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This paper presents a novel sequential variational inference algorithm for distributed multi-sensor tracking and fusion. The algorithm is based on a multi-sensor target representation where a target is represented jointly by its states at different sensors and a global state fusing all sensor data. A tree-structured graphical model is adopted to model the dependencies between these states at a time...
Target tracking from incomplete measurements of distinct sensors in a sensor network is a task of data fusion, present in a lot of applications. Difficulties in tracking using extended Kalman filters lead to unstable behavior, mainly caused by difficult initialization. Instead of using numerical batch-estimators, we offer an analytical approach to initialize the filter from a minimum number of observations...
Errors due to sensor bias are often present in sensor data and can reduce the tracking accuracy and stability of multi-sensor systems. The other practical problem is that the target data reported by the sensors are usually not time-coincident or synchronous due to the different data. This paper deals with these problems and presents a new algorithm for estimation of both constant and dynamic biases...
An algorithm for detection and tracking of multiple targets using bearings measurements from several sensors is developed. The algorithm is an implementation of a multiple hypothesis tracker with pruning of unlikely hypotheses. Tracking conditional on each hypothesis can be performed using any suitable filtering approximation. In this paper a range- parameterized unscented Kalman filter is used. Each...
DSTO Australia concluded a two year research program in March 2006 that demonstrated distributed, autonomous, self-organising stand-in sensor and effector systems. This paper describes research activities from this program including the development of miniaturised sensor and effector payloads, the development of novel algorithms for the fusion of data from distributed sensor payloads and the development...
This paper addresses the problem of sensor management for a large network of agile sensors. Sensor management refers to the process of dynamically retasking agile sensors in response to an evolving environment. Sensors may be agile in a variety of ways, e.g., the ability to reposition, point an antenna, choose sensing mode, or waveform. The goal of sensor management in a large network is to choose...
Cyber-physical networks promise to identify the spatial diffusion-dispersion phenomena by utilizing sensors to measure the underlying physical phenomenon and using models and information operations executed on computational nodes to estimate the parameters. We propose a projective space that utilizes the parameters of the underlying phenomenon, each of which might be measured or computed or both using...
The rapid evolution of distributed sensor systems involving multiple heterogeneous sensors, the combination of mobile and stationary sensors, and dynamic information requirements presents a challenge for system resource utilization. On one hand, evolving standards and service oriented architecture concepts enables creation and use of multiple sensors and resources (including human reports). These...
In this paper a comparison is made between a sensor selection algorithm (SSA) based on the modified Riccati equation (MRE) on the one hand, and a random sensor selection (RSS) or a fixed sensor selection (FSS) scheme on the other hand. The goal is to investigate the benefits the MRE SSA yields compared to the other selection schemes. The MRE SSA is capable of handling sensors with probability of detection...
This work addresses the design of a distributed fault-tolerant decision fusion in the presence of sensor faults when the local sensors sequentially send their decisions to a fusion center. A collaborative sensor fault detection (CSFD) scheme is proposed here to eliminate unreliable local decisions when performing distributed decision fusion. Based on the pre-designed fusion rule, assuming identical...
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