IEEE - Aerospace and Electronic Systems - July 2022 - 6

I Am Not Afraid of the GPS Jammer: Resilient Navigation Via Signals of Opportunity in GPS-Denied Environments
Figure 1.
Overview ofa tightly coupled radio SLAM framework. The radio front-end collects signals, which are processed in the navigation receivers.
The EKF time update is performed based on the toggling switch: (i) using a dynamical model that describes the navigator's dynamics or (ii)
using an INS, when available. The EKF measurement update is performed using navigation observables from received SOP signals and
GNSS signals, when available.
model formulation; clock error dynamics model;
and SOP measurement model;
2) gives explicit description of the experiments including
filter initialization and software and hardware
setup;
3) provides further analyses and experimental results
of the C=N0 experienced during jamming as well as
the filter's estimation error.
The rest ofthis article is organized as follows. The section
" Radio Slam " provides and overview of the radio SLAM
framework. The section " GPS-Jammed Environment and
Experimental Setup " describes the GPS-jammed environment
and hardware and software setup. " PNTExperimental Results
in the GPS-Jammed Environment " presents PNT experimental
results. Finally, the " Conclusion " is presented.
RADIO SLAM
This section overviews radio SLAM framework as well as
the navigator's dynamics model, clock error dynamics,
and SOP measurement models.
FRAMEWORK OVERVIEW
Radio SLAM estimates the states of the navigatormounted
receiver simultaneously with the SOPs' states.
Radio SLAM produces an SOP-derived navigation solution
in a standalone fashion [26], [31] or an integrated
fashion by fusing SOPs with sensors (e.g., IMU, lidar,
etc.) and digital maps [32].
Observability of radio SLAM was analyzed in the
authors' work [26], [33], leading to establishing the minimal
a priori knowledge needed about the navigator-mounted
receiver's and/or SOP transmitters' states. The most significant
conclusion from these observability analyzes is that ifa
6
mobile navigator with knowledge of its initial states (position,
velocity, clock bias, and clock drift), denoted xr,
makes pseudorange measurements to M 1 terrestrial
SOPs whose states (position, clock bias, and clock drift),
denoted xs;i
M
i¼1 are unknown, then the environment is
fully observable, i.e., the navigator can estimate its states
simultaneously with the states ofthe SOPs. The conclusions
from these observability analyzes will be used in estimating
the mobile ground vehicle's and SOP's states in the
section " Experiment 2: Mobile Receiver. "
A simple, yet effective estimator that could be
employed in radio SLAM is the extended Kalman filter
(EKF). Here, one could employ a similar architecture to a
tightly coupled GNSS-INS. This architecture i) performs
the EKF measurement update (yielding the corrected estimate
GNSS
observables (e.g., pseudorange and carrier phase)
are available and ii) performs the EKF time update (yielding
the predicted estimate ^x and prediction error covariance
P) with raw IMU data between GNSS measurement
epochs. The added complexity with SOPs is that the EKF
state vector is composed of both the navigator's states and
the SOPs' states, i.e., x , xT
hiT
r ;xxT
s;1; .. . ;xxT
s;M
. If no INS
is used, then a proper dynamical model for the navigator
dynamics can be used for the EKF time update. Of course,
this would introduce a mismatch between the true navigator's
dynamics and the model used in the EKF; nevertheless,
advanced methods such as adaptive filters (e.g.,
interacting multiple models [34] and noise covariance estimation
[35] could alleviate this mismatch.
Figure 1 depicts a high-level block diagram of tightly
coupled radio SLAM, which operates in the following two
modes.
Mapping mode: GNSS observables are available.
Here, GNSS and SOP observables are fused in the
EKF to aid the INS (if available), producing a more
accurate estimate of xr
IEEE A&E SYSTEMS MAGAZINE
JULY 2022
x^þ and corrected error covariance Pþ) whenever
while mapping the SOP

IEEE - Aerospace and Electronic Systems - July 2022

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