Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems

Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems

Saad Iqbal (NUST School of Electrical Engineering and Computer Science (SEECS), Pakistan), Usman Iqbal (NUST School of Electrical Engineering and Computer Science (SEECS), Pakistan) and Syed Ali Hassan (NUST School of Electrical Engineering and Computer Science (SEECS), Pakistan)
DOI: 10.4018/978-1-5225-7458-3.ch012

Abstract

Target localization and tracking has always been a hot topic in all eras of communication studies. Conventional system used radars for the purpose of locating and/or tracking an object using the classical methods of signal processing. Radars are generally classified as active and passive, where the former uses both transmitter and receivers simultaneously to perform the localization task. On the other hand, passive radars use existing illuminators of opportunity such as wi-fi or GSM signals to perform the aforementioned tasks. Although they perform detection using classical correlation methods and CFAR, recently machine learning has been used in various application of passive sensing to elevate the system performance. The latest developed models for intelligent RF passive sensing system for both outdoor and indoor scenarios are discussed in this chapter, which will give insight to the readers about their designing.
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Background

The concept of passive radars is not new. Back in 1935, Robert Watson Watt demonstrated the working of the radar by detecting a Handley Page Heyford bomber from a distance of 12 km using the BBC shortwave transmitter at Daventry as mentioned in (Chodos et al., 2006). During World War 2, the project named “Klein Heidelberg” was initiated by the Germans as described in (Griffiths & Willis, 2010) for long-range air surveillance. Six of these bi-static radars were deployed along the Belgian, Dutch and French side of the English Channel and North Sea. These radars didn’t have any dedicated transmitters and worked by using the reflections from the Chain Home (British coastal radar system).

Key Terms in this Chapter

Multi-Path: In wireless telecommunications, multipath is the propagation phenomenon that results in radio signals reaching the receiving antenna by two or more paths.

DPI: Direct path interference are composed of direct echoes of the transmitter signals received by the surveillance receiver in passive sensing system.

Ambiguity Function: An ambiguity function is a two-dimensional function of time delay and Doppler frequency showing the variation in returned pulse due to the receiver matched filter.

CSI: In wireless communications, channel state information (CSI) refers to known channel properties of a communication link. This information describes how a signal propagates from the transmitter to the receiver and represents the combined effect of, for example, scattering, fading, and power decay with distance.

Deep Learning: Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised, or unsupervised.

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