This study addresses the problem of geolocating a strictly non-circular source on the surface of Earth by a cluster of passive satellites. The known satellite positions and velocities are subject to random errors. The authors propose a single-step satellite geolocation algorithm that directly localises the transmitter from sensor outputs using the information of time delays and Doppler shifts but without explicitly estimating them. It exploits the non-circular property of signals and a priori information of satellite orbit error distribution to jointly calibrate orbit errors and determine the longitude and latitude of the transmitter based on the ellipsoidal Earth model, which integrates an alternating iteration scheme for the estimation of various unknowns instead of the exhaustive grid search. Additionally, a detailed Cramér-Rao bound (CRB) derivation is presented for the single-step satellite geolocation of a non-circular source on Earth with and without satellite orbit perturbations, and it is proved that these CRBs are lower than the associated CRBs for a circular source. The simulation results illustrate that the proposed method asymptotically attains the associated CRB, and shows greater performance robustness to signal-to-noise ratio (SNR) and satellite orbit errors compared with conventional twostep satellite geolocation approaches.

This paper proposes a direct position determination (DPD) method for a digital modulation signal based on time difference of arrival (TDOA) measurements. Unlike the two-step positioning process, the measurements are used to directly estimate the source position. Fully utilizing information in a transmitted waveform can improve the accuracy of source localization in a single-step method. Based on maximum likelihood (ML) estimates, when the digital modulation scheme is known to the location system, an alternating iterative DPD method is developed to locate the emitter. The objective function of the proposed algorithm takes into account the source position and transmitted symbol sequence, which makes it a mixed-integer optimization problem. In particular, ML sequence estimation is adopted and the complex envelope is restored using a genetic algorithm. Then, based on Newton’s method, an alternating iterative algorithm is proposed to update the position and symbol sequence results. Compared with the existing DPD method, the proposed algorithm gives more accurate location results for unknown waveforms. In addition, the proposed algorithm can reach the Cramér-Rao bound (CRB) for known signal waveforms, as verified using comprehensive simulations.