A Novel Method for Sea-Land Clutter Separation Using Regularized Randomized and Kernel Ridge Neural Networks

Abstract

Classification of clutter, especially in the context of shore based radars, plays a crucial role in several applications. However, the task of distinguishing and classifying the sea clutter from land clutter has been historically performed using clutter models and/or coastal maps. In this paper, we propose two machine learning, particularly neural network, based approaches for sea-land clutter separation, namely the regularized randomized neural network (RRNN) and the kernel ridge regression neural network (KRR). We use a number of features, such as energy variation, discrete signal amplitude change frequency, autocorrelation performance, and other statistical characteristics of the respective clutter distributions, to improve the performance of the classification. Our evaluation based on a unique mixed dataset, which is comprised of partially synthetic clutter data for land and real clutter data from sea, offers improved classification accuracy. More specifically, the RRNN and KRR methods offer 98.50% and 98.75% accuracy, outperforming the conventional support vector machine and extreme learning based solutions.

Sea-Land Clutter Classification Based on Graph Spectrum Features

“…Radar clutter from sea and land surfaces is time-varying and nonstationary, so we transfer the data to a piecewise steady signal by separating the entire time series into a number of segments; each segment is referred to as the quasi-steady state of the signal in the short time interval. Then, we select the value of the segment length d as in our previous work [39]. For a sampled signal x(t) with a length of l, let d be the length of each segment, and let the signal x(t) be segmented into…”

“…Furthermore, we evaluated the impact of different features on the additional nine datasets shown in Table 3. The existing valid features, such as ECVA features [39] and RTT (relative average amplitude, temporal information entropy and temporal hurst exponent) features in [37], are compared with the proposed graph features on different datasets, and the results are shown in Figure 8. It can clearly be seen that the testing accuracy based on the proposed feature set and ECVA feature set remains higher than that of the RTT.…”