# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 1 Introduction
This paper investigates using RISS to provide an efficient, reliable, yet less complex and inexpensive ultra-tight GPS/INS integrated solution. Augmenting GPS vector-tracking loops with the RISS, the proposed ultra-tight GPS/INS integrated solution, as discussed below, uses specific models that are different from its counterparts reported in literature. Furthermore, the performance of the introduced system is also tested using a unique GPS/INS setup that has been implemented on the side of this work. Figure 1: A Block Diagram of Scalar Tracking Systems. It is a fact that GPS is not the only operational GNSS anymore. Hence, multi-constellation and multi-frequency GNSS is one of the more realistic solutions to the problem discussed in this paper. However, there are number of applications that require relying on a single-constellation system. For example, for some strategic applications, there is an essential demand to explore solutions relying only on the GPS constellation and in most cases involving only one of the carrier frequencies. Some of the future civilian and rescue services applications require relying only on GPS and most of them either rely on L1 GPS or L5 GPS. This research addresses the issue of aiding single constellation (GPS) operating at one carrier frequency with other self-contained navigation systems, i.e., INS, rather than using multi-constellations. However, the concepts introduced in this paper can similarly be applied to other satellite-based navigation systems. Different forms of ultra-tightly coupled GPS/INS integration systems have been presented in literature using either different integration algorithms and/or sensors configuration. A Kalman filter-based design is proposed in [14] that combines the GPS and INS systems at the correlator output stage of the GPS receiver with the conventional code/carrier tracking loops merged in the integration Kalman filter. Research in [15] proposed a GPS/INS integrated based on what is called multi-channel cooperated tracking (COOP tracking). The INS and COOP loops operate together to provide a nearly perfect reference for receiver dynamics and receiver clock errors. Thus, the PLL/DLL loops track the differences between the incoming and the local signals, which have been compensated by INS and COOP loops. In [16] researchers propose a strategy of an ultra-tight GPS/INS integration system based on an unscented Kalman filter (UKF). The GPS/INS ultra-tight integration system exploit the position velocity and attitude which are obtained from INS to estimate the I/Q signal, then the I/Q error formed by INS estimation and GPS measurement can be obtained. Research in [10] introduced an ultra-tight integration system to fuse GPS and reduced IMU configuration consisting of two horizontal accelerometers, a single vertical gyroscope, and wheel speed sensors. The paper investigates the feasibility of the proposed method for the ultra-tight integration for land vehicle navigation applications. Research in [17] is extension of what is proposed in [10]. A major addition was the use of dual frequency receiver to overcome some limitations experienced by the legacy L1 C/A signals. A combination of L1 and L2C signals, available in modern receivers, was used to obtain improved observability and therefore better navigation performance. In [18], researchers applied an  
Figure 2: A Block Diagram of Vector Tracking Systems. adaptive unscented Kalman filter which prudently selects deterministic sigma points from Gaussian distributions and propagates these points through a nonlinear function. Besides, the Scaled Unscented Transformation (SUT) method was selected for the sigma point selection, which gave the ability to alter the spread of sigma points and control the higher order errors through some design parameters. An improved ultra-tightly GPS/INS integration approach is proposed in [19]. The method makes use of the neural network (NN) to improve the quality of the Doppler estimates from the Kalman filter. The algorithm is meant for seamless navigation during GPS outages.