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Surface Plasmon Resonance has been developed into a widely-used methodology for various biosensing applications. For the most popular angular-interrogation SPR system, a convergent light beam and a photo diode array are used, where the photo diode array with a large amount of pixels is necessary for high performance, and unavoidable trivial vibration between sensing unit and photo diode array will...
A distributed network of sensors leverages its performance by aggregating information gathered by individual sensors. This process is referred to as sensor fusion. The primary goal of sensor fusion is to process and progressively refine information from multiple sensors to eventually create situation awareness (SA). Sensor fusion requires sensors to exchange data and information over a network with...
To address perception problems we must be able to track dynamic objects of the environment. An important issue of tracking is the association problem in which we have to associate each new observation with one existing object in the environment. This problem is complex: unfortunately, the number of observations generally does not correspond to the number of objects. Moreover, the number of objects...
The exponential embedding of two or more probability density functions (PDFs) is proposed for multimodal sensor processing. It approximates the unknown PDF by exponentially embedding the known PDFs. Such embedding is of a exponential family indexed by some parameters, and hence inherits many nice properties of the exponential family. It is shown that the approximated PDF is asymptotically the one...
The problem of robust estimation of the complex amplitudes of sinusoidal signals using multiple sensors, in an unknown heavy-tailed, spatially and temporally i.i.d. noise environement is considered. A semiparametric approach for this case is presented, where non-parametric estimation of the noise density is succeeded by maximum likelihood estimation incorporating the estimated density. The suggested...
The cooperative operation can improve the sensing performance of cognitive radio networks and reduce the sensing time. Combining multiple cognitive users' local detection results and making accurate judgment is essential to improve cooperative gain. According to uploaded information from cognitive users, hard decision based on the combination of large numbers and soft decision based on the confidence...
Sensor fusion of multiple sources plays an important role in robotic systems to achieve refined target position and velocity estimates. In this paper, we address the general registration problem, which is a key module for a fusion system to accurately correct systematic errors of sensors. A fast maximum a posteriori (FMAP) algorithm for joint registration and tracking is presented. The algorithm uses...
Reliable sand detection is an important component of oil production system. In practice, produced sand in oil pipelines poses a serious problem in many production situations, since a small amount of sand in the produced fluid can result in significant erosion in a very short time stage. A new data fusion framework for sand detection in pipeline is presented. The framework is collecting data from oil...
Designing, building, and launching missions to deploy space based sensors typically take many years and cost billions of dollars. Missions are often delayed or canceled, and data from some parts of the world may be unavailable. When a physical sensor is unavailable for any reason, we propose the notion of a virtual sensor, in which we exploit the hundreds of spaced based sensors already observing...
In this paper, we study the source localization problem in wireless sensor networks. Sensors transmit their quantized signal amplitude measurements to the fusion center and source location is estimated based on these quantized measurements. In this paper, we propose an energy efficient iterative localization scheme, where the algorithm starts with a coarse location estimate obtained from a set of...
System-level discrimination performance for missile defense relies on how well data can be associated between participating sensors. Under the existing architecture, there may be a handover of tracks between two sensors in which tracks formed by one sensor are passed to another sensor to improve knowledge of the targets. The global nearest pattern matching (GNPM) problem is a mathematical programming...
Sound source localization and tracking of moving sound source (displacement tracking) has received a lot of attention lately because of its importance in a number of next generation applications. In this paper we perform these operations using distributed microphone arrays. We generate spatial likelihood function using steered response power with phase transform filter (SRP-PHAT) for all microphone...
When an unknown target emits a radio signal, its position can be localized by a network of sensors (or radar receivers) using the time difference of arrival (TDOA) information. In this paper, we consider the maximum likelihood formulation of this target localization problem and provide efficient convex relaxations for this nonconvex optimization problem. In addition, we propose a robust target localization...
In this paper, two types of distributed constant false alarm rate (CFAR) detectors; binary and fuzzy distributed detectors, are introduced. In these two types of distributed detectors, it was assumed that the clutter parameters at the local sensors are unknown and each local detector performs CFAR processing based on maximum likelihood (ML) and order statistic (OS) CFAR processor before transmitting...
While elegant in form, the maximum likelihood estimator (MLE) for heavily bandwidth-constrained distributed estimation in Gaussian noise is computationally expensive to implement. We consider an alternative estimator for this case which requires far less computational complexity, yet performs close to the MLE under the same operating conditions.
We formulate the target tracking based on received signal strength in the sensor networks using Bayesian network representation. Data fusion among the same type of sensors in an active sensor neighborhood is referred to as cross-sensor fusion, conceptualized as "cooperative fusion". This data fusion is embedded in the likelihood function derivation. Fusion of signals collected by multiple...
We develop a probabilistic technique for performing multiple target detection and tracking based on data from multiple, flying, sensors. Multiple sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, the data association problem can cause the computational complexity of standard...
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