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The contemporary operating environment (COE) imposes significant challenges for military personnel and systems. The asymmetric nature of the threat and the proliferation of sophisticated weapon systems to rogue nations and trans-national terrorist organizations only further the need to ensure friendly forces maintain a high-level of warfighter readiness. As this threat evolves the importance of higher-level...
Energy based detection measures sensor received signal strength (RSS) transmitted from a target. In this paper, we propose a new approach for estimating a moving target trajectory over a sensor field via energy based detections as an alternative to trilateration positioning or nonlinear estimation. In 2D case, possible target locations described by a RSS ratio from two sensors are approximated using...
Intent inference involves analyzing the actions and activities of a target of interest to deduce its purpose. In an environment cluttered with many targets, loaded with information, and under stress, the human may not be able to perform well Hence a cognitive aid that could derive possible intent inference and monitor the target may help augment human cognition and assist critical human decision making...
In this paper, we propose a new maximum-likelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach "channel-aware target...
In this paper a hybrid Kalman filter is derived for the tracking of ground based targets. The propagation is performed using unscented Kalman filter equations, while the updates are performed using extended Kalman filter equations. The novel feature of this hybrid filter is that terrain information has been incorporated to improve the accuracy of state estimates. This information, termed trafficability,...
The fundamental point of this paper is that the fusion of several simple, somewhat unreliable, and somewhat inefficient frontal face detectors results in an efficient and reliable frontal face detector which, without any training, performs similarly to a state-of-the-art neural network based face detector trained on 60,000 images. The simple detectors used include a skin detector, symmetry detectors,...
Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A two-filter smoother that uses a Backward Sigma-Point Information Filter, and also a forward-backward Rauch-Tung-Striebel (RTS) smoothing form are re-derived using the weighted statistical...
Multimedia documents are increasingly numerous. Their efficient management requires tools to provide services that measure up to users' expectations, based on the contents of these voluminous document databases. This implies a number of challenges. Although we can extract highly symbolic concepts from texts, a wide semantic gap appears when processing images and sound. Thus, we propose using data...
In this paper, we present two novel methods to handle the fusion of multiple Bayesian Network knowledge fragments which we termed N-Combinator and N-Clone. In DSO National Laboratories, we have developed a cognition based dynamic reasoning machine called D'Brain capable of performing high level data fusion. Knowledge is encapsulated in D'Brain as Bayesian Networks knowledge fragments. D'Brain is dynamic...
This work addresses off-line accurate trajectory reconstruction for air traffic control. We propose the use of specific dynamic models after identification of regular motion patterns. Datasets recorded from opportunity traffic are first segmented in motion segments, based on the mode probabilities of an IMM filter. Then, reconstruction is applied with an optimal smoothing filter operating forward...
An new object oriented development suite for data fusion is presented. It is shown how the various issues in the data fusion development like design, implementation, simulation and testing are realised and automated by this development suite. This allows the realisation of high sophisticated data fusion systems as applied in numerous civil and defence areas, like air traffic or satellite orbit control,...
The first challenge in crisis response management is the early damage and needs assessment based on all incoming information from various sources such as on field deployed sensors or human observations. Likewise, it is important for an efficient crisis response management to make sure that ambiguous terminology is clearly defined, and methodologies and indicators explained. To support the damage assessment,...
Information fusion is a key factor for insuring information superiority in various military and civil surveillance systems. Military applications may be found in air defence, force and convoy protection, and combat management systems for naval ships. Coastal and urban surveillance, air traffic control, and Harbour protection address more civil areas. A new aspect in all these applications is the issue...
This paper proposes an automatic semantic video content indexing and retrieval system based on fusing various low level visual and shape descriptors. Extracted features from region and sub-image blocks segmentation of video shots key-frames are described via IVSM signature (Image Vector Space Model) in order to have a compact and efficient description of the content. Static feature fusion based on...
The exploitation of bistatic Doppler measurements for multistatic tracking is considered. It is found through simulation, that, while the velocity estimation of the standard extended Kalman filter is improved in monostotic situations and multistatic situations where measurement errors are small, a degradation in performance is observed in multistatic situations where the measurement errors are realistically...
This paper describes a generalization of Murty's algorithm generating ranked solutions for classical assignment problems. The generalization extends the domain to a general class of zero-one integer linear programming problems that can be used to solve multi-frame data association problems for track-oriented multiple hypothesis tracking (MHT). The generalized Murty's algorithm mostly follows the steps...
Probabilistic multi-hypothesis tracking (PMHT) is an algorithm for tracking multiple targets when measurement-to- target assignments are unknown and must be jointly estimated with the target tracks. Multi-frame assignment PMHT (MF- PMHT) is an algorithm designed to mitigate some performance problems associated with PMHT. In MF-PMHT, the PMHT algorithm is applied to multi-frame sequences in the last...
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