Detection reliability for passive infrared detectors in intrusion and hold-up alarm systems and their ergonomics

ArticleCurrently it is highly important for detectors to be able to achieve efficiency, reliability, and faultless operation, and to be ergonomic thanks to their assembly and being easy-to-fit. In the case of a proposal for the placement of detectors it is naturally important to determine position of the detector and the type of detector being used, but also to guarantee their capability to be able to detect anything when in use and their user and installation-friendliness. The problem of passive infrared (PIR) detectors affects a large proportion of intrusion and hold-up alarm systems (I&HAS). In a time of increasing property crime, it is highly important for PIR detector to actually be able to detect break-in attempts within the guarded area on a reliable basis and free of error. In the case of the installation of PIR detectors, it is naturally important not only to ensure correct installation, to gauge the external influences which may impact upon the detector and to ensure proper maintenance, but also to guarantee the capability of detection under more arduous conditions. The tests and comparisons which have been conducted examine both the normal operation of the PIR detectors and the ergonomics of these detectors. These tests are important both from an informative perspective and due to the opportunities to be able to develop potential counter-measures which could lead to their improvement

Sequential Decision Algorithms for Measurement-Based Impromptu Deployment of a Wireless Relay Network along a Line

We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (which we call an explore-forward approach). We consider a light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure as-you-go approach. Next, we propose two learning algorithms for the explore-forward approach, based on Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical, model-free algorithms for deployment over large regions.Comment: 29 pages. arXiv admin note: text overlap with arXiv:1308.068

An animation-and-chirplet based approach to intruder classification using PIR sensing

The development of a Passive Infra-Red (PIR) sensing based intrusion detection system is presented here having the ability to reject vegetative clutter and distinguish between human and animal intrusions. This has potential application to reducing human-animal conflicts in the vicinity of a wildlife park. The system takes on the form of a sensor-tower platform (STP) and was developed in-house. It employs a sensor array that endows the platform with a spatial-resolution capability. Given the difficulty of collecting data involving animal motion, a simulation tool was created with the aid of Blender and OpenGL software that is capable of quickly generating streams of human and animal-intrusion data. The generated data was then examined to identify a suitable collection of features that are useful in classification. The features selected corresponded to parameters that model the received signal as the superimposition of a fixed number of chirplets, an energy signature and a cross-correlation parameter. The resultant feature vector was then passed on to a Support Vector Machine (SVM) for classification. This approach to classification was validated by making use of real-world data collected by the STP which showed both STP design as well as classification technique employed to be quite effective. The average classification accuracy with both real and simulated data was in excess of 94%