CN1327242C - Method for compensating relative motion of mobile multiple objective for reverse synthetic aperture radar - Google Patents

Abstract

The present invention provides a compensation method for ISAR power-driven multi-target relative movements. Movement compensation is carried out for sub echo by extracting the sub echo of a target of a relative movement from echo data after power-driven multi-target primary compensation. Thus, the influence of Doppler diffusion caused by the relative movement on target imaging is overcome. Then, secondary movement compensation is carried out for the total echo so as to eliminate compensating errors caused by the primary compensation. Thus, a two-dimensional distribution diagram of multi-target clear space of a high-speed movement can be obtained. Complex targets of high-speed movements, a plurality of targets, relative movements, etc. can be imaged by adopting the method of the present invention. The compensation method for ISAR power-driven multi-target relative movements provides a tried and true method for high-speed movement and multi-target two-dimensional imaging.

Description

Inverse synthetic aperture radar maneuvering multi-target relative motion compensation method

Technical Field

The invention belongs to the technical field of synthetic aperture radar imaging, and particularly relates to a motion compensation technology of a maneuvering target with relative motion.

Background

The Inverse Synthetic Aperture Radar (ISAR) can perform all-weather, all-time and long-distance imaging on moving targets such as missiles, satellites, ships, celestial bodies and the like from a fixed or moving platform, and has important application values in strategic defense, anti-satellite, tactical weapons and radar astronomy. Inverse Synthetic Aperture Radar (ISAR) is based on range-Doppler principle imaging, the key of imaging is motion compensation, a certain point on a target is changed into an immobile point through the motion compensation, the motion of the target is equivalent to the rotation around the immobile point, ISAR imaging is equivalent to turntable target imaging, and the later is easy to realize.

There are many methods for motion compensation, and in the data after distance compression, distance alignment can be performed by envelope cross-correlation or tracking the historical time (like peak or centroid) of the reference point and fitting to a polynomial. However, the requirements for lateral phase tracking are much more stringent than for range calibration, and range errors must be controlled to within fractions of the radar wavelength. Most existing lateral phase estimation algorithms obtain the lateral phase error by tracking the phase history of a strong scatterer of a single well-isolated target, such as the strong scatterer algorithm (DSA), the multi-scatterer algorithm (MSA) (statistical scattering centroid (SSC) or doppler centroid). DSA performs well when there is an isolated strong scatterer on the target, however, the effect is not ideal in many cases due to the effect of target flicker and shadowing. Centroid-based algorithms are in a sense more robust than DSA, but require phase averaging, which is more detrimental if the phase unwrapping is incorrect. The document proposes an auto-focus algorithm, which is different from other multi-scatterer algorithms, which is a parametric algorithm based on an extremely flexible data model, automatically selects (without isolation or very strong) multiple scatterers in a two-dimensional (2-D) image domain, and integrates their phase and RCS information using an optimal method, thereby avoiding the cumbersome phase unwrapping step.

The radar echo signal after range compression can be expressed as

$r(m,n)=\left\{\underset{k=1}{\overset{K}{\mathrm{\Σ}}}{\mathrm{\α}}_k{e}^{j4\mathrm{\π}{f}_m[x_k\cos \left(\mathrm{nt}\right)+y_k\sin \left(\mathrm{nt}\right)]/c}\right\}\×e^{j[4\mathrm{\π}{f}_m\mathrm{\Δ}{R}_0\left(\mathrm{nt}\right)}+e(m,n)$

$\begin{array}{cc}0\≤m\≤M-1,& 0\≤n\≤N-1\end{array}$

In the form of (a); where K denotes the number of scattering points, α_k、x_k、y_kRespectively representing the complex amplitude, the abscissa and the ordinate of the Kth scattering point; r₀(nt) represents the distance movement and,the method is the difference between the position of a target tracked by the nth pulse and radar measurement, e (M, N) represents clutter and noise, c is the speed of light, M is the number of sampling points, and N is the number of pulses in a distance window; f. of_mIs a discrete frequency, which can be sampled for a time t_mIs represented as follows:

