The time difference of arrival (TDOA) estimation plays a crucial role in emitter localization and time synchronization applications. When time among multiple sensors is synchronized, the TDOAs between the sensors and the emitter can be measured to achieve hyperbolic positioning of the emitter. Conversely, if the positions of both the sensors and the emitter are known, TDOAs can be utilized to synchronize the clocks across the sensors. Given that compressed sensing (CS) can reduce both the sampling rate and data volume, thereby enhancing the efficiency of TDOA estimation, there has been growing interest among researchers in exploring TDOA estimation within the CS framework. In scenarios such as passive positioning, the signals received by sensors are often non-cooperative, and the underlying signal system is unknown, making it difficult to obtain a sparse representation of the signal. This paper introduces an incomplete reconstruction-based TDOA estimation method along with an improved variant. By selecting a partial Fourier transform matrix as the measurement matrix and a Fourier transform matrix as the projection matrix, the orthogonal matching pursuit (OMP) algorithm is employed to reconstruct the compressed measurement data. Through subsequent processing steps, such as conjugate mirroring, TDOA estimation between two signals can be performed. Although the reconstructed signal may substantially differ from the original, the accuracy of TDOA estimation remains reliable. Simulation results demonstrate that when the signal-to-noise ratio (SNR) of the received signal is at least 0 dB and the compressed sampling length exceeds one-tenth of the original signal length, the TDOA estimation error of the proposed method is nearly identical to that of the cross-correlation method.

The timing accuracy of eLoran systems is susceptible to meteorological fluctuations, with medium-to-long-range propagation delay variations reaching hundreds of nanoseconds to microseconds. While conventional models have been widely adopted for short-range delay prediction, they fail to accurately characterize the coupled effects of multiple factors in long-range scenarios. This study theoretically examines the influence mechanisms of temperature, humidity, and atmospheric pressure on signal propagation delays, proposing a hybrid prediction model integrating meteorological data with a back-propagation neural network (BPNN) through path-weighted Pearson correlation coefficient analysis. Long-term observational data from multiple differential reference stations and meteorological stations reveal that short-term delay fluctuations strongly correlate with localized instantaneous humidity variations, whereas long-term trends are governed by cumulative temperature–humidity effects in regional environments. A multi-tier neural network architecture was developed, incorporating spatial analysis of propagation distance impacts on model accuracy. Experimental results demonstrate enhanced prediction stability in long-range scenarios. The proposed model provides an innovative tool for eLoran system delay correction, while establishing an interdisciplinary framework that bridges meteorological parameters with signal propagation characteristics. This methodology offers new perspectives for reliable timing solutions in global navigation satellite system (GNSS)-denied environments and advances our understanding of meteorological–electromagnetic wave interactions.

To enhance the denoising performance of satellite signals at a low signal-to-noise ratio (SNR), we propose an improved wavelet threshold denoising correlation (IWTDC) method that utilizes wavelet decomposition to analyze the received satellite signals. By dynamically selecting the threshold based on the transformation of the wavelet coefficients of each layer, we reconstruct the signal using processed coefficients for effective denoising. To validate the denoising effect of the proposed IWTDC method, we compare it with methods such as traditional cross-correlation, adaptive filtering correlation, and wavelet threshold correlation. Simulation results indicate that compared to other methods, SNR can be improved by up to 5 dB using the IWTDC method, leading to superior signal correlation and enhanced detection performance for the related peaks. Furthermore, experimental data collected during the study were consistent with the simulation results, confirming the performance of the IWTDC method. Thus, our proposed method can overcome the limitations of using traditional threshold functions through adaptive threshold selection, preserve signal details, and significantly improve the denoising performance.

Reliable performance monitoring is indispensable for FM broadcast time service systems, yet systematic evaluation and long-term operational data in practical scenarios remain scarce. This study addresses the gap and verifies the actual service capability of the FM broadcast time service system deployed in 10 key Chinese cities. We established a systematic monitoring model, applied the GUM to evaluate measurement uncertainty, and conducted continuous, multi-site monitoring and statistical analysis over 24 months. Results show the expanded measurement uncertainty of all stations ranges from 62.768 μs to 80.646 μs (k = 2), meeting the 100 μs requirement, and long-term monitoring confirms the system achieves sub-millisecond timing accuracy in practical operation. This work fills the gap in long-term operational data for FM broadcast timing technology, provides a standardized uncertainty evaluation method for the monitoring system, and lays a robust theoretical and data foundation for the technology’s optimization and wide adoption, thereby enhancing user confidence in FM broadcast time services.

Time Difference of Arrival (TDOA) estimation is a pivotal technique with extensive applications across various domains, including passive detection and indoor positioning. For signals characterized by unknown modulation types and originating from non-cooperative sources, achieving high-precision TDOA estimation traditionally necessitates a substantial volume of sampling data. Conventional approaches, such as cross-correlation, require high sampling rates and extended durations, posing significant challenges in terms of data acquisition, transmission, and storage. To surmount these obstacles, this paper delves into an enhanced inexact reconstruction-based compressed sensing method for TDOA estimation (EIRCS), presents an optimized algorithmic procedure that further reduces the computational complexity of the EIRCS method. Experimental results substantiate that the EIRCS method is an unbiased estimation technique. It accomplishes high-precision TDOA estimation concurrent with efficient data compression. These insights suggest that the EIRCS method can yield dependable TDOA estimates with minimal error, even at elevated compression ratios. Its capacity to sustain high accuracy across a range of compression ratios renders it particularly apt for applications demanding efficient data processing and reliable TDOA estimation.