Toward a maritime surveillance objective, many ship detection and tracking algorithms have been investigated but are faced with poor performance in practical ocean environments. Compact high-frequency (HF) radar has also faced critical issues due to its long coherent processing interval and varying response from its orthogonal antenna structure. Hence, a simulator based on compact HF radar is proposed in this letter to provide a guideline for effective assessment of ship detection and tracking algorithms while considering these practical issues. To validate the proposed simulator, the simulator generated data has been compared with real data obtained by the compact HF radar sites.

This paper proposes an effective method of improving ship detection performance of a compact high-frequency (HF) radar system which has been primarily optimized for observing surface radial current velocities and bearings. Previously developed ship detection systems have been vulnerable to error sources such as environmental noise and clutter when they are applied in a compact HF radar optimized for observing surface current. In particular, the influences of error are reduced by applying a principle component analysis of the generated range-Doppler maps. A compact radar signal model is first developed by the data acquired from an operating compact HF radar site. The proposed method is then validated by comparing it to the conventional ship detection method in terms of detection and false alarm rates. The experimental results confirm that the proposed method shows superior performance in both simulated and practical environments.

High-frequency surface wave radar (HFSWR) suffers from a man-made interference because its amplitude is high enough to mask the Bragg scattering signal. Although several methods have been proposed for resolving this problem, they are inapplicable to compact HFSWR due to their antenna structures. This letter proposes an effective method of suppressing man-made radio frequency interference for compact HFSWR. The proposed method is composed of man-made interference detection and suppression by using regression based on probabilistic signal model. The proposed method is demonstrated in comparison with conventional methods in terms of root-mean-square error in experiments using synthetic and real data. The results show that the proposed method outperforms other methods in both simulated and practical situations.

Automatic emotion recognition based on facial cues, such as facial action units (AUs), has received huge attention in the last decade due to its wide variety of applications. Current computer-based automated two-phase facial emotion recognition procedures first detect AUs from input images and then infer target emotions from the detected AUs. However, more robust AU detection and AU-to-emotion mapping methods are required to deal with the error accumulation problem inherent in the multiphase scheme. Motivated by our key observation that a single AU detector does not perform equally well for all AUs, we propose a novel two-phase facial emotion recognition framework, where the presence of AUs is detected by group decisions of multiple AU detectors and a target emotion is inferred from the combined AU detection decisions. Our emotion recognition framework consists of three major components — multiple AU detection, AU detection fusion, and AU-to-emotion mapping. The experimental results on two real-world face databases demonstrate an improved performance over the previous two-phase method using a single AU detector in terms of both AU detection accuracy and correct emotion recognition rate.

This letter proposes a generative network based on adversarial learning for synthesizing short-time audio streams and investigates the effectiveness of data augmentation for amphibian call sounds classification. Based on Fourier analysis, the generator is designed by a multi-layer perceptron composed of frequency basis learning layers and an output layer, and a discriminator is constructed by a convolutional neural network. Additionally, regularization on weights is introduced to train the networks with practical data that includes some disturbances. Synthetic audio streams are evaluated by quantitative comparison using inception score, and classification results are compared for real versus synthetic data. In conclusion, the proposed generative network is shown to produce realistic sounds and therefore useful for data augmentation.