Plug-and-play Information Fusion in an INS/GNSS/SoOps Integrated Navigation

X. Zhang, X. Zhan

Abstract:	Most current integrated navigation systems rely on a combination of the Global Navigation Satellite System (GNSS) and an Inertial Measurement Unit (IMU), and often one or more extra aiding sensors, to eliminate, or at least mitigate, the inevitable drift in single inertial navigation solution. Nonetheless, these systems are optimized with custom filter solutions for their specific sensors and measurement sources operating at different frequencies, leading to point solutions that are inflexible to new capabilities or mission challenges. The quality and robustness of the navigation solution can be significantly enhanced through the use of information extracted from different combinations of aiding sensors, such as, LiDARs, laser rangers, cameras, barometers and magnetometers. Signals of opportunity (SoOPs) including cellular, Wi-Fi, ASTC, DVB-T, DAB/ DMB and AM/FM transmissions from the RF background infrastructure and other natural phenomena have also proven useful, and new sensors and measurement sources are being explored constantly. Therefore, it is essential to develop new navigation filtering algorithms, abstraction methods, and an overall navigation system architecture, to accommodate any combination of a large and rapidly expanding array of sensors and measurements, in a plug-and-play fashion. Success in these objectives enables robust positioning and navigation in the face of new conditions and missions, while reducing the cost of bringing new sensors and new capabilities to the users[1]. It is in this regard that a new information fusion approach is explored for the aforementioned ASPN (All Source Positioning and Navigation) system. As a natural first step, the measurement sources are constrained to IMU, GPS and SoOps. This approach can be easily extended to fit for the stringent requirements of autonomous navigation for its flexibility, as described later. Whereas various approaches to navigation filtering are readily found, arguably the most common one is based on extended Kalman filter (EKF). In spite of the EKF’s ubiquity, incorporating measurements from varied sensors working at multiple frequencies is cumbersome. An augmented state is typically used to accommodate multi-rate measurements, and to better incorporate non-linear measurement models, other EKF versions such as the iterated EKF and unscented EKF, can be applied. Nevertheless, current techniques necessitate updating the whole augmented state vector once a measurement arrives, which can be expensive if the state vector is large. In practice, this is not always necessary, since some of the state variables remain unchanged in certain conditions. An alternative is to maintain a buffer of past navigation solutions. However, such an approach produces only an approximated solution. In this paper a factor graph approach is applied to process all available measurements into a navigation solution. A factor graph [2] is a standard bipartite graphical representation of a mathematical relation—in this case, the “is an argument of” relation between variables and local functions. It has been applied in Bayesian inference, enabling efficient computation of marginal distributions through the sum-product algorithm. One success story of factor graphs is the decoding of capacity-approaching error-correcting codes, such as LDPC [3]. A factor graph is represented by a bipartite graph comprising of variable and factor nodes. Variable nodes are associated with system states, and factors are associated with measurements, and the factor graph encodes the posterior probability of the states over time, given all available measurements [4]. Using factor graphs allows handling different sensors at varying frequencies in a simple and intuitive manner. The factor graph scheme also provides plug-and-play capability, since measurement updates are just additional sources of factors added to the graph and vice versa; no special procedure or coordination is required. Section two briefs the fundamentals of incremental smoothing. Computing the full navigation solution over all available states can be performed effectively using the relatively new incremental smoothing techniques[5][6], which optimizes a small fraction of the nodes in the obtained graph, rendering the proposed algorithm suitable for high output frequency applications. Section three introduces the factor graph formulation and presents factors for some of the common sensors in navigation applications. Take for example, IMU, SoOps, LIDARs, etc. In this regard, the proposed factor graph/incremental smoothing algorithm is suitable for information fusion in ASPN (All Source Positioning System). The incremental non-linear optimization is then discussed in section four. Simulation results demonstrating the proposed approach are provided in Section five in which incremental smoothing approach is compared to the conventional EKF approach. Section six concludes the paper. [1] Broad Agency Announcement All Source Positioning and Navigation (ASPN) Phase 2, STRATEGIC TECHNOLOGY OFFICE, DARPA-BAA-12-45. [2] F.R. Kschischang, B.J. Frey, and H-A. Loeliger. Factor graphs and the sum-product algorithm. IEEE Trans. Inform. Theory, 47(2), February 2001. [3] R. G. Gallager, Low-Density Parity-Check Codes. Cambridge, MA: MIT Press, 1963. [4] Vadim Indelman et al. Factor graph based incremental smoothing in inertial navigation system. [5] Kaess, M. iSAM: Incremental smoothing and mapping. IEEE Trans. Robotics, 24(6), December 2008. [6] Kaess, M. iSAM2: Incremental smoothing and mapping with fluid relinearization and incremental variable reordering. IEEE International Conference on Robotics and Automation (ICRA 2011), May 2011.
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:	1970 - 1976
Cite this article:	Zhang, X., Zhan, X., "Plug-and-play Information Fusion in an INS/GNSS/SoOps Integrated Navigation," Proceedings of the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2013), Nashville, TN, September 2013, pp. 1970-1976.
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