$f_m=f_0+\frac{f_R}{\mathrm{\π}}{t}_m$

wherein f is₀Representing the carrier frequency, f_RThe frequency is linearly tuned. r (m, n) can be simplified to the following form

Here frequency pair

Coordinate (x) with the k-th scattering point_k，y_k) In response to this, the mobile terminal is allowed to,

corresponding to the amount of range migration DeltaR₀(nt)，{ψ(n)}_n=0 ^N-1Representing an arbitrary lateral phase error. For details, see the literature: JIANLI, RENBIAOWU, VICTOR c].IEEE Transactions on Aerospace and electronic systems.2001，37(3)：1056-1069.

All the existing imaging algorithms are directed at targets such as airplanes and ships, and although scattering points on the targets are many, relative motion and distance change do not exist among the scattering points, and the targets can be regarded as a single rigid body with a plurality of scattering points. The above algorithm works well for such moving objects.

However, the algorithms do not solve the problem of relative motion among multiple targets such as multiple warheads, airplane formation and the like; especially, the method is not suitable for imaging targets with high-speed motion, spin and large relative motion between the targets, such as missiles, and cannot compensate the distance and the phase caused by the relative motion between the targets. For multi-target echoes, when motion compensation is performed by taking any one of the targets as a reference point, even if the compensation precision is high, due to the relative motion between the targets, the doppler frequency generated by the relative motion can cause doppler frequency shift in the transverse direction, thereby blurring two-dimensional imaging or generating false spatial distribution of the targets (see (b) and (c) of fig. 5; and (a) of fig. 6, 7 and 8). Therefore, the relative motion between the targets must be compensated to eliminate the influence of the relative motion on the imaging result.

Disclosure of Invention

The invention aims to provide an ISAR (inverse synthetic aperture radar) maneuvering multi-target relative motion compensation method, which is used for compensating relative motion among multiple targets, and can eliminate the influence of the relative motion on an imaging result, so that the imaging result can meet the requirements of high-speed motion and multi-target ISAR imaging, and a real and clear two-dimensional distribution map of a space target is obtained.

The invention aims to compensate the relative motion of the target echo and eliminate the influence of the relative motion on the imaging result. The specific implementation process is as follows:

for convenience of describing the contents of the present invention, first, a term explanation is made:

1. method for estimating parameters by using optimized nonlinear least square method

The specific process is as follows:

1) carrying out primary compensation on the echo after distance compression by using an envelope cross-correlation method to obtain an initial value

2) Extracting characteristics;

in 1) give

Is estimated value of

Then the motion parameters can be obtained by the nonlinear least square method

Is estimated from the feasible value ofThe calculation formula is

Wherein

3) Estimating the motion; estimated by 2)

Can be initialized by using a nonlinear least square methodIs estimated value of

Is calculated by

Wherein

4) Repeating the above steps until the cost function between two adjacent iterations

Is below a certain predetermined threshold, the parameter value of the last iteration is the final estimate of the parameter.

5) Using the estimated parameter value to perform motion compensation on r (m, n), and the calculation formula is

The obtained result is the compensated echo data.

Estimating parameters of the echo signals r (m, n) after the distance compression by using an optimized nonlinear least square method;

the algorithm flow chart is shown in the attached figure 2, and the detailed content is shown in the literature: JIAN LI, RENBIAO WU, VICTOR c. chen. robust Autofocus Imaging for Imaging Targets [ J ]. ieee transactions on Aerospace and electronic systems.2001, 37 (3): 1056-1069.

2. With respect to time-frequency analysis

The present invention utilizes the STFT (short time Fourier) transform in time-frequency analysis, whose expression is:

STFT(t，ω)＝∫s(τ)g(τ-t)exp{-jωτ}dτ

where s (τ) is the signal and g (τ -t) is the time window. By selecting a suitable window, the characteristics in the frequency domain of the signal can be observed in a very narrow time. In consideration of the characteristics of various windows, a Hamming window type is selected in the implementation process. Since this window type has the advantages of better frequency resolution, lower side lobes and larger attenuation speed.

