This paper presents a description of recent research and development in HF passive bistatic radar (HFPBR) based on DRM digital AM broadcasting at Wuhan University, China. First, preliminary evaluation of its detection performance with special focus on the hybrid sky-surface wave propagation mode is introduced. Then, DRM broadcasting signal analysis as a radar waveform and associated signal processing techniques are described, consisting of ambiguity function analysis, reference signal extraction, multipath clutter rejection, and target localization. Finally, the experimental system and experimental data analysis are provided. Initial results from field experiments show that DRM-based HFPBR with hybrid sky-surface wave is a promising system for wide area moving target detection and ocean remote sensing.

Digital Radio Mondiale (DRM)-based HF passive bistatic radar has massive potential for surveillance purposes. Progress about DRM based passive bistatic radar is described.Several key problems need to be overcome for the base-band signal processing, including the vice-peaks suppression of DRM signal ambiguity function, the rejection of direct-path and multipath clutter in the surveillance channel and the extraction of the reference signal in the reference channel. Relative solutions of these problems will be presented. In addition, some results based on simulation data are shown in this paper.

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Face recognition plays an important role in many robotic and human–computer interaction systems. To this end, in recent years, sparse-representation-based classification and its variants have drawn extensive attention in compress sensing and pattern recognition. For image classification, one key to the success of a sparse-representation-based approach is to extract consistent image feature representations for the images of the same subject captured under a wide spectrum of appearance variations, for example, in pose, expression and illumination. These variations can be categorized into two main types: geometric and textural variations. To eliminate the difficulties posed by different appearance variations, the article presents a new collaborative-representation-based face classification approach using deep aligned neural network features. To be more specific, we first apply a facial landmark detection network to an input face image to obtain its fine-grained geometric information in the form of a set of 2D facial landmarks. These facial landmarks are then used to perform 2D geometric alignment across different face images. Second, we apply a deep neural network for facial image feature extraction due to the robustness of deep image features to a variety of appearance variations. We use the term deep aligned features for this two-step feature extraction approach. Last, a new collaborative-representation-based classification method is used to perform face classification. Specifically, we propose a group dictionary selection method for representation-based face classification to further boost the performance and reduce the uncertainty in decision-making. Experimental results obtained on several facial landmark detection and face classification data sets validate the effectiveness of the proposed method.

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