ADMM-based ℓ1−ℓ1 optimization algorithm for robust sparse channel estimation in OFDM systems

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

Off-Grid Sparse Bayesian Inference with Biased Total Grids for Dense Time Delay Estimation

A system for locating wireless transmitters employing an Orthogonal Frequency Division Multiplexing (OFDM) digital modulation scheme. The OFDM scheme comprises transmitting signal components over narrowband frequency channels spanning a wideband channel. The system includes a first receiving system configured to receive a fraction of the signal components transmitted by a first wireless transmitter to be located in a fraction of the narrowband frequency channels, and to process the fraction of the signal components to derive location related measurements. The system further includes at least a second receiving system configured to receive the fraction of the signal components transmitted by the first wireless transmitter, and to process this fraction of the signal components to derive location related measurements. The system also includes a processing system configured to use location related measurements from the first and second receiving systems to compute the location of the wireless transmitter.

Location of wideband ofdm transmitters with limited receiver bandwidth

/pdf/communication-and-ranging-composite-system-based-on-parallel-15vh4jni.pdf

Communication and ranging composite system based on parallel combinatory spread spectrum technology

Aiming at the problem of time delay estimation for orthogonal frequency division multiplexing (OFDM) signals with small samples, a new compressed sensing time delay estimation method based on atomic evolution and elimination is proposed. This method introduces the idea of evolution and elimination from Darwin's evolutionism, abandons the fixed and invariable way of grid points in the traditional algorithm, takes grid points as adjustable parameters, and updates grid points in each iteration process to promote the evolution of atoms. In order to deal with the off-grid effect, the exponential function is directly modified instead of linear approximation, which effectively eliminates the modeling error caused by the original off-grid algorithm. In the process of calculation, the atomic elimination mechanism is adopted, which reduces the amount of calculation and improves the speed of it. The simulation results show that the proposed algorithm can significantly improve the performance of time delay estimation, especially when using coarse grids. Based on the universal software radio peripheral (USRP), the validity of the proposed algorithm is verified by the actual signal.

/pdf/compressed-sensing-ofdm-time-delay-estimation-based-on-u7bzyuhb7p.pdf

Compressed Sensing OFDM Time Delay Estimation Based on Atomic Evolution and Elimination

This paper proposes a new method using orthogonal frequency division multiplexing (OFDM) signals for time-of-arrival (TOA) estimation in complex environments, such as electromagnetic interference and short burst received signals. Under that circumstance, sometimes only a short period of signals that were seriously interfered [i.e., low signal-to-noise ratio (SNR) and small samples] can be received for estimation. However, the traditional algorithm would be faced with a decrease in estimation accuracy under that conditions. Due to the fact that the sparse reconstruction method can recover the original signal from a small number of observations with high probability, this paper proposes an OFDM TOA estimation algorithm based on it. The algorithm not only considers the correlation of the channel complex fading coefficients in the samples at adjacent moments but also considers the off-grid effect. Under the Bayesian learning framework, this correlation and off-grid parameter are introduced into the estimation process, which effectively improves the estimation accuracy under small samples and low SNR conditions. The proposed algorithm first constructs a sparse representation model based on the channel frequency response estimated by the OFDM signal physical layer protocol data unit and then introduce the correlation characterization matrix and the off-grid parameter and make probability assumptions for the noise vector, the off-grid parameter, and the sparse coefficient vector in the model. Finally, according to Bayesian inference, the expectation-maximization algorithm is used to solve the hyperparameters to achieve high-resolution estimation of TOA. The simulation results show that the proposed algorithm has a better estimation performance than the traditional algorithms and existing sparse reconstruction algorithms. In addition, its performance curve is closer to the Cramer-Rao bound at low SNR. At the same time, based on the universal software radio peripheral, the effectiveness of the proposed algorithm is verified by the actual OFDM signal.

/pdf/high-resolution-time-of-arrival-estimation-with-small-ig3c2bz8t9.pdf

High-Resolution Time of Arrival Estimation With Small Samples Considering Time-Varying Channel Fading Coefficient Correlation in OFDM

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Compressed Sensing OFDM Time Delay Estimation Based on Atomic Evolution and Elimination

High-Resolution Time of Arrival Estimation With Small Samples Considering Time-Varying Channel Fading Coefficient Correlation in OFDM