The flow chart of the algorithm for performing the transverse processing by the existing time-frequency analysis method is shown in the attached figure 9, and the detailed content is shown in the literature: l, coyohn, white constitution, time-frequency analysis: theory and application, the journal of the university of west ampere publishing.

A method for compensating the relative motion of multiple maneuvering targets of an inverse synthetic aperture radar comprises the following steps: step 1, distance compression

Let the radar transmit monopulse signal as p (t), and the radar collect data as baseband echo signal s_b(t)，T_pTime, omega, for triggering the acquisition of the gate signal_cIs a radar carrier;

firstly, frequency domain matched filtering is adopted for digital signal processing, and the specific steps are as follows:

1) echo signal s collected by ISAR radar_b(t) obtaining the frequency spectrum S of the echo signal by performing digital Fourier transform_b(ω)；

2) Using the formula S_Ob(t)＝p(t-T_p)exp(-jω_ct), calculating a reference baseband echo signal S_Ob(t)；

3) To S_Ob(t) digital Fourier transform to obtain the spectrum S of the baseband reference signal_Ob(ω)；

4) Calculating S_b(ω)S_Ob ^*(ω) Inverse Fourier transform to obtain a baseband matching filtering result S_Mb(t), the obtained result is the echo data after the distance compression;

step 2, performing primary distance and phase compensation on the echo

Estimating parameters by using an optimized nonlinear least square method according to the echo signals r (m, n) subjected to distance compression in the step 1;

it is characterized by also comprising the following steps:

step 3, extracting sub-echoes of the relatively moving target

1) Performing amplitude normalization on the echo data subjected to the primary compensation in the step 2, and averaging all the imaged normalized echo range images to obtain a relatively stable average range image;

2) setting an amplitude threshold, and finding out the position of a stronger scattering point from the obtained average distance image through the amplitude threshold;

3) extracting corresponding sub-echo information of the relatively moving target from the imaged total echo by using the obtained scattering point position information;

step 4, performing distance and phase compensation on the extracted sub-echoes

Performing distance and phase compensation on the sub-echo extracted in the step 3 by using the method in the step 2, and updating the compensated sub-echo data into the overall echo data after primary compensation; obtaining a total echo after relative motion compensation;

step 5, performing secondary phase compensation on the total echo

Performing motion compensation on the echo data subjected to the relative motion compensation in the step 4 again by using the method in the step 2; obtaining echo data after motion compensation required by imaging;

step 6, utilizing a time-frequency analysis method to carry out two-dimensional imaging on the total echo

And (5) carrying out transverse processing on the compensated total echo obtained in the step (5) by using a time-frequency analysis method to obtain a two-dimensional distribution map of the target.

After the steps, the relative motion compensation of ISAR maneuvering multiple targets can be realized, and a two-dimensional distribution map of the targets is obtained.

The flow chart of the ISAR maneuvering multi-target relative motion compensation method is shown in FIG. 4. Fig. 5 to 8 are examples of relative motion compensated imaging.

It should be noted that:

the purpose of the step 2 is mainly to perform motion compensation by using a scattering point in the target as a reference point, after the motion compensation, the target echo is aligned by using the scattering point as the reference point, but because other targets have relative motion with respect to the reference target, other targets of adjacent echoes may still have translational components and may exceed the distance resolution unit, and the influence of the imaging quality on the imaging by the relative motion target is not eliminated. Therefore, the relative moving object must be motion compensated; the flow chart is shown in fig. 2.

The purpose of the step 3 is to obtain the echo position of the relative moving target from the range image after primary compensation, and extract the sub-echo of the relative moving target from the total echo; and comparing the echo range images in the same imaging time, respectively calculating the positions of the maximum value points in the echo range images, and finding out the maximum value points with obvious position changes, wherein the target corresponding to the maximum value points is the target with relative motion. If the target is far away from other scattering points, then the independent echoes of the scattering points can be extracted from it, as shown in fig. 2 (b).

