A framework that could detect the presence of repetitive features in unknown signals is developed and experimentally demonstrated with non-terrestrial (NTN) sources, namely GPS and low Earth orbit (LEO) satellites. In modern communication systems, the existence of repetitive feature (i.e., the beacon) in the transmitted signals is inherited either implicitly; due to the transmitter’s source and channel coding, modulation, and multiplexing schemes; or explicitly, by broadcasting repetitive sequences for synchronization purposes. Upon detecting an unknown source, this framework, termed cognitive beacon detection and estimation, is capable of estimating the beacon, regardless of the adopted modulation scheme by the source. First, a model that is adequate to describe received signals from highly dynamic satellite channels is presented. The model parametrizes signals received from multiple unknown sources in terms of their time-varying carrier power, delay, Doppler, and user data. Second, a sequential beacon detection and estimation approach is developed, which is agnostic to the modulation scheme employed by the transmitter. The proposed cognitive framework allows for opportunistic exploitation of salient information embedded in the estimated beacon, such as beam, sector, and transmitter identification sequences. Third, the proposed framework is shown to converge almost surely to the correct beacon one at a time, until all sources are detected. Fourth, experimental results are presented demonstrating the efficacy of the developed cognitive framework in blindly detecting and estimating beacons of NTN sources, namely GPS and 5 LEO constellations: NOAA, Orbcomm, Iridium, Starlink, and OneWeb. These sources transmit in a diverse swath of modulation schemes: AM/FM,M-PSK, SC-FDMA, and OFDM. Finally, experimental results are presented demonstrating the applicability of the developed framework in navigating a ground vehicle with multi-constellation LEO satellite signals. The pseudorange rate measurements produced by the framework to 2 Orbcomm, 1 Iridium, 4 Starlink, and 1 OneWeb were fused with altimeter measurements via an extended Kalman filter to estimate the vehicle’s nearly 1 km trajectory, achieving a three-dimensional position root mean squared error of 4.15 m.

The problem of position estimation using range measurements from transmitters with inaccurately known positions is considered. The true position of each transmitter is assumed to lie within a disk of a known radius, centered at the inaccurate position. A robust estimation framework is proposed, formulating a min-max optimization problem and presenting a tractable solution approach. A sensitivity map is constructed to quantify the positioning error in different regions due to inaccuracies in the transmitters’ positions. Numerical simulations are presented demonstrating the construction and application of the sensitivity map for estimating the position of a mobile receiver. It is shown that the sensitivity map yields invaluable insights to the expected positioning error in various regions within the environment. Experimental results are presented of a vehicle navigating in a real-world GPS-jammed environment using pseudorange measurements from 7 cellular transmitters whose positions are inaccurately known. The vehicle’s positioning error is justified utilizing the offline-generated sensitivity map.

An opportunistic navigation receiver that exploits always-on and on-demand 5G downlink signals is presented. The semi-cognitive receiver operates in two stages: (i) acquisition, which utilizes the "always-on" signals to detect gNBs, and (ii) Kalman filter (KF)-based tracking, which continuously estimates the on-demand reference signals (RSs) to refine the receiver’s local replica. Experimental results show that the estimated replica effectively utilizes nearly the entire channel bandwidth, spans almost all orthogonal frequency division multiplexing (OFDM) symbols for longer integration time, and achieves higher processing gain, enhancing the carrier-to-noise ratio for reliable acquisition and tracking. An experiment with real 5G signals on a ground vehicle demonstrated a significant 62% reduction in position root-mean squared error (RMSE) compared to a conventional opportunistic navigation 5G receiver which only utilized always-on signals.

A localization upper-bound with range measurements to transmitters whose positions are uncertain is derived. The transmitters’ positions uncertainty is known to be bounded by a disk with a known radius ε. First, assuming that only one of the transmitters’ positions is uncertain (while the rest are perfectly known), an analytic solution is derived for the inaccurate transmitter position within the uncertainty disk, which maximizes the ranging error. This leads to establishing a localization error upper-bound. To demonstrate the applicability of the upper-bound to the case of multiple uncertain transmitter positions, numerical simulations are presented demonstrating that the derived upper-bound still holds. Experimental results are presented of a vehicle navigating in GPS-denied environment, making pseudorange measurements to 7 cellular transmitters, whose positions are uncertain. The experimental results demonstrate the applicability of the upper-bound in predicting the worst-case vehicle localization performance.