The estimation accuracy of conventional parameter estimation methods, including the maximum likelihood (ML) estimator and subspace-based estimation methods, diverges from the Cramer–Rao lower bound (CRLB) under low- signal-to-noise ratio (SNR) conditions. Conventional stochastic resonance (SR) technique has shown appealing weak signal improvement advantages under low SNR, but it still needs a priori information, such as the probability density functions (pdfs) of weak signal and channel noise. In this study, to address the channel parameter estimation for weak signal conditions, a novel channel parameter estimation algorithm based on dynamic stochastic resonance networks (SRNs) is introduced. Since the signal statistical properties are altered by the SRN processing, the CRLB of the wireless channel parameter estimation employing the SRN-enhanced signal is derived, and then the corresponding ML estimator is presented. Theoretical analyses show that the CRLB is lower than those from the original signal and SR-enhanced signal. Computer simulations are performed to verify the effectiveness of the theoretical CRLB expressions. Both simulation and real experimental results indicate that the proposed SRN processing approach outperforms the conventional SR processing through achieving the CRLB improvement, the ML estimation performance enhancement under low- SNR conditions, and the hardware complexity reduction.

GNSS signals are easily blocked or degraded because of the dense presence of high-rise buildings in urban areas, and positioning errors arising from reflected signals amount to as much as hundreds of meters. Various conventional GNSS techniques have been utilized to resolve this problem, but applying them to urban environments has been difficult owing to the complexity of the reflected signals and their unpredictable and nonlinear variation to the signal receiving environments. In this study, multipath maps were generated for dynamic users at multiple positions on a road and a residual-based map selection algorithm was implemented to solve the problem of user position uncertainty in deep urban environments. GNSS data collected over a period of 327 min were used to train the multipath maps corresponding to 247 points near the 2.5 km stretch of the Teheran-ro road in Seoul, South Korea. The proposed system performed efficiently—it was verified to be capable of constructing a multipath map with a radius of 25 m using only 4 min of data. Moreover, it improved positioning accuracy by 45 % horizontally and by 80 % vertically, enabling the determination of the positional information of an urban vehicle with a horizontal accuracy of 18 m during 99% of the duration of a one-hour-long dynamic test. Due to the nonreliance of the proposed method on prior information or implementation of additional sensors, it is expected to be widely used for constructing map-based multipath mitigation models as part of intelligent transportation infrastructure in all cities in future.

Long-term evolution (LTE) signals are potential signals-of-opportunity for position and navigation, especially in challenging urban and indoor environments. A major challenge is that the LTE signal time-of-arrival (TOA) estimations are susceptible to the multipath propagation effects. In this paper, the multipath estimating delay lock loop (MEDLL), which is originally designed for global positioning system receivers, is applied to LTE signal TOA estimation in multipath environments. We derive the analytical expression of the correlation function for LTE signals and present the procedure for estimating parameters of the detected multipath components. Two initialization methods without and with super-resolution algorithm (SRA) are developed for the MEDLL. Our analyses show that the MEDLL with SRA-based initialization can achieve better multipath resolution, while the one without SRA has less complexity. Extensive simulations involving static multipath scenarios are conducted to examine the statistical TOA estimation performance of the proposed MEDLL with LTE cell-specific reference signal. The simulation results and computational complexity analysis indicate that the proposed MEDLL outperforms the conventional delay lock loop and SRA in term of multipath mitigation performance and computational complexity. Experimental results using real collected LTE signals in urban environments are also provided to demonstrate the effectiveness of the proposed technique for realistic scenarios.