The purpose of step 4 is mainly to compensate the echo of the relatively moving target and eliminate the influence of Doppler frequency generated by relative movement on imaging;

due to the existence of the relative motion, other scattering points generate larger errors in the compensation process in the step 2, and the main purpose of the step 5 is to reduce the errors so as to meet the imaging requirement;

the processing in steps 4 and 5 can eliminate the influence of the doppler frequency generated by the relative motion of the target on the echo phase when the motion compensation is performed in step 2. So that the overall echo is fully motion compensated.

The innovation of the invention is that: extracting sub-echoes of the relatively moving target from the echo data after the primary compensation of the dynamic multi-target, and performing motion compensation on the sub-echoes, thereby overcoming the influence of Doppler diffusion caused by relative motion on target imaging; performing secondary motion compensation on the total echo to eliminate a compensation error caused by primary compensation; thereby obtaining a clear space two-dimensional distribution diagram of multiple targets moving at high speed; an effective method is provided for the two-dimensional imaging of multiple targets moving at high speed.

Working principle analysis of the invention

Referring to fig. 1, let the radar be located at the origin L, A, B be two scatterers in the imaging space, and the distance between the initial time a and the radar be R₀The distance between A and B is r₀The moving speed of the object A is

And

is beta, and the relative speed of A and B is v₀And is and

and speed

Is the same, the distance from the initial time B to the radar is R_b＝R₀+r₀cos beta. At time t the target A, B moves to a ', B', respectively. The distance from the target A to the radar is

$R_0\left(t\right)=\sqrt{{({\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+\mathrm{vt}\cos \mathrm{\&beta;})}^2+{\left(\mathrm{vt}\sin \mathrm{\&beta;}\right)}^2}$

When R is₀At the value of > vt (in mm), $R_0\left(t\right)\&cong;{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+\mathrm{vt}\cos \mathrm{\&beta;}+\frac{{\left(\mathrm{vt}\sin \mathrm{\&beta;}\right)}^2}{2({\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+\mathrm{vt}\cos \mathrm{\&beta;})}---\left(1\right)$

and the distance from the target B to the radar L at the time t is

$R_b\left(t\right)\&cong;{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+\left[r_0(v+v_0)t\right]\cos \mathrm{\&beta;}+\frac{{\{{[\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+(v+v_0)t]\sin \mathrm{\&beta;}\}}^2}{2\{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+[{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+(v+v_0)t\left]\cos \mathrm{\&beta;}\right\}}---\left(2\right)$

Motion compensation with A as reference, i.e. equivalent to translating A to R₀Is on the circle of radius, i.e. translating point A 'to A', then translating point B 'to B', when there is no relative motion between the targets, the target B moves to point C at time t, and the distance from C to the radar after compensation is

$R_c^{\text{'}}\left(t\right)=\sqrt{{({\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0\cos (\mathrm{\&beta;}-\mathrm{\&theta;}))}^2+{\left({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0\sin (\mathrm{\&beta;}-\mathrm{\&theta;})\right)}^2}$

Wherein θ is the angle of the radar line of sight and $\mathrm{\&theta;}\&ap;\frac{\mathrm{vt}\sin \mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}$ then, then

$R_c^{\&prime;}\left(t\right)\&ap;{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0\cos \mathrm{\&beta;}+\frac{{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0\mathrm{vt}{\sin }^2\mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}+\frac{{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0{\sin }^2\mathrm{\&beta;}}{2{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}$

In this case, the difference in distance of C from the radar is

$\mathrm{\&Delta;}{R}_c\left(t\right)=R_c^{\&prime;}\left(t\right)-R_b==\frac{{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0\mathrm{vt}{\sin }^2\mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}+\frac{{\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0{\sin }^2\mathrm{\&beta;}}{2{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}$

So that the Doppler frequency generated by the motion of point B in the absence of relative motion is

