In this paper, we propose an energy efficient cooperative monitoring algorithm for detecting and localizing adversarial communications in the presence of malicious reporting, Our solution is based on a combination of a game theory formulation for distributivity managing the monitoring-energy tradeoffs and a novel recursive trilateration algorithm which provides robustness to measurement errors and malicious reporting. Our results show that the energy consumption for monitoring is significantly reduced for our proposed solution compared to the all monitoring benchmark, and that the algorithm shows high robustness to malicious reporting. Our proposed method was able to still correctly localize the adversarial source within a specified tolerance error, and within a prescribed number of monitoring epochs, 83% of the time, when 25% of the monitoring nodes were compromised, reporting maliciously.
KEYWORDS: Visualization, Unmanned aerial vehicles, Sensors, Data fusion, System identification, Neural networks, Defense and security, Data acquisition, Classification systems
Object identification or classification has found many applications, ranging from civilian to defense application scenarios and there is a rising need for a both effective and efficient identification approach. One of the common methods for this task is using a neural network. However, it could be very difficult for such a network to obtain accurate answers due to complex environments, especially when the data is of single modality. In this paper, we attempt to build a combined deep learning model which takes two distinct data modalities to help us achieve high accuracy multimodal classification systems. An experiment is conducted on Multimodal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance (AU-AIR) using both visual and sensor data. We compare our results between a model trained with visual data only and another combined model trained with both visual and sensor data. Improved object classification performance is observed when the multimodal method is applied.
This work presents a novel method to reconstruct ultra-wideband radar signals over their entire bands when only a portion of the spectrum is available. The inherent sparse configuration of sensing radars is exploited to develop a robust wideband synthesizing framework. The proposed approach consists of the following two steps: 1) A radar signal model is developed which allows for a sparse representation of the target’s signature over wide bandwidths. 2) A flexible statistical model is introduced to solve the sparse model of Step 1 in a Bayesian framework. By imposing Gamma-Gaussian sparsity promoting priors on the target’s signature, the model improves the sensing accuracy. In addition, by introducing a Gaussian mixture model of noise, any non-deterministic density of noise or jamming can be accurately approximated. This significantly increases robustness in complex environments. The proposed approach is verified through the development of an ultra-wideband (12–110 GHz) radar system consisting of canonical-spherical targets. Monte Carlo simulations are carried out to compare the performance of the proposed statistical method when the data missing rate is 25% with other methods in the literature. It is shown that the proposed model has closed-form solutions and is robust to complex environments with satisfying performance. As the noise distribution violates the single Gaussian assumption, other relevant methods in the literature fail to recover the model with a large RMS error (< 0.50). In contrast, our method has a maximum RMS of only 0.11.
This paper proposes a novel region-based structure measure for object proposal ranking. It is able to efficiently reduce redundant object proposals and highlight dominant objects in an image. The computation of this measure is fully unsupervised, without any image level annotation or any visual semantics labeling. In this work, a new set of heuristic rules are introduced to indicate regions that may contain objects. The distinctiveness of a proposal region is assessed based on its structural uniqueness, structural distributions and deformable shapes. A scoring function is then constructed to combine these multi-view rule-based assessments into a single object score. Furthermore, a rank-recall optimization is proposed to optimize the scoring function for proposal ranking. The final optimized ranking significantly reduces the number of object proposals while maintaining potential object regions. Promising results show that the proposed ranking method simultaneously reduces proposals and highlights dominant objects.
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