Compared with the classical constant false alarm ratio (CFAR) detector, the neural network
(NN)-based detector has data-driven representation learning ability, which can improve the …

This paper proposes a novel algorithm for retrieving the ocean wind vector from marine
radar image sequences in real time. It is presented as an alternative to mitigate anemometer …

One of the most relevant parameters to characterize the severity of ocean waves is the
significant wave height (H s). The estimate of H s from remotely sensed data acquired by …

Image deterioration problem occurs in radar imaging for ship target, which results from the
complex time-varying motions of ship, the noise in channels, and the clutter on sea surface …

Due to the increasing complexity of electromagnetic signals, there exists a significant
challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach …

The application of supervised learning machines trained to minimize the Cross-Entropy error
to radar detection is explored in this article. The detector is implemented with a learning …

Due to nonlinear varying motions of the physical sea surface, sea clutter suppression is
challenging with the broadened and rapidly changed Doppler spectrum content. In this …

This paper presents a new approach of compensating truck scale's weighing errors based
on prior knowledge and neural network ensembles (PKNNEs). Truck scale is a typical …

Constant false alarm rate (CFAR), a target detection method commonly used in the radar
systems, has an inconsistent performance against various environments. For improving the …

In this paper, the performance analysis of the natural frequency-based radar target
recognition in the time domain is considered. We investigate the dependence of the …