$f_d^{\&prime;}=\frac{2\mathrm{<figure-callout\; id="0"\; label="f\; c\; r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">f</figure-callout>}}{c}\frac{r_{}}{}\frac{{}_0{\sin }^2\mathrm{\&beta;}}{R_0}---\left(3\right)$

And B' is a distance from the radar

$R_n^{\&prime;}\left(t\right)=\sqrt{{[{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+v_{0}t)\cos (\mathrm{\&beta;}-\mathrm{\&theta;})]}^2+{\left[({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+v_{0}t)\sin (\mathrm{\&beta;}-\mathrm{\&theta;})\right]}^2}$

$\&ap;{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0+({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+v_{0}t)\cos \mathrm{\&beta;}+({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+v_{0}t)\frac{\mathrm{vt}{\sin }^2\mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}+\frac{{\left(({\mathrm{<figure-callout\; id="0"\; label="r"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">r</figure-callout>}}_0+v_{0}t)\sin \mathrm{\&beta;}\right)}^2}{2{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}$

The change in distance to the radar due to the relative velocity is

$\mathrm{\&Delta;R}\left(t\right)=R_b^{\&prime;}\left(t\right)-R_c^{\&prime;}=v_{0}t\cos \mathrm{\&beta;}+\frac{{\mathrm{<figure-callout\; id="0"\; label="v"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">v</figure-callout>}}_0{\mathrm{vt}}^2{\sin }^2\mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}+\frac{[{\left(v_{0}t\right)}^2+{2b}_0{v}_{0}t]{\sin }^2\mathrm{\&beta;}}{2{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}$

So that the Doppler frequency due to the relative motion is

$f_d=\frac{2f}{c}\frac{\mathrm{d\&Delta;R}}{\mathrm{dt}}=\frac{2f}{c}({\mathrm{<figure-callout\; id="0"\; label="v"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">v</figure-callout>}}_0\cos \mathrm{\&beta;}+\frac{2{\mathrm{<figure-callout\; id="0"\; label="v"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">v</figure-callout>}}_0\mathrm{vt}{\sin }^2\mathrm{\&beta;}}{{\mathrm{<figure-callout\; id="0"\; label="R"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">R</figure-callout>}}_0}+\frac{[{2\mathrm{<figure-callout\; id="0"\; label="v"\; filenames="C20041004030400161.png,C20041004030400191.png"\; state="\{\{state\}\}">v</figure-callout>}}_0^{2}t+{2r}_0{v}_0]{\sin }^2\mathrm{\&beta;}}{2R_0})---\left(4\right)$

Wherein f'_dWhen the target does not have relative motion, the target is equivalent to Doppler frequency generated by rotation of the turntable, and is required by radar imaging; and f_dIs the Doppler frequency generated by the movement of the target relative to a reference point, when f_dAbove the lateral doppler resolution, doppler blur will be generated in the lateral direction. The relative motion must be compensated for. For a moving object with acceleration, the moving object can be regarded as uniform acceleration linear motion in the same imaging time. In this case, the influence of acceleration may be added to the above formulas.

The method can compensate the distance translation caused by the relative motion speed, and eliminate the influence of Doppler frequency shift generated by relative motion on imaging.

In summary, the ISAR maneuvering multi-target relative motion compensation method provided by the invention eliminates the transverse Doppler shift generated by the relative motion, thereby eliminating the transverse distance blur in the two-dimensional imaging and the false target generated due to the influence of the relative motion, and obtaining the correct two-dimensional image of the ISAR maneuvering multi-target. The method can image complex targets with high-speed motion, multiple targets, relative motion and the like, and provides an effective method for two-dimensional imaging of the multiple targets with high-speed motion.

Drawings

FIG. 1 is a schematic diagram of ISAR (inverse synthetic aperture radar) maneuvering multi-target relative motion compensation

Wherein L is the position of the radar, A, B is the positions of two targets at the initial time, and the distance between the initial time A and the radar is R₀The distance between A and B is r₀The moving speed of the object A is

Andis beta, and the relative speed of A and B is v₀(ii) a The target A, B moves to a ', B' at time t, respectively; motion compensation with A as reference, i.e. equivalent to translating A to R₀A circle of radius, i.e., translating point A 'to A ", then B' translates to B"; because B has relative motion with the target A, the distance from B 'to A' is not equal to the distance from B to A, namely, after the motion compensation is carried out by taking A as a reference point, the target B still has distance translation, and if the relative motion is not compensated, the imaging result must have Doppler fuzzy and diffusion. The distance translation resulting from the relative motion must be compensated for.

