PADF RF Localization Criteria for Multi-Model Scattering Environments

摘要

This paper provides a summary of recent results on a novel multi- platform RF emitter localization technique denoted as Position-Adaptive RF Direction Finding (PADF). This basic PADF formulation is based on the investigation of iterative path-loss based (i.e. path loss exponent) metrics estimates that are measured across multiple platforms in order to robotically/intelligently adapt (i.e. self-adjust) the location of each distributed/cooperative platform. Recent results at the AFRL indicate that this position-adaptive approach shows potential for accurate emitter localization in challenging embedded multipath environments (i.e., urban environments). As part of a general introductory discussion on PADF techniques, this paper provides a summary of our recent results on PADF and includes a discussion on the underlying and enabling concepts that provide potential enhancements in RF localization accuracy in challenging environments. Also, an outline of recent results that incorporate sample approaches to real-time multi-platform data pruning is included as part of a discussion on potential approaches to refining a basic PADF technique in order to integrate and perform distributed self- sensitivity and self-consistency analysis as part of a PADF technique with distributed robotic/intelligent features. The focus of this paper is on the experimental performance analysis of hardware-simulated PADF environments that generate multiple simultaneous mode-adaptive scattering trends. We cite approaches to addressing PADF localization performance challenges in these multi-modal complex laboratory simulated environments via providing analysis of our multimodal experiment design together with analysis of the resulting hardware-simulated PADF data.