Collaborative GNSS Signal Processing

A. Soloviev, J. Dickman

Abstract:	A common feature of existing methods for improving the GNSS performance is that they attempt to enhance signal processing and/or navigation estimation parts of a single receiver. At the same time, potential that is inherent to the integration of data from multiple receivers remains mostly unexplored. To fill this technological gap, a concept of MUlti-platform Signal and Trajectory Estimation Receiver (MUSTER) was introduced and initially verified using experimental data [1]. This paper continues the development of multi-platform receiver technology and specifically focuses on benefits of integrating multi-node receiver data at the level of signal processing. Two case studies are considered including: 1) Collaborative GNSS signal processing for recovery of attenuated signals; and, 2) Use of multi-node antenna arrays for interference mitigation. The paper will describe signal processing methods developed and will use experimental data for their validation and performance demonstration. To exclude a single point of failure, the receiver network is implemented in a decentralized fashion. Each receiver obtains GNSS signals from other receivers via a communication link and uses these data to operate in a MUSTER mode (i.e. to implement a multi-platform signal fusion and navigation solution). At the same time, each receiver supplies other receivers in the network with its signal and measurement data. MUSTER signal processing methods are specifically designed to accommodate limitations of military and civilian data links. To support the functionality of the receiver network at the signal processing level (i.e., to enable multi-platform signal tracking and multi-platform phased arrays) while satisfying bandwidth limitations of existing data link standards, individual receivers exchange pre-correlated signal functions rather than exchanging raw GPS signal samples. Specifically, receivers broadcast portions of their pre-correlated signal images that are represented as a complex signal amplitude over the code/Doppler correlation space for 1-ms or 20-ms signal accumulation. For broadcasting, portions of signal images are selected around expected energy peaks whose locations are derived from some initial navigation and clock knowledge. This approach is scalable for the increased number of networked receivers and/or increased sampling rate of the ranging code (such as P(Y)-code vs. CA-code). The link bandwidth is accommodated by tightening the uncertainty in the location of the energy peak. As a result, the choice of the data link becomes a trade-off between the number of collaborative receivers and MUSTER cold-start capabilities (i.e., maximum initial uncertainties in the navigation and clock solution). The multi-platform tracking architecture has been developed to integrate signals from multiple independently operating GPS receivers (including independent clock operations) in order to improve the signal-to-noise ratio (SNR) and enable processing of weak GPS signals. The signal processing approach extends an open-loop tracking concept that has been previously researched for single receivers [2] to networked GPS receivers. Particularly, signals from multiple platforms are combined to construct a joint 3D signal image (signal energy vs. code phase and Doppler shift). Signal parameters (code phase, Doppler shift, carrier phase) are then estimated directly from this image and without employing tracking loops. The paper will present experimental results that show tracking of GNSS signals attenuated by tree canopy, buildings as well as indoor signal processing results. The main advantage of the multi-platform antenna arrays is that interference suppression capabilities are improved by incorporating additional users into the GPS receiver network and/or increasing a separation between them rather than increasing the number of elements (and as a result size, cost and power consumption) of individual antennas. A novel digital beam-forming (DBF) method has been established that does not require precise (cm-level) knowledge of inter-nose position and clock states. The method implements post-correlation beam-forming and searches through possible phase adjustments for I and Q values received from supplemental platforms. For each point in the search space, DBF weights are computed based on the given set of phased adjustments and pre-correlated signal covariance matrix. The phase adjustment combination that maximizes carrier-to-noise ratio for the signal peak in the open-loop GNSS image is chosen. The paper will present interference suppression results for a three-node array scenario with non-uniform array distribution and unsynchronized operation of individual node clocks. References: [1] A. Soloviev, J. Dickman, J. Campbell, “Collaborative GNSS Receiver Architecture for Weak Signal Processing,” Proceesing of ION International Technical Meeting, 2013. [2] F. van Graas, A. Soloviev, M. Uijt de Haag, S. Gunawardena, “Closed Loop Sequential Signal Processing and Open Loop Batch Processing Approaches for GNSS Receiver Design,” IEEE Journal of Selected Topics in Signal Processing, Vol. 3, Issue 4, August 2009.
Published in:	Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013) September 16 - 20, 2013 Nashville Convention Center, Nashville, Tennessee Nashville, TN
Pages:	135 - 143
Cite this article:	Soloviev, A., Dickman, J., "Collaborative GNSS Signal Processing," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 135-143.
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