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This paper introduces a highly integrated system providing very accurate object detection with RGB-D sensor. To solve the problem that there are always insufficient training sets for object detection in real world, we present an online learning architecture to learn templates and to detect objects real-time. The proposed novel concept skips the training phase required in previous recognition works,...
Information fusion is the keystone of many surveillance systems, in which the security of the information is a crucial aspect. This paper proposes a method to fuse information exchanging only encrypted data, through a secure extension of the popular consensus gossip algorithm using secure multi-party computation methodology. Sensor entities exchange only encrypted information and never have direct...
Taking into account the existence of multipath ionospheric propagation (MIP), this paper develops the received signal model for a non-point target for multiple-input multiple-output skywave over-the-horizon (MIMO-OTH) radar for the first time. The model describes the ionospheric state, the number of propagation paths between a radar antenna and the target center, as well as the statistics of the reflection...
We consider the problem where a large known library of L alternatives is available and we wish to maximize the detection power in a worst case scenario. The considered minimax detection approach relies on a GLR test allied to a sparsity constraint. This approach conditions the optimization of the target subspaces, in number r ≪ L. While the exact solution of the minimax optimization problem can be...
Sparse-signal processing (SSP) is interpreted in this paper as a sparse model-based refinement of typical steps in radar processing. Matched filtering remains vital within SSP but joined with radar detection promoting the sparsity. Realistic measurements are also supported in SSP by using MonteCarlo (MC) methods. MC-based SSP promotes the sparsity by detection-driven MC-sampling that also improves...
We show that the optimal design of non-randomized discrete sequential tests, i.e., tests whose test statistics take on only a countable number of states, can be modeled as a mixed integer linear problem. This is done by reformulating the difference equations describing the random walk on the integer lattice in terms of linear mixed integer constraints. We outline the general procedure and give a simple...
In this study, a method that aims at detecting small and faint objects in noisy hyperspectral astrophysical images is presented. The particularity of the hyperspectral images that we are interested in is the high dynamics between object intensities. Detection of the smallest and faintest objects is challenging, because their signal-to-noise ratio is low, and if the brightest objects are not well reconstructed,...
We present an inference framework for automatic detection of activations of home appliances based on voltage envelope waveforms. We cast the problem of appliance detection and recognition as an inference problem. When the activation signatures are known, the problem reduces to a simple detection problem. When the activation signatures are unknown, the problem is reformulated as a blind joint delay...
In this paper, we address a technique and related algorithms for precise detection, parametric imaging and classification of small marine targets in a harsh sensing environment attributed for heavy sea clutter via noncooperative processing of the GPS-based Forward Scatter Radar (FSR) data. In contrary to GPS L5 detection approach, the proposed technique utilizes civil GPS L1 signal formats in FSR...
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