FIG. 2 is a flowchart of a conventional algorithm for initial compensation of distance and phase of echo

Wherein,

the number of scattering points is represented by,is composed of

The intermediate variable of (a) is,is composed ofAn estimate of (d).

FIG. 3 is a schematic diagram of the step 3 of extracting the sub-echoes of the relatively moving object from the total echo according to the present invention

Wherein, fig. 3(a) is a schematic diagram of being unable to extract sub-echo information;

in fig. 3(a), the echo of the relatively moving object in the box overlaps with the echo of the adjacent object, and at this time, the sub-echo of the relatively moving object cannot be extracted, and therefore, the compensation for the relative motion cannot be performed;

FIG. 3(b) is a schematic diagram of extracting sub-echo information

In fig. 3(b), if the distance from other scattering points of the moving object is far, the independent echo of the scattering point can be extracted; the influence of relative motion can be eliminated by performing motion compensation on the extracted data; m, n, m + p represent the sample point of the second order of the echo.

FIG. 4 is a flow chart of the system of the present invention

Wherein the auto-clean algorithm is the algorithm of step 2.

FIG. 5 is a graph of the imaging simulation results of the simulation data of the present invention

Wherein the image (a) is a two-dimensional image of the three objects without relative motion therebetween

The graph (b) is a graph obtained by adding a relative movement velocity v to each of the intermediate targets in the graph (a)₀Two-dimensional image after 10m/s without relative motion compensation

The graph (c) is a graph obtained by adding the relative movement velocity v to the intermediate target in the graph (a)₀Two-dimensional image after 20m/s without relative motion compensation

The relative moving targets in the images (b) and (c) deviate from the real positions due to the Doppler frequency shift caused by the relative movement, and certain Doppler blurring is generated in the transverse direction;

FIG. d is a two-dimensional image compensated for (b) and (c) by the compensation method of the present invention

The image (d) agrees with the image formation result of the image (a). The result shows that the method of the invention compensates the influence of the relative motion on the two-dimensional imaging, and is effective for compensating the relative motion;

FIGS. 6, 7 and 8 are graphs of imaging results of measured data of a certain radar

Of these figures, (a) shows the imaging result before the relative motion compensation, and (b) shows the imaging result after the relative motion compensation. It can be seen from these figures that the relative motion makes the two-dimensional image of the target generate severe blur in the transverse direction, and the number of targets and the two-dimensional true distribution of the targets cannot be distinguished from the image. Through the relative motion compensation, the influence of Doppler frequency shift caused by relative motion on a target two-dimensional image is eliminated, so that a clear and real two-dimensional distribution map of the target is obtained, as shown in (b) in fig. 6, 7 and 8;

FIG. 9 is a flowchart of a procedure performed by the time-frequency analysis method (STFT)

Wherein g (τ) is a time window, s (τ) is echo data after motion compensation, FFT is fast Fourier transform, and f (t, τ) is the product of g (τ) and s (τ);

the specific implementation mode is as follows:

relative motion compensation is carried out on a plurality of maneuvering multiple targets acquired by an L-113 radar by adopting the method, and results shown in FIGS. 6-8 can be obtained by adopting a VC programming method;

as can be seen from fig. 5, 6, 7 and 8, the algorithm of the present invention can compensate the distance translation caused by the relative motion between the targets well, eliminate the influence of the relative motion on the imaging quality, and obtain real and clear target space two-dimensional distribution, so the algorithm of the present invention is very effective for multi-target imaging with complex motion form and high-speed motion.

