Abstract
This paper first establishes a new complex independent component analysis (cICA) algorithm based on the spatiotemporal extension of complex-valued entropy bound minimization (CEBM) to separate received complex-valued radar signals. Next, we propose a new cICA-based detector with an open structure to find Swerling model targets, lognormal targets, and sea-surface small floating targets in coherent high-resolution maritime surveillance radars. The detector encountered three major problems when adopting cICA for detection and solved them using three effective suggestions. After performing cICA on the time series received by the radar, we obtained two different sources. Using the first and second theoretical and empirical moment estimates of the K-distribution, the target was selected between these two output source signals. Detector performance was verified quantitatively and qualitatively using the real-life IPIX radar database. Comprehensive experiments on this database with synthetic injected targets showed promising results. The computational time and sample size dependency of the proposed cICA algorithm were also discussed. Finally, a comparison of the detector with several novel detectors for detecting sea-surface floating small targets of the IPIX radar database demonstrated the proposed detector’s superiority.
Highlights
Academic Editor: Sergio Baselga is paper first establishes a new complex independent component analysis algorithm based on the spatiotemporal extension of complex-valued entropy bound minimization (CEBM) to separate received complex-valued radar signals
Introduction e detection of radar targets using the concepts of other fields, e.g., independent component analysis (ICA) [1,2,3], blind source separation (BSS) [4, 5], artificial intelligence, and machine learning methods such as neural networks [6], fuzzy theory [7], graph theory [8], and support vector machines (SVM) [9] has been an active area of research in recent years. ese nonlinear strategies have been outlined to overcome troublesome radar detection problems that cannot be resolved via routine radar detection strategies, e.g., Neyman–Pearson (NP)- and Bayesian-based detectors or CFAR and GLRT detector classes
Note that if the complex-valued ICA (cICA) detector uses Complex-valued entropy rate bound minimization (CERBM) or IQ-spatiotemporal FastICA (STFICA) in the cICA block instead of our Convolutive CEBM (CCEBM) algorithm, it leads to similar results qualitatively. e results are unacceptable for other algorithms, e.g., CEBM, nc-FastICA, JADE, and even native PCA or LDA
Summary
Academic Editor: Sergio Baselga is paper first establishes a new complex independent component analysis (cICA) algorithm based on the spatiotemporal extension of complex-valued entropy bound minimization (CEBM) to separate received complex-valued radar signals. In coherent high-resolution maritime surveillance radars, due to the complex and nonstationary nature of the returned backscatters from the sea surface, the detection of low-velocity or floating small targets is a challenging problem as the target Doppler frequency occurring in the clutter Doppler cell makes clutter filtering impossible [14]. In this circumstance, the application of nonlinear signal processing strategies, e.g., cICA, is appealing for two primary reasons. Other general assumptions of the cICA algorithms are valid for radar signals since, as mentioned in [4], clutter and target signals are independent of each other. ey are noncircular and non-Gaussian distributed signals
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