A novel maximum likelihood estimation model for time delay is constructed to estimate the passive time delay; the signal of emitter is unknown in this model. According to the model characteristics, the fast Fourier transformation(FFT)method is used to achieve time delay estimation(TDE). In order to improve the accuracy of estimator, the Markov Chain Monte Carlo(MCMC) sampling method is used to estimate the time delay value directly. Unlike traditional algorithms,MCMC method can obtain time delay without peak detection. Furthermore, the Cramer-Rao lower bound(CRLB)of this model is derived. Finally, simulations show that the proposed approach is suitable for both narrowband and broadband TDE, and the MCMC algorithm can achieve more precise time delay value with the same sample, and it has lower computational complexity than IS algorithm. The novel approach can estimate also the time delay of non-integer multiple of the sampling interval.

Time delay estimation using Markov Chain Monte Carlo method

When the Root-MUSIC algorithm (Root-Multiple Signal Classification) is used to estimate time delay in the Orthogonal Frequency Division Multiplexing (OFDM) system, there exists computational redundancy because of the required roots being conjugating symmetry. Aiming at this problem, a Root-MUSIC algorithm based on spectral factorization—SF-Root-MUSIC algorithm is proposed. Based on the structural characteristics of Laurent polynomials, the proposed algorithm uses spectral factorization to reduce the order for the root polynomial by half thus reducing the computational complexity. The simulation results show that the proposed algorithm has lower computational complexity while maintaining the same precision, and is more suitable for real-time computing.

A low-complexity time delay estimation algorithm based on spectral factorization in OFDM system

Precise parameter estimation is essential for developing effective channel models. However, jointly estimating the various channel parameters is a challenging problem as these parameters can only be extracted from comprehensive data collected during massive measurement activities. Traditional algorithms suffer from limited estimation accuracy due to high mobility and signal correlation. In this paper, we propose a low-complexity full-dimension parameter extraction algorithm. The method extends the forward-backward spatial smoothing technique to estimate two-dimensional (2-D) angle information in the uniform planar array (UPA), enhancing aperture size and providing higher resolution for coherent signals. Then, we provide a Doppler compensation technique to prevent the deterioration in complex amplitude estimation quality caused by large Doppler frequency in high-speed mobile environments. Moreover, the proposed algorithm is also applied to the fifth-generation new radio (5G-NR) system. To remedy power leakage in the channel impulse response (CIR), we propose an eigenvalue-assisted sampling point selection scheme. This scheme preserves sampling positions abundant in channel information to reduce computational complexity and eliminate most noise. Simulation and field tests show that the proposed algorithm achieves better performance with lower computational complexity compared to the existing algorithm.

Low-Complexity Full-Dimensional Wireless Channel Parameter Extraction Algorithm and Its Application in 5G-NR Systems

Joint estimation of delay and angle in multipath environments is a key link in wireless positioning. A joint delay and angle estimation at low complexity based on propagator method of the extended subspace is presented. The propagator method is used to estimate the extended noise subspace of the received signal, and the parameters are automatically paired. The algorithm not only effectively reduces the computational complexity, but also accomplishes the high precision estimation when the number of paths exceeds the number of antennas. The validity and feasibility of the proposed algorithm are verified by simulation.

Joint Delay and Angle Estimation Algorithm with Low-Complexity in Multipath Environments

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Joint Delay and Angle Estimation Algorithm with Low-Complexity in Multipath Environments