FIG. 14, i.e., SNRINAt-6 dB, it can be seen that only TOAF based methods can successfully identify T1 and T2, whereas LPWD, SFT and CRQCRD based methods are not effective. Fig. 15-18 are graphs of ISAR imaging results based on LPWD, SFT, CRQCRD, TOAF, respectively, according to an embodiment of the present invention, where the abscissa represents the distance unit and the ordinate represents the doppler unit. In order to visually observe the image quality, the SNR by the four methods is also shown in fig. 15 to 18INNormalized ISAR imaging results obtained at-6 dB. The energy threshold is set at 6% of the original signal energy, which ensures that weak scattering sites are not lost. The entropy was chosen as a criterion and the results are shown in table 4. Images that are focused better will yield less entropy. Due to the poor noise immunity of LPWD and SFT based methods, the ship imaging may be completely submerged in the spurious scattering points, as shown in fig. 15 and 16.
Compared with the two methods, the CRQCRD-based method has better estimation performance. Therefore, the entropy of fig. 17 is small. For the present invention, almost all CPSs can be estimated correctly, and the image quality of FIG. 18 is significantly better than that of FIGS. 15-17, and conforms to the entropy values listed in Table 3.
TABLE 3 entropy values of ISAR imaging simulation experiments
Figure BDA0003024641890000181
It is noted that, in the present invention, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that an article or device that comprises a list of elements does not include only those elements but may include other elements not expressly listed. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of additional like elements in the article or device comprising the element.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Claims (7)

1. A TOAF-based ISAR imaging method for a complex moving object is characterized by comprising the following steps:
acquiring radar echo signals of scattering points of a target to be detected in each distance unit;
carrying out distance walking compensation on the radar echo signal;
modeling the radar echo signal after the distance walking compensation into a cubic phase signal;
performing parameter estimation on the cubic phase signal by adopting a third-order autocorrelation function and a nonlinear frequency modulation technology to obtain an estimated parameter;
according to the pre-estimated parameters, performing Doppler frequency shift compensation on the cubic phase signal;
and reconstructing to obtain an ISAR image of the target to be detected by using the cubic phase signal after Doppler frequency shift compensation.
2. The method of claim 1, wherein the cubic phase signal is:
Figure FDA0003024641880000011
wherein L represents the L-th range bin, L represents the total range bin number, tnDenotes the slow time, P denotes the number of scattering points in the l-th range bin, ApRepresenting the amplitude, alpha, of the radar echo signal at the p-th scattering pointpRepresenting the center frequency, beta, of the radar echo signal of the p-th scattering pointpFrequency modulation, gamma, of radar echo signal representing the p-th scattering pointpRepresenting the quadratic frequency of the radar echo signal of the p-th scattering point.
3. The method of claim 2, wherein the pre-estimated parameters comprise: a frequency modulation rate estimate, a secondary frequency modulation rate estimate, a center frequency estimate, and an amplitude estimate.
4. The method of claim 3, wherein the performing the parameter estimation on the cubic phase signal by using the third-order autocorrelation function and the dechirp technique to obtain the estimated parameter comprises:
processing the cubic phase signal by adopting a third-order autocorrelation function to obtain an estimation model of a radar echo signal of the scattering point;
the third order autocorrelation function is:
TOAF(tn,τn)＝x(tn+τn)x(tn-τn)x*(2tn)
wherein, taunRepresenting a delay variable, x*(2tn) Denotes x (2 t)n) Conjugation of (1);
the estimation model is as follows:
Figure FDA0003024641880000021
accumulating the signal energy along a delay axis by performing integral operation on the estimation model to obtain an integrated estimation model;
the integrated estimation model is as follows:
Figure FDA0003024641880000022
wherein,
Figure FDA0003024641880000023
representing a time-delay variable τnCorresponding frequency domain, δ (-) denotes the impulse function, A'pRepresenting an amplitude value;
performing time-varying phase term compensation on the integrated estimation model by using a compensation function to obtain a compensated estimation model;
the compensation function is:
Figure FDA0003024641880000024
compensated estimation model:
Figure FDA0003024641880000025
carrying out Hough transform on the compensated estimation model, and obtaining a frequency modulation rate estimation value and a secondary frequency modulation rate estimation value of the radar echo signal of the scattering point by a peak detection technology;
Figure FDA0003024641880000031
Figure FDA0003024641880000032
wherein, betapFrequency modulation estimate, gamma, of radar echo signal representing the p-th scattering pointpEstimated secondary frequency modulation value beta of radar echo signal representing p scattering pointsIndicating the search modulation frequency, gammasIndicating the search quadratic frequency, R (beta)s,γs) Representing the cubic phase signal x for the l-th range unitl(tn) Result after Hough transform, A'p' represents the amplitude of the echo signal;
processing the cubic phase signal according to the frequency modulation rate estimation value and the secondary frequency modulation rate estimation value;
and based on the processed cubic phase signal, obtaining a central frequency estimation value and an amplitude estimation value of the radar echo signal of the scattering point by utilizing a de-chirp technology.
5. The method of claim 4, wherein processing the cubic phase signal according to the chirp estimate and the quadratic chirp estimate comprises:
processing the cubic phase signal using a processing function;
wherein the processing function is:
Figure FDA0003024641880000033
6. the method of claim 5, wherein the obtaining the center frequency estimate and the amplitude estimate of the radar echo signal of the scattering point using a dechirp technique based on the processed cubic phase signal comprises:
determining a calculation formula of a central frequency estimation value and an amplitude estimation value by utilizing a de-chirp technology based on the processed cubic phase signal;
calculating to obtain a central frequency estimated value and an amplitude estimated value of the radar echo signal of the scattering point by using a calculation formula for determining the central frequency estimated value and the amplitude estimated value;
the calculation formula of the central frequency estimated value and the amplitude estimated value is as follows:
Figure FDA0003024641880000041
wherein,
Figure FDA0003024641880000042
an amplitude estimate of the radar return signal representing the p-th scattering point,
Figure FDA0003024641880000043
representing the estimated value of the center frequency of the radar echo signal of the p-th scattering point, D' representing the peak amplitude of the signal obtained after fast Fourier transform,
Figure FDA0003024641880000044
indicating a slow time tnThe corresponding frequency domain.
7. The method of claim 6, wherein the performing Doppler shift compensation on the cubic phase signal according to the estimated parameters comprises:
compensating the cubic phase signals of scattering points in each distance unit by using a Doppler frequency shift compensation formula according to the estimated parameters to obtain the cubic phase signals after Doppler frequency shift compensation;
the Doppler frequency shift compensation formula is as follows:
Figure FDA0003024641880000045
CN202110412307.2A 2021-04-16 2021-04-16 TOAF-based ISAR imaging method for complex target Pending CN113204021A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110412307.2A CN113204021A (en) 2021-04-16 2021-04-16 TOAF-based ISAR imaging method for complex target