Claims (1)

1. A method for compensating the relative motion of multiple maneuvering targets of an inverse synthetic aperture radar comprises the following steps:

step 1, distance compression

Let the radar transmit monopulse signal as p (t), and the radar collect data as baseband echo signal s_b(t)，T_pTime, omega, for triggering the acquisition of the gate signal_cIs a radar carrier;

firstly, frequency domain matched filtering is adopted for digital signal processing, and the specific steps are as follows:

1) echo signal collected by ISAR radars_b(t) obtaining the frequency spectrum S of the echo signal by performing digital Fourier transform_b(ω)；

2) Using the formula S_Ob(t)＝p(t-T_p)exp(-jω_ct), calculating a reference baseband echo signal S_Ob(t)；

3) To S_Ob(t) digital Fourier transform to obtain the spectrum S of the baseband reference signal_Ob(ω)；

4) Calculating S_b(ω)S_Ob ^*(omega) inverse Fourier transform to obtain base band matched filtering result S_Mb(t), the obtained result is the echo data after the distance compression;

step 2, performing primary distance and phase compensation on the echo

The echo signal can be represented as a result of the distance compression of the first step

$r(m,n)=\left\{\underset{k=1}{\overset{K}{\mathrm{\&Sigma;}}}{\mathrm{\&alpha;}}_k{e}^{j4\mathrm{\&pi;}{f}_m\&lsqb;x_k\cos \left(\mathrm{nt}\right)+y_k\sin \left(\mathrm{nt}\right)]/c}\right\}\&times;e^{j\&lsqb;4\mathrm{\&pi;}{f}_m\mathrm{\&Delta;}{R}_0\left(\mathrm{nt}\right)}+e(m,n)$

0≤m≤M-1， 0≤n≤N-1

In the form of (a); where K denotes the number of scattering points, α_k、x_k、y_kRespectively representing the complex amplitude, the abscissa and the ordinate of the Kth scattering point; r₀(nt) represents the distance movement, which is the difference between the position of the target tracked by the nth pulse and the radar measurement, e (M, N) represents clutter and noise, c is the speed of light, M is the number of sampling points, and N is the number of pulses in the distance window; f. of_mIs a discrete frequency, which can be sampled for a time t_mIs represented as follows:

$f_m=f_0+\frac{f_R}{\mathrm{\&pi;}}{t}_m$

wherein f is₀Representing the carrier frequency, f_RThe frequency is linearly tuned.

r (m, n) can be simplified to the following form

Here frequency pair () Coordinate (x) with the k-th scattering point_k，y_k) In response to this, the mobile terminal is allowed to,corresponding to the amount of range migration DeltaR₀(nt)，{ψ(n)}_n＝0 ^n-1Representing an arbitrary lateral phase error.

Estimating parameters of the echo signals r (m, n) after the distance compression by using an optimized nonlinear least square method;

characterized in that it further comprises the following steps

Step 3, extracting sub-echoes of the relatively moving target

1) Performing amplitude normalization on the echo data subjected to the primary compensation in the step 2, and averaging all the imaged normalized echo range images to obtain a relatively stable average range image;

2) setting an amplitude threshold, and finding out the position of a stronger scattering point from the obtained average distance image through the amplitude threshold:

3) extracting corresponding sub-echo information of the relatively moving target from the imaged total echo by using the obtained scattering point position information;

step 4, performing distance and phase compensation on the extracted sub-echoes

Performing distance and phase compensation on the sub-echo extracted in the step 3 by using the method in the step 2, and updating the compensated sub-echo data into the overall echo data after primary compensation; obtaining a total echo after relative motion compensation;

step 5, performing secondary phase compensation on the total echo

Performing motion compensation on the echo data subjected to the relative motion compensation in the step 4 again by using the method in the step 2; obtaining echo data after motion compensation required by imaging;

step 6, utilizing a time-frequency analysis method to carry out two-dimensional imaging on the total echo

And (5) carrying out transverse processing on the compensated total echo obtained in the step (5) by using a time-frequency analysis method to obtain a two-dimensional distribution map of the target.

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