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.

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.

The fundamental tracking performance in radio navigation is characterized, leading to optimal receiver design considerations. First, a generalized beacon model is proposed and its sufficient, salient parameters are defined. Second, closed-form approximations of the delay, Doppler, Doppler stretch, and Doppler rate ambiguity functions (AFs) and a generalized coherent integration efficiency model are proposed. Third, design considerations and optimal coherent processing interval (CPI) length selection are presented, based on the complete navigation framework, entailing the: (i) beacon’s parameters, (ii) channel dynamics between the transmitter and receiver, (iii) employed clocks by the transmitter and receiver, (iv) search space adopted in the acquisition stage, and (v) dynamical model’s order employed in the tracking stage. Fourth, the acquisition and tracking stages of the navigation receiver architecture are discussed. Fifth, three sets of experimental results are presented validating the proposed closed-form approximation of the Doppler stretch and Doppler rate AFs and demonstrating the performance of a receiver tuned by the proposed design considerations in acquiring, tracking, and localization, namely: (i) aircraft tracking of terrestrial 4G signals, (ii) stationary receiver tracking of Starlink low Earth orbit (LEO) signals, and (iii) stationary receiver localization with Starlink and OneWeb LEO signals. For the Starlink and OneWeb receiver localization experiment, the receiver was capable of tracking the Doppler and carrier phase of 5 Starlink and 3 OneWeb LEO satellites. Starting from an initial estimate 50 km away from its true position, the receiver converged to a final two-dimensional (2D) position error of 30.3 m.

A blind Doppler spectral approach is proposed for exploiting unknown Starlink low Earth orbit (LEO) satellite signals for positioning. First, an analytical derivation of the received signal frequency spectrum is presented, which accounts for the highly dynamic channel between the LEO satellite and a ground-based receiver. Second, a frequency domain-based blind Doppler discriminator is proposed. Third, a Kalman filter (KF)-based Doppler tracking algorithm is developed. Finally, experimental results are presented of a stationary receiver tracking the Doppler, in a blind fashion, of six Starlink LEO satellites over a period of about 800 seconds with Hz-level accuracy. The Doppler measurements were fused through a nonlinear least-squares estimator to localize the receiver to an unprecedented level of accuracy. Starting with an initial estimate 200 km away, the proposed approach achieved a final horizontal two-dimensional (2D) position error of 4.3 m.