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110412307.2A CN113204021A (en) 2021-04-16 2021-04-16 TOAF-based ISAR imaging method for complex target

Publications (1)

Publication Number Publication Date
CN113204021A true CN113204021A (en) 2021-08-03

Family

ID=77027442

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110412307.2A Pending CN113204021A (en) 2021-04-16 2021-04-16 TOAF-based ISAR imaging method for complex target

Country Status (1)

Country Link
CN (1) CN113204021A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114114264A (en) * 2021-11-26 2022-03-01 北京理工大学重庆创新中心 Space maneuvering target ISAR imaging method, device, equipment and storage medium

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110988874A (en) * 2019-11-08 2020-04-10 西北大学 ISAR imaging method for complex moving target

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110988874A (en) * 2019-11-08 2020-04-10 西北大学 ISAR imaging method for complex moving target

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
YANYAN LI等: "ISAR Imaging of Nonuniformly Rotating Targets With Low SNR Based on Third Order Autocorrelation Function" *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114114264A (en) * 2021-11-26 2022-03-01 北京理工大学重庆创新中心 Space maneuvering target ISAR imaging method, device, equipment and storage medium
CN114114264B (en) * 2021-11-26 2024-05-21 北京理工大学重庆创新中心 Space maneuvering target ISAR imaging method, device, equipment and storage medium

Similar Documents

Publication Publication Date Title
CN108107430B (en) Ship target ISAR imaging method based on fractional Fourier transform
CN110988874B (en) ISAR imaging method for complex moving target
CN107132534B (en) Optimization method for high-speed radar target frequency domain detection
Lv et al. ISAR imaging of maneuvering targets based on the range centroid Doppler technique
Barbarossa et al. Detection and imaging of moving objects with synthetic aperture radar. Part 2: Joint time-frequency analysis by Wigner-Ville distribution
CN110146886A (en) The method for quick estimating of non-homogeneous rotating target motion parameter
CN104007435B (en) A kind of accurate speed-measuring method based on intermediate frequency adjacent echoes phase contrast
CN110850384B (en) Method for generating broadband deskew echo based on sweep frequency data
CN106772302A (en) A kind of knowledge assistance STAP detection methods under complex Gaussian background
CN112014833B (en) High-speed target time-frequency domain detection method
Gao et al. Weak target detection in the presence of sea clutter using radon-fractional fourier transform canceller
CN116299490B (en) Underwater sound multi-target detection method based on self-adaptive Doppler filtering-Radon Fourier transformation
CN116643250B (en) Broadband radar high-speed target coherent accumulation method based on sparse linear frequency modulation signal
CN116449326A (en) Broadband multi-target translational parameter estimation and compensation method
CN113204021A (en) TOAF-based ISAR imaging method for complex target
Holder et al. Modeling and simulation of radar sensor artifacts for virtual testing of autonomous driving
US10386471B1 (en) Velocity estimation with linear frequency modulated (LFM) waveforms
Altesmber et al. Doppler-tolerant classification of distributed targets--a bionic sonar
CN114200446A (en) Maneuvering target ISAR imaging method based on CINTAF
CN106569190A (en) Device and method for detecting sea-surface target under high sea conditions
Rao et al. Passive target tracking in underwater environment using bearing and frequency measurements
CN110907930B (en) Vehicle-mounted radar target detection and estimation method and device based on angle estimation
CN109655804B (en) Near target relative distance estimation method based on singular value decomposition
Li et al. Pulse jamming suppression for airborne radar based on joint time-frequency analysis
Oyedokun Sea clutter simulation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20210803

RJ01 Rejection of invention patent application after publication