Yuzhou Li

Precisely estimating the propagation time error induced by complex terrains and meteorological factors during the propagation of the signal along the ground, also termed the additional secondary phase factor (ASF), is crucial to improve positioning accuracy in the enhanced long-range navigation (eLoran) system. Among all of the ASF estimation algorithms, the well-known kriging-based algorithms, a kind of methods that estimate the ASF only with limited ASF data, often suffer performance reduction, as the fixed basis functions adopted lack adaptability. In this article, we propose a trend kriging algorithm, which dynamically selects basis functions to construct the optimal trend function, for estimating the ASF under limited ASF data. Specifically, we design an optimization framework that integrates the selection task of basis functions and the search task of the correlation parameter into a single problem for jointly handling these nested tasks. We then develop an objective function based on the weighted covariance model (WCO) to ensure that the detrended data follows the spatial correlation feature. To tackle this problem with multiple local optima, we employ the niching differential evolution (NDE) algorithm, a recently developed metaheuristic algorithm. The performance verification results based on the simulated and practical ASF data cautiously exhibit the superiority of our proposed algorithm. For example, based on the practical data, compared with the representative inverse distance weighting (IDW), the ordinary kriging (OK), the first-order universal kriging (UK 1), and the second-order UK (UK 2), our proposed algorithm reduces the root-mean-square error by 21.68%, 29.26%, 10.53%, and 23.45%, respectively.

It is crucial to recognize the sky and ground waves, i.e., to determine the delays of the waves, in enhanced long-range navigation (eLoran) systems. However, the performance of most existing algorithms sharply decreases when the delay differences $\Delta \tau$ between the sky and ground waves are small or the signal-to-noise ratios (SNRs) are low. In this paper, after transforming the originally time-domain sky-ground waves recognition problem to the frequency domain, we interestingly find that it can be effectively addressed by the deterministic maximum likelihood (DML). Thus, we design a DML-based sky-ground waves recognition algorithm. The verification results based on both the simulated and the realistic data show that the DML outperforms the four representative algorithms, including the IFFT, the FOC-MUSIC, the WRELAX, and the LM, in terms of the success rate in recognizing the groundwave, particularly in situations with small $\Delta \tau$ or low SNRs. Numerically, compared to the IFFT, the LM, and the FOC-MUSIC, the success rate achieved by the DML is improved by 67.11%, 66.67%, and 64.19% respectively, and the WRELAX fails to work, in a challenging situation of SNR $=0$ dB and $\Delta \tau =37\ \mu s$ .

Multiple-input multiple-output orthogonal time frequency space (MIMO-OTFS) channels exhibit unique sparse properties in the delay- Doppler-angle (DDA) dimension. In this paper, we are dedicated to exploring how to utilize these special sparse properties to design an efficient channel estimation algorithm to balance the computational cost and estimation accuracy. To this end, we propose an iterative 3D efficient subspace pursuit (3D-ESP) channel estimation algorithm by exploiting these unique properties. Firstly, we invent a novel subspace pursuit technique to efficiently locate channel supports by detecting multiple dominant paths per iteration. Secondly, we adopt least squares twice in each iteration to optimize the estimation accuracy. Thirdly, we propose a noise-adaptive criterion to filter the estimated channels to promote the estimation accuracy in high-level noise cases. The performance verification results show that, compared to the classical 3D-SOMP, the 3D-ESP reduces the runtime by a range of 17.19% to 45.15%, and meanwhile decreases the normalized mean square error (NMSE) by 12.29% to 21.82%, when the signal-to-noise ratio (SNR) is 0 dB and the antenna number goes from 1 to 20.

Inter-carrier interference (ICI) and noise mitigation is crucial for precise signal detection in underwater acoustic (UWA) differential orthogonal frequency division multiplexing (DOFDM) communication systems. In this paper, we adopt the Transformer to design a detector, referred to as the TransDetector, which can dramatically mitigate ICI implicitly and noise explicitly, even without requiring any pilot. Compared with the standard Transformer, we come up with three creative designs. Firstly, we break the inner-encoder computation paradigm of the multi-head attention (MHA) in the standard Transformer, and design a brand new inter-encoder attention mechanism, referred to as the interactive MHA, which can significantly improve the performance, as well as accelerate the convergence rate. Secondly, to reduce the noise component attached to the received signal, we design an auto-perception denoising structure, which allows the network to learn the noise distribution in received signals. Thirdly, to better match the characteristics of DOFDM signals and selectively focus on the data at specified locations, we propose a trapezoidal positional encoding (PE), instead of adopting the original sine-cosine PE in the Transformer. The performance verification results across a wide range of signal-to-noise ratios (SNRs) and Doppler shifts exhibit that, the TransDetector outperforms the classical χ-FFT algorithms and the DNNDetector in both the the simulation channels and the realistic underwater channels. For example, based on the simulation channel, the bit error rate (BER) achieved by the TransDetector is reduced by 31.22% and 5.01% when SNR = 0 dB and by 48.21% and 42.01% when SNR = 20 dB against the PS-FFT and the DNNDetector, respectively, while the reduction increases from 8.01% and 9.88% to 44.22% and 18.23%, when the Doppler factor goes from 1 × 10 ^-4 to 3 × 10 ^-4 .

A fundamentally difficult problem to apply the orthogonal matching pursuit (OMP) to estimate the underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) channel is designing a proper iteration termination condition. However, existing OMP-based algorithms that design termination conditions based on the physical sparsity suffer the limited estimation accuracy in high-level noise cases and the difficult balance between the accuracy and computational costs in low-level noise cases. We find that it is the recovery sparsity, a quantity related to but possibly significantly different from the physical sparsity, that should be utilized to devise termination conditions. Based on this observation, we first exploit the recovery sparsity to derive a closed-form expression for the termination condition to improve the estimation accuracy in high-level noise cases. Then, to balance the estimation accuracy and computational costs in low-level noise cases, we design another termination condition based on the residual vector and the observation vector. By embedding these two conditions into the OMP structure, we propose an innovative channel estimation algorithm for UWA-OFDM communication systems, referred to as the adaptive OMP (A-OMP). Simulation results show that, compared with the OMP, the A-OMP can achieve comparable or even higher estimation accuracy with smaller computational costs in all considered noise cases. Specially, the A-OMP can increase the estimation accuracy by 71.31% with only 13.48% CPU running time in high-level noise cases.

In order to improve the accuracy of traffic flow data, this paper proposed a fusion method between floating car data and section detection data. Firstly, multi-scenario was discussed according to different data sources: GPS floating car system coexists with microwave radar detector, GPS floating car system coexists with ultrasonic detector, GPS floating car system coexists with travel time detector and induction Coil detectors, and only a single GPS floating car system. Second, three types of fusion models were put forward based on linear combination method, and then the model parameters were separately analyzed. Finally, the empirical experiments were verified the model validity and reliability.