1. Introduction
An inverse synthetic aperture radar (ISAR) can achieve long-range and high-resolution imaging of non-cooperative targets in all weather and at all times, so it has an important application value in both military and civilian fields [
1,
2,
3]. ISAR achieves a high radiation resolution by transmitting large bandwidth signals, and achieves a high azimuth resolution by using the Doppler frequency generated by the equivalent rotation of the target relative radar [
4,
5]. The traditional ISAR imaging algorithm is the range Doppler (RD) algorithm. For the equivalent uniform rotation model, discrete Fourier transform (DFT) can be used to realize azimuth focusing directly. However, as the moving state of the maneuvering target relative to the radar is complex, after translational compensation, the moving state cannot be equivalent to the uniform rotation motion, which results in variation in the azimuth Doppler. If the DFT method is used again for azimuth imaging, serious azimuth defocusing occurs.
At present, the ISAR imaging methods for maneuvering targets mainly include: joint time-frequency distribution (JTFD) [
6], Wigner-Ville distribution (WVD) [
7,
8], Radon-Wigner transform (RWT) [
9,
10], chirp Fourier transform (CFT) [
11,
12], adaptive Chirplet decomposition [
13], and sub-echo clean separation [
14,
15,
16]. Among them, the JTFD method uses range-instantaneous Doppler (RID) to image, where the instantaneous Doppler is obtained by time-frequency analysis. However, the imaging quality of the JTFD method depends on the time-frequency method used, and the time-frequency resolution of different methods varies. At present, there is no method that combines both time domain and frequency domain resolution. The WVD method has a high time-frequency resolution, but there are cross terms, and the imaging quality is easily affected by the cross terms. The RWT, CFT, adaptive Chirplet decomposition, and sub-echo clean separation methods are essentially parameter estimation methods [
17,
18]. These methods model the signal echo as a multi-component model, and then estimate or separate the sub-component parameters one-by-one to achieve high-resolution imaging. The computational demand of these methods is huge, and the algorithm efficiency is low, so they do not meet the requirements for ISAR real-time imaging.
Research shows that target approximation satisfies the first-order linear motion with a short imaging time, so the target scattering point azimuth echo signal can be approximated as a multi-component chirp signal (LFM) [
1]. The ratio of the quadratic phase term (chirp rate) to the first-order phase term (central frequency) of the scattering point echo signal is approximately a fixed value and equal to the ratio of the target rotational angular acceleration to the rotational angular velocity [
19,
20]. Given this feature, an imaging algorithm based on matched Fourier transform (MFT) [
21] is proposed in this study, but this algorithm only uses a single sub-echo signal for parameter estimation and the estimation error is large. A modified Fourier transform [
22] is also proposed to modify the phase kernel function of Fourier to realize azimuth focusing. This method needs to reconstruct the phase function. The value of column vectors is strictly selected when reconstructing, and the reconstructed phase function is no longer the phase function of fast Fourier transform (FFT), which affects the speed of calculation.
Based on the above analysis, this paper proposes a modified chirp Fourier transform (MCFT) maneuvering target imaging method. Firstly, the rough estimate of the target rotation ratio is estimated by discrete chirp Fourier transform (DCFT), and then the minimum entropy function and the conjugate gradient descent method are used to obtain the exact value of the rotational parameter. Finally, azimuth focusing is achieved by only performing MCFT once on the azimuth echo signal. Thus, this method avoids estimating and separating the sub-echo components one-by-one, greatly improving the imaging speed, and there is no cross-term effect. Simulation and experimental results show that the proposed method can realize two-dimensional, high-resolution, and fast imaging of maneuvering targets.
2. ISAR Imaging Model of Maneuvering Targets
As shown in
Figure 1, we established a three-dimensional ISAR imaging geometric model [
23] of a maneuvering target with the center of the target turntable as the coordinate origin (O), where
is the position vector of any scattering point
on the target. The ISAR imaging projection plane
is determined by the three-dimensional rotation vector
and the range vector
R of the radar to the target origin.
consists of the radial component
in the
plane and the normal component
perpendicular to the
plane.
moves the scattering point, resulting in a change in the echo phase and producing a Doppler shift related to the target azimuth that can result in high-resolution azimuth imaging of the target, which is called the effective rotation component. Conversely,
does not cause radial motion of the scattering point, which is called the invalid rotation component. Similarly, the three-dimensional velocity
of the target can be decomposed into a radial component
along the line-of-sight (LOS) direction and a normal component
perpendicular to the LOS. Notably,
is not necessarily in the same direction as
.
causes the target to move in the radial direction, causing the range to move, which needs to be compensated for, whereas
has no effect on the target’s radial motion. ISAR imaging projects the three-dimensional effective component onto a two-dimensional imaging plane for turntable imaging, as shown in
Figure 1b, where
and
are the effective projection component of angular velocity and translational velocity, respectively.
