Figure 38.62 (a) Sky plot of GPS SVs: 14, 18, 21, 22, and 27 used for the 5 SV scenarios. For the 4 SV scenario, SVs 14, 21, 22, and 27 were used. The elevation mask, elsv, min, was set to 20° (dashed red circle). (b) Top: Cellular CDMA tower locations and receiver location. Bottom: Uncertainty ellipsoid (yellow) of navigation solution from using pseudoranges from five GPS SVs and uncertainty ellipsoid (blue) of navigation solution from using pseudoranges from five GPS SVs and three cellular CDMA towers. Map data: Google Earth (Morales et al. [7]).
Source: Reproduced with permission of Z. Kassas (International Technical Meeting Conference).
38.9 Cellular‐Aided INS
Traditional integrated navigation systems, particularly onboard vehicles, integrate GNSS receivers with an INS. When these systems are integrated, the long‐term stability of a GNSS navigation solution complements the short‐term accuracy of an INS. GNSS–INS fusion architectures with loosely coupled, tightly coupled, and deeply coupled estimators are well studied [87]. Regardless of the coupling type, the errors of a GNSS‐aided INS will diverge in the absence of GNSS signals, and the rate of divergence depends on the quality of the IMU. Cellular signals could be used in place of GNSS signals to aid an INS [44]. This section outlines how cellular signals could be used to aid an INS in the absence of GNSS signals. Additional details can be found in [4, 45, 88, 89].
This section is organized as follows. Section 38.9.1 discusses how to aid the INS with cellular signals in a radio SLAM fashion. Sections 38.9.2 and 38.9.3 present simulation and experimental results, respectively, of a UAV navigating in a radio SLAM fashion, while aiding its INS with ambient cellular signals.
Figure 38.63 Experimental results comparing the navigation solution uncertainty ellipsoids produced by (1) GPS alone and (2) GPS and cellular CDMA and LTE. Map data: Google Earth (Kassas et al. [6]).
Source: Reproduced with permission of IEEE.
38.9.1 Radio SLAM with Cellular Signals
To correct INS errors using cellular pseudoranges, an EKF framework similar to a traditional tightly coupled GNSS‐aided INS integration strategy can be adopted, with the added complexity that the cellular towers’ states (position and clock error states) are simultaneously estimated alongside the navigating vehicle’s states (position, velocity, attitude, IMU measurement error states, and receiver clock error states). This framework is composed of two modes:
Mapping ModeThe EKF produces estimates and associated estimation error covariances of both the navigating vehicle and the cellular towers’ states (augmented in ) using both GNSS SV and cellular pseudoranges. Between aiding corrections, the EKF produces the state prediction and prediction error covariance P− using the INS and receiver and cellular transmitter clocks models. When an aiding source is available, either GNSS SV or cellular pseudoranges, the EKF produces a state estimate update and associated estimation error covariance P+.
Radio SLAM ModeThe cellular‐aided INS framework enters a radio SLAM mode when GNSS pseudoranges become unavailable. In this mode, INS errors are corrected using cellular pseudoranges and the cellular transmitters’ state estimates that were last computed in the mapping mode. As the vehicle navigates, it continues to refine the cellular transmitters’ state estimates simultaneously with estimating the vehicle’s own states.
Figure 38.64 illustrates a high‐level diagram of the cellular‐aided INS framework.
38.9.2 Simulation Results
To demonstrate the performance of the cellular‐aided INS framework, simulations were conducted of a UAV equipped with cellular navigation receivers, navigating in downtown Los Angeles, California, while listening to ambient cellular signals. Two navigation systems were employed to estimate the trajectory of the UAV: (i) a traditional tightly coupled GPS‐aided INS with a tactical‐grade IMU and (ii) the cellular‐aided INS discussed in Section 38.9.1 with a consumer‐grade IMU. A simulator generated the true trajectory of the UAV and clock error states of the UAV‐mounted receiver, the cellular transmitters’ clock error states, noise‐corrupted IMU measurements of specific force and angular rates, and noise‐corrupted pseudoranges to multiple cellular towers and GPS SVs. The IMU signal generator used a triad gyroscope and a triad accelerometer model, each with time‐evolving biases that provided sampled data at 100 Hz. GPS L1 C/A pseudoranges were generated at 1 Hz using SV orbits produced from receiver‐independent exchange files downloaded on October 22, 2016, from a continuously operating reference station server [90]. The GPS L1 C/A pseudoranges were set to be available for only the first 100 s of the 200 s simulation. Cellular pseudoranges were generated at 5 Hz to four cellular towers,
