Lin Haas

GLANSER: Geospatial location, accountability, and Navigation System for Emergency Responders - system concept and performance assessment

A system that provides accurate and reliable location of Emergency Responders (ERs) in all types of environments presents multifaceted technological challenges. The system is intended to provide indoor/outdoor precision navigation, robust communications and real-time position updates on remote command display devices. Operational requirements include rapid and nonintrusive deployment, scalability to 500 users and seamless integration with existing procedures. Additional challenges are imposed by the need for a device that minimizes size, weight, and power with the ability to operate in uncertain and potentially hazardous in-building environments. The Department of Homeland Security Science and Technology Directorate (Program Manager - Dr. Jalal Mapar) has sponsored Honeywell, with team members Argon ST and TRX Systems, to develop the Geo-spatial Location, Accountability and Navigation System for Emergency Responders (GLANSER). GLANSER is currently in its Option 1 phase which is the second of a four-year program to migrate the technology from concept development all the way to product and operations (1). This paper describes development of both the current and continuing development of GLANSER system components, including the overall architecture, the navigation sensors (e.g. IMU, Doppler velocimeter), the sensor fusion and navigator design, the integrated networking, ranging, and data communications radio, the display implementation and a description of the heuristic elements, including automatic map building and constraint-based navigation corrections. It also describes testing protocols and recent navigation performance results of the prototype system. The remainder of this document is organized into three major sections: Section I is introductory and provides background information including key system requirements, technical challenges, candidate approaches, and rationale for selection of approach, sensors, hardware, and algorithms employed by the GLANSER system. Section II describes the GLANSER system and its major subcomponents in greater detail (including descriptions of the User Interface display). Section III describes the underlying ranging and communications network on which the GLANSER system is based. The final section of this report (section IV) presents results which describe performance and navigation accuracy of the current system under test.

Synthetic aperture navigation in multipath environments

Multipath propagation is one of the major error sources for radio navigation systems. While many new applications require ever more precise user positioning in urban and indoor settings, existing receivers are especially vulnerable to severe multipath, which is often present in such environments. This article reviews recent advances in the concept of synthetic aperture navigation, which enables the user to separate co-channel navigation signals, including combating the adverse effects of multipath. A deeply integrated RF receiver and an inertial measurement unit produce an estimate of the user trajectory, which makes it possible to form a synthetic aperture from the users motion using a single-element antenna. In turn, using the synthetic aperture opens the door to applying modern direction finding and beamforming methods. These signal processing schemes are able to differentiate between the line of sight and multi path signals (or between different signals in the band in general), as they often arrive from different directions. The desired signal can be extracted in the presence of strong multipath, and its delay can be accurately estimated.

First responder location and tracking using Synthetic Aperture Navigation

The Synthetic Aperture Navigation (SAN) signal processing algorithm identifies the desired line of sight (LOS) signal component by exploiting user motion. As implied by the name, it forms a synthetic aperture along the user trajectory by taking multiple snapshots of signal correlation with the replica waveform over some period of time as the user moves. The synthetic aperture serves as an array, which enables beamforming with a single-element antenna. Fundamentally, this method discriminates between different signal components (e.g., line of sight and multipath) by their directions of arrival. SAN places the antenna array gain on the desired signal component and places nulls on all other components. This operation is applied to data from all correlators in the receiver, thus effectively providing the receiver discriminator with nearly multipath-free measurements. SAN is even able to produce a quality line of sight (LOS) measurement when the LOS component is much weaker than multipath.

Inverse Beamforming for navigating in multipath environments

Radionavigation in indoor and urban environments suffers from the effects of severe multipath and low signal strength. In this paper, we present a new technique, Inverse Beamforming, designed to reduce multipath power using signals from purpose deployed beacons. This technique complements other multipath mitigation methods which receivers use to make pseudorange measurements. Inverse beamforming is based on array signal processing by the user; however, a user needs only a single antenna, and the array itself is housed by the beacon (hence the name). Thus, a mobile user is not required to carry a cumbersome antenna array while taking full advantage of array signal processing gains. We present the concept and results of a laboratory test for this technique.

The IMRE Kalman filter — A new Kalman filter extension for nonlinear applications

The IMRE Kalman filter is designed to compute the measurement update, when nonlinearities are weak enough to be treated as a perturbation. This paper explores four different nonlinear effects that any nonlinear filter should be able to handle. Based on simple, intuitive cases we show that other comparable known filters (the second-order filter, the UKF, the IEKF, the Gauss-Hermite quadrature filter, or the cubature filter) handle only some, but not all four nonlinear effects. In contrast, the IMRE Kalman filter addresses all four kinds of nonlinear effects, which makes it more general.