In this paper we develop algorithms for distributed computation of a broad range of estimation and detection tasks over networks with arbitrary but fixed connectivity. The distributed algorithms we develop are linear dynamical systems that generate sequences of approximations to the desired computation. The algorithms are locally constructed at each node by exploiting only locally available and macro-scopic information about the network topology. We present methods for designing these distributed algorithms so as to optimize the convergence rates to the desired computation and demonstrate their performance characteristics in the context of a problem of signal estimation from multi-node signal observations in Gaussian noise.

Locally constructed algorithms for distributed computations in ad-hoc networks

In this paper, we develop algorithms for distributed computation of averages of the node data over networks with arbitrary but fixed connectivity. The algorithms we develop are linear dynamical systems that generate sequences of improving approximations to the desired computation at each node, via iterative processing and broadcasting. The algorithms are locally constructed at each node by exploiting only locally available and macroscopic information about the network topology. We present methods for optimizing the convergence rates of these algorithms to the desired computation, and evaluate their performance characteristics in the context of a problem of signal estimation from multinode noisy observations. By conducting simulations based on simple power-loss propagation models, we perform a preliminary comparison of the algorithms we develop against other types of distributed algorithms for computing averages, and identify transmit-power optimized algorithmic implementations as a function of the size and density of the sensor network.

Distributed computation of averages over ad hoc networks

The invention is a method and system for aggregating quantitative data and qualitative information from a plurality of sensors, each sensor having a plurality of associated sensor uncertainties, to determine the most likely position of an object and an associated probability distribution for the most likely position of the object. Statistical analysis and probability theory is preferably used to more accurately locate the object and to minimize the range of the associated probability distribution. The method and system are applicable for locating and tracking a plurality of objects.

Multi-sensor information fusion technique

With the ever increasing complex sensing and actuating tasks in manufacturing plants, intelligent sensors cluster in hybrid networks becomes a rapidly expanding area. They play a dominant role in many fields from macro and micro scale. Global object control and the ability to self organize into fault-tolerant and scalable systems are expected for high level applications. In this paper, new structural concepts of intelligent sensors and networks with new intelligent agents are presented. Embedding new functionalities to dynamically manage cooperative agents for autonomous machines are interesting key enabling technologies most required in manufacturing for zero defects production.

https://www.mdpi.com/1424-8220/6/6/557/pdf

Further Structural Intelligence for Sensors Cluster Technology in Manufacturing

Procedure for the monitoring of large surface areas, in which procedure the area (10) to be monitored is observed by means of a monitoring unit (14) comprising a thermal camera (2) and an electromagnetic radar (3), which are rotated about a vertical shaft to scan the area to be monitored in a horizontal plane, and a processing device (13) for the processing of the data provided by the thermal camera and the radar. In the procedure, the surface area (10) to be monitored is divided into surface elements (12) as a function of the angle of rotation of the vertical shaft and the scanning distance, and monitoring criteria are defined for the surface elements; movements in the surface area to be monitored are observed by means of the electromagnetic radar (3); objects (1) differing from their surroundings in respect of temperature are searched by means of the thermal camera (2); and the location of the object, i.e. the surface element corresponding to the object, is determined and the object is identified by means of either the radar or the thermal camera, depending on the conditions and the distance of observation.

Surveillance method for wide areas

The purpose of an intelligent alarm analysis system is to provide complete and manageable information to a central alarm station operator by applying alarm processing and fusion techniques to sensor information. The paper discusses the sensor fusion approach taken to perform intelligent alarm analysis for the Advanced Exterior Sensor (AES). The AES is an intrusion detection and assessment system designed for wide area coverage, quick deployment, low false/nuisance alarm operation, and immediate visual assessment. It combines three sensor technologies (visible, infrared, and millimeter wave radar) collocated on a compact and portable remote sensor module. The remote sensor module rotates at a rate of 1 revolution per second to detect and track motion and provide assessment in a continuous 360d field-of-regard. Sensor fusion techniques are used to correlate and integrate the track data from these three sensors into a single track for operator observation. Additional inputs to the fusion process include environmental data, knowledge of sensor performance under certain weather conditions, sensor priority, and recent operator feedback. A confidence value is assigned to the track as a result of the fusion process. This helps to reduce nuisance alarms and to increase operator confidence in the system while reducing the workload of the operator

/pdf/sensor-fusion-for-intelligent-alarm-analysis-24i7f6v54z.pdf

Sensor fusion for intelligent alarm analysis

The purpose of an intelligent alarm analysis system is to provide complete and manageable information to a central alarm station operator by applying alarm processing and fusion techniques to sensor information. This paper discusses the sensor fusion approach taken to perform intelligent alarm analysis for the Advanced Exterior Sensor (AES). The AES is an intrusion detection and assessment system designed for wide-area coverage, quick deployment, low false/nuisance alarm operation, and immediate visual assessment. It combines three sensor technologies (visible, infrared, and millimeter wave radar) collocated on a compact and portable remote sensor module. The remote sensor module rotates at a rate of 1 revolution per second to detect and track motion and provide assessment in a continuous 360/spl deg/ field-of-regard. Sensor fusion techniques are used to correlate and integrate the track data from these three sensors into a single track for operator observation. Additional inputs to the fusion process include environmental data, knowledge of sensor performance under certain weather conditions, sensor priority, and recent operator feedback. A confidence value is assigned to the track as a result of the fusion process. This helps to reduce nuisance alarms and to increase operator confidence in the system while reducing the workload of the operator.

/pdf/sensor-fusion-for-intelligent-alarm-analysis-2t4ckd2260.pdf

The purpose of an intelligent alarm analysis system is to provide complete and manageable information to a central alarm station operator by applying alarm processing and fusion techniques to sensor information. This paper discusses the sensor fusion approach taken to perform intelligent alarm analysis for the Advanced Exterior Sensor (AES). The AES is an intrusion detection and assessment system designed for wide-area coverage, quick deployment, low false/nuisance alarm operation, and immediate visual assessment. It combines three sensor technologies (visible, infrared, and millimeter wave radar) collocated on a compact and portable remote sensor module. The remote sensor module rotates at a rate of 1 revolution per second to detect and track motion and provide assessment in a continuous 360` field-of-regard. Sensor fusion techniques are used to correlate and integrate the track data from these three sensors into a single track for operator observation. Additional inputs to the fusion process include environmental data, knowledge of sensor performance under certain weather conditions, sensor priority, and recent operator feedback. A confidence value is assigned to the track as a result of the fusion process. This helps to reduce nuisance alarms and to increase operator confidence in the system while reducing the workload of the operator.

/pdf/sensor-fusion-for-intelligent-alarm-analysis-5fnw0takvn.pdf

C.L. Nelson

Papers

Sensor fusion for intelligent alarm analysis

Sensor fusion for intelligent alarm analysis

Sensor fusion for intelligent alarm analysis