Abstract
This paper considers the problem of estimating the motion parameters of a low-observable high-speed maneuvering target with high-order motions, where the target’s complex motions will result in the range migration (RM) and Doppler frequency migration (DFM) effects within the observation time interval, which will significantly affect the radar performance. Based on the Hough transform (HT) and the polynomial Chirplet transform (PCT), a novel method for the motion parameter estimation, which we coin as HPCT, is proposed in this paper. It first employs the trajectory feature extracted from the image of range profiles by the HT to correct the first-order RM and resolve the Doppler ambiguity. Then, it compensates the residual range curvature and the DFM with the motion parameters estimated from the time-frequency ridge, which is extracted from the highly concentrated time-frequency representation (TFR) generated by the PCT. After that, the energy of radar returns can be coherently integrated along the target’s moving trajectory. In addition, we evaluate the parameter estimation performance, integration performance and computational cost of the proposed method via theoretical analysis or numerical experiments. The results show that in terms of the anti-noise ability, cross-term interference avoidance, measurement accuracy and computational complexity, the HPCT is superior to some common methods for the motion parameter estimation. Finally, we extend it to the scenario of multiple targets with different motion orders.
Highlights
INTEGRATION FOR A MANEUVERING TARGET In this subsection, we evaluate the coherent integration performance of HPCT for a high-speed maneuvering target with the motion parameters described in TABLE 3, where the additive complex Gaussian white noise is added to the radar returns and the signal-to-noise ratio (SNR) before pulse compression is manually set to −35 dB
It can be seen that the proposed method (HPCT) can achieve close integration performance as the ground truth-values of the target’s motion parameters are directly used when the input SNR is no less than −35 dB, which may indicate that the estimated motion parameters is sufficiently accurate to integrate the energy of radar returns along the target’s moving trajectory
The results showed that the HPCT can achieve a much lower and narrower input SNR threshold region than the widely used envelope cross-correlation function (CCF) algorithm, that is, it has a much better noise resistance capability
Summary
The fourth category achieves the long-time coherent integration along the target’s moving trajectory via jointly searching in the target’s motion parameter space, e.g., the generalized Radon Fourier transform (GRFT) [13], VOLUME 9, 2021 generalized Keystone transform-generalized de-chirp process (GKT-GDP) [12] and Radon-S transform [34] This kind of method can obtain desirable estimation and integration performances. The results demonstrate that the proposed method has many advantages when compared with some common methods for motion parameter estimation, such as excellent anti-noise performance, free from cross-term interference, high measurement accuracy and low computational complexity It seems to be promising and attractive for the motion parameter estimation of the high-speed maneuvering target under low SNR environment. The results demonstrate that the proposed method can estimate the motion parameters of the maneuvering targets with different motion order accurately and integrate the signal energy of each target along its moving trajectory, which is helpful to the target detection
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