Multiscale graph-based framework for efficient multi-sensor integration and event detection

Lakshman Prasad

doi:10.1117/12.2304288

Abstract

We present a general framework for integrating disparate sensors to dynamically detect events. Events are often observed as multiple, asynchronous, disparate sensors’ activations in time. The challenge is to reconcile them to infer that a process of interest is underway or has occurred. We abstractly model each sensor as a value-attributed time interval over which it takes values that are relevant to a known process of interest. Process knowledge is incorporated in the detection scheme by defining sensor neighborhood intervals that overlap with temporally neighboring sensor neighborhood intervals in the process. The sensor neighborhoods are represented as nodes of an interval graph, with edges between nodes of overlapping sensor neighborhoods. Sensor activity is then interpreted via this process model by constructing an interval graph time series, for relevant sensor types and process-driven neighborhoods, and looking for subgraphs that match those of the process model graph. A time series that dynamically records the number of sensor neighborhoods overlapping at any given time is used to detect temporal regions of high sensor activity indicative of an event. Multiscale analysis of this time series reveals peaks over different time scales. The peaks are then used to efficiently triage underlying interval subgraphs of sensor activity to examine them for relational patterns similar to the process model graph of interest. Thus, our framework synergistically uses relational as well as scale information to dynamically and efficiently triage sensors related to a process. Multiple processes of interest may be efficiently detected and tracked in parallel using this approach.

Conference Presentation

Citation Download Citation

Lakshman Prasad, Lakshman Prasad, } "Multiscale graph-based framework for efficient multi-sensor integration and event detection", Proc. SPIE 10646, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVII, 106460Q (27 April 2018); doi: 10.1117/12.2304288; https://doi.org/10.1117/12.2304288

ACCESS THE FULL ARTICLE

PERSONAL SIGN IN
Full access may be available with your subscription

Email or Username Forgot your username?

Password Forgot your password?

Show

Remember Email/Username on this computer

Remember Password

No SPIE account? Create an account
or Institutional Sign In via Shibboleth

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $14.40

Non-members: $18.00 ADD TO CART

Keywords/Phrases

Search In:

Publication Years