For maneuvering targets, their motion during long observing time will deteriorate the integration results and degrade the performance of range and motion parameters estimation. To solve this problem, a computationally efficient method based on the time-reversal (TR) process and maximum likelihood (ML) principle, i.e., TR-MLE is proposed. The proposed method decouples the joint parameter estimation problem into two simpler problems, which not only increases the efficiency but also improves the estimation performance in low signal-noise-ratio. Furthermore, a fast method using Chirp-Z transform and Newton's method is developed for a more efficient implementation. The theoretical analysis of the noise properties after the TR process is carried out. Then, the corresponding Cramér–Rao lower bound that can evaluate the performance loss introduced by the TR process is discussed in detail. Simulated data and real data are used to assess the performance of the proposed method.

中文翻译：

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使用时间反转过程快速估计机动目标的距离和运动参数

对于机动目标，它们在长时间观察期间的运动会恶化积分结果，降低距离和运动参数估计的性能。为了解决这个问题，提出了一种基于时间反转（TR）过程和最大似然（ML）原理的计算效率高的方法，即TR-MLE。该方法将联合参数估计问题解耦为两个更简单的问题，不仅提高了效率，而且提高了低信噪比下的估计性能。此外，开发了一种使用 Chirp-Z 变换和牛顿方法的快速方法，以实现更有效的实现。对TR过程后的噪声特性进行了理论分析。然后，详细讨论了可以评估由 TR 过程引入的性能损失的相应 Cramér-Rao 下界。模拟数据和真实数据用于评估所提出方法的性能。