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 signalnoise-ratio (SNR). Furthermore, a fast method using Chirp- Z transform (CZT) 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 (CRLB) which 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.

In this paper, a novel time series heteroskedastic model is proposed for sea clutter modeling application. In the light of characteristics of the practical clutter at low grazing angle, the original generalized autoregressive conditional heteroskedasticity (GARCH) process, which has been widely used in various fields of economics, is extended from three aspects. First, the autoregressive moving-average (ARMA) terms are introduced for modeling the temporal correlation of both clutter returns and innovations. Second, the exponential of the conditional variance is generalized from one to arbitrary positive value, to capture the nonlinearity existing in the practical clutter. Third, the traditional Gaussian innovation is replaced by the Johnson Su random variable, which is a monotonic transformation of the Gaussian random variable and is capable of modeling the skewness and kurtosis. By systematically analyzing a large number of practical sea clutter data sets, we show that the proposed time series model fits the data better than some commonly used statistic-based distributions, such as the Weibull and compound Gaussian (CG) distributions.