As can be seen from
Figure 1a, the scattering point
P is rotated by a linear velocity of
; its corresponding radial component is
. Therefore, the instantaneous Doppler frequency of the scattering point
P is:
where
is the wavelength of the radar signal, and
and
represent the outer product and inner product of the vector, respectively. Since the ISAR imaging time is short, we assumed that the target imaging projection plane is approximately constant during the short imaging time, so the Doppler frequency of the scattering point
P can be further expanded to:
where
,
, and
are projected components of
,
, and
on the three axes
, respectively.
When a target is maneuvering,
generally changes with time. Because the imaging time is short, the motion of the target can approximately change linearly with time [
7], ignoring the influence of high-order components on the imaging, so we can obtain:
where
and
respectively represent the initial velocity and acceleration of the target,
and
respectively represent the angular velocity and angular acceleration of the decomposed
along three coordinate axes, and
is the azimuth time.
Combining Equations (2) and (3), the range from the scattering point
P to the radar is:
where
represents the range from the radar to the target origin O at the initial moment
.
,
, and
.
From Equation (4), for the maneuvering target, the instantaneous range exhibits a quadratic characteristic of the azimuth time, corresponding to the frequency modulation characteristic in the azimuth phase, which seriously affects the azimuth Doppler imaging.
In order to obtain a high-range resolution, a radar usually uses an ultra-bandwidth linear frequency modulation (LFM) signal. The expression of the transmitted signal is:
where
,
is the pulse width,
is the carrier frequency,
is the chirp-rate,
is the range fast time, and
is the azimuth slow time.
After the time delay
, the radar receives the echo signal of the point
P:
ISAR uses the “dechirp” reception mode to reduce the sampling rate of the echo signal. The reference signal is:
where
,
is the reference range. For ease of analysis, it is usually assumed that
is satisfied. Therefore, the range compression signal after the dechirp processes compensates for the residual video phase (RVP) and the envelope skew term is [
15]:
where
.
It can be seen from Equation (8) that
in the phase of the range compressed signal determines the azimuth focusing effect. The first and second terms cause envelope migration and need to be compensated for. The third term is necessary to achieve high-resolution azimuth imaging; the fourth term is due to azimuth defocusing caused by azimuth Doppler time-varying and needs to be compensated for. Therefore, envelope migration correction and azimuth focusing processing are needed to achieve high-resolution imaging. Envelope migration has been studied in many papers and achieved good results [
24,
25,
26], and we will not go into detail in this paper. This paper focuses on azimuth focusing imaging.
Assuming that the target has completed envelope migration correction, the target scattering point is corrected in the corresponding range unit, and the range unit where the
P point is located has
N scattering points. The azimuth echo signal of the range unit can be expressed as:
where
,
,
.
,
, and
represent the amplitude, central frequency, and chirp-rate of the azimuth echo of the
ith scattering point, respectively.
It can be seen from Equation (9) that the target azimuth echo signal is a multi-component LFM signal with different central frequencies and chirp rates due to the distribution of a plurality of scattering points having different lateral distances in the same range cell.
Figure 2 is a time-frequency diagram of a certain range cell.
Figure 2a depicts a pulse-range cell diagram after translation compensation of range compression;
Figure 2b demonstrates the time-frequency transformation of the azimuth signal of the range cell where the black arrow is located. From the time-frequency diagram, it can be seen that the range cell signal is approximately a multi-component LFM signal.
As shown in
Figure 1b, suppose
is the
ith scattering point on the range cell,
is the lateral distance of the imaging plane, and the effective rotational component
can be expressed as angular velocity
and angular acceleration
in the imaging plane. Then,
and
. Therefore, the ratio of chirp rate
to central frequency
can be obtained:
It can be seen from the rotation ratio
that the coefficient is only related to the rotation ratio of the target and is independent of the coordinates of the scattering point. Therefore, for the rigid body target, rotation ratios remain unchanged, and the rotation ratios
of the sub-echo LFM signals of all scattering points are also the same. In turn, Equation (9) can be resolved as: