Analysis and estimation of wave-induced Doppler shift from low-incidence-angle RAR based on sea state parameters

Empirical investigations show that at low and moderate signal‐to‐noise ratios, maximum entropy (ME) Doppler shift and spectral width estimates of VHF radar signals have significantly higher accuracies than conventional periodogram estimates with noise thresholding. The variances of the ME estimates decrease with decreasing spectral width and clearly indicate a limiting signal‐to‐noise ratio below which the Doppler shift estimates are dominated by cosmic and instrumental noise rather than fluctuating radar signals. Two criteria are derived empirically that yield estimates of the optimum ME prediction error filter lengths for computing the Doppler shift and spectral width of individual radar signals. At small signal‐to‐noise ratios the Doppler shift criterion produces variances that are close to the minimum variance bounds of spectral methods. Fast ME algorithms for computing signal power, Doppler shift, and spectral width are described. At large signal‐to‐noise ratios the ME Doppler shift estimator is faster than the corresponding periodogram estimator based on a fast Fourier tranform, whereas at low signal‐to‐noise ratios, it is slower. For computing a typical height profile of the mean radial velocity in the troposphere and lower stratosphere, the ME estimator is as fast as the periodogram estimator, whereas for a height profile of the mean spectral width, it needs approximately 3 times as much computation time as the periodogram estimator.

We present a new digital direct-sequence (DS) re- ceiver with joint estimation of code delay, multipath gains and Doppler shift. A parameter estimator consisting of a parallel bank of extended Kalman filters (EKF's) extracts estimates of the timing, T and the multipath coefficients, fl distorting the received signal. A detected estimate of the Doppler shift, UT distorting the received signal is also provided by the estimator. We compute the bit error rate that results when a RAKE matched filter uses the estimated parameters to detect the DPSK encoded binary data in the received signal. The bit-error rate (BER) is evaluated, and successful performance of the proposed receiver in the presence of Doppler shift distortion is observed in many cases. We demonstrate that the receiver can operate when the multipath coefficients vary in time (Doppler spread). N this paper we apply a RAKE receiver to the problem of I spread-spectrum communication in multipath. The reader is referred to (l) for an introduction and survey of spread spectrum RAKE type receivers in multipath. For the theory of extended Kalman filtering and adaptive parameter estimation the reader is referred to 131. In order for a spread-spectrum receiver to demodulate data, the timing offset between the receiver generated reference signal and the transmitted signal must be accurately tracked. Any Doppler shift distortion present on the signal must also be taken into account. Here we will assume a pre-acquired coarse timing estimate. The remaining problem is thus to acquire coarse estimates of the Doppler shift, and to track the code delay, timing and complex-valued multipath coefficients. The problem of tracking timing and multipath coefficients jointly was first considered in 141 and solved using an EKF algorithm. The EKF in (5) tracked Doppler by appending the Doppler parameter to the state vector, and was thus subject to filter divergence. In light of the divergence problems encountered in (5), we seek an alternative estimator which can both acquire and track Doppler shift. We will apply the general technique of partitioning de- scribed in (6), and (3) to estimate w,. The motivation for this approach is the poor observability of the Doppler shift param- eter, which can lead to filter divergence in an EKF algorithm. In summary, a bank of Kalman filters; each conditioned on a particular value, v,, i of the unknown Doppler velocity, v, can

The urbanization and the fourth industrial revolution lead to the explosion of smart cities. One of the most prior problems to solve in digital cities is transportation infrastructures and high speed railway (HSR) is an effective solution. In communication aspect of HSR, the passenger's demand and transportation management networks require high-speed data services with reliable connections. Orthogonal frequency division multiplexing (OFDM) is a modulation method in the advanced communication systems to provide broadband communications services. OFDM is substantial against inter-symbol-interference due to long symbol duration, but it is very sensitive to doppler effect that happens when the speed of the train is getting much faster. In addition, inter-carrier interference (ICI) caused by high doppler frequency shift has a severe impact on OFDM in case of high channel variations. In this paper, we propose an ICI mitigation method by utilizing the estimation and pre-compensation of high doppler shifts in HSR communication systems for smart cities. The estimate of the doppler shift is based on a preamble frame of data in communication link between EnodeB and user equipment. The simulation results show that the performance of system has been improved using the proposed model.

OFDM is substantial against Inter-symbol-interference due to long symbol duration. However, inter-carrier interference (ICI) caused by high Doppler frequency shift has a severe impact on OFDM in case of high channel variations. In this paper, we propose an ICI mitigation method by utilizing the estimation and pre-compensation of high Doppler shifts in high-speed railway communication systems for smart cities. The estimate of the Doppler shift is based on a preamble frame of data in communication link between EnodeB and user equipment. The simulation results show that the performance of system has been improved using the proposed model.

This paper investigates joint maximum likelihood (ML) Doppler shift and channel estimation problem for high speed railway (HSR) wireless communication systems with applying massive uniform linear array (ULA). For characterizing the performance of the considered scenarios, we provide a tractable framework for analyzing the performance of on Doppler shift estimation. Furthermore, the analytical expressions of ML channel estimation are derived, under the joint consideration of Doppler shift. Both analysis match their corresponding Cramer-Rao Bounds (CRBs) well. Finally, simulation results are provided to corroborate our proposed studies.

Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.

The estimation of time delay, Doppler shift, amplitude, and phase is an important fundamental tool in signal processing, which has received extensive study for cases with known transmitted signals, but little study for unknown transmitted signals. We derive the closed-form Cramér-Rao bound (CRB) expressions for joint or separate estimation of time delay, Doppler shift, amplitude, and phase with unknown signals with possibly known structure and possible multiple looks at direct path and reflected path observations. The presented results generalize previous results for known transmitted signals and show how many looks from the direct path and the reflected path we need to derive an accurate estimation of time delay, Doppler shift, amplitude, and phase. The advantages of the known signal format with unknown parameters over totally unknown signals are illustrated. After analysis under a simple white clutter-plus-noise model, extensions to the case with dependent clutter plus noise are discussed. Numerical results show very similar behavior. Numerical calculations of the mean square error from maximum likelihood estimation are provided to support the utility of the unknown signal CRB and the known signal format CRB.

A joint Doppler shift and channel estimation method for the millimeter-wave communication system of an unmanned aerial vehicle (UAV) equipped with a large-scale uniform linear antenna (ULA) array has been proposed. Since Doppler shift induces intercarrier interference, the parameters of the channel paths have been decomposed into the Doppler shift and the channel information. In order to obtain the Doppler shift, a new estimation algorithm based on a combination of discrete Fourier transform and phase rotation has been proposed, which can determine the appropriate number of antennas. In addition to estimating the channel information, a low-complexity joint Doppler shift and channel estimation method has been designed that can quickly obtain accurate estimates. Furthermore, the achievable sum rate, the theoretical bounds of the mean squared errors, and the Cramér-Rao lower bounds of the estimation method have been derived. The analysis and simulation results prove that the performance of the proposed approach is close to the theoretical inference.

The standard approach for joint estimation of time delay and Doppler shift of a signal is to estimate the point at which the cross ambiguity function of the original and modified signals attains its maximum modulus. Since band-limited signals can be expressed exactly by their Shannon series, we here consider approximated signals gained by truncating their Shannon series to involve only the sampled signal values. We then estimate the time delay and Doppler shift by calculating a point at which the cross ambiguity function of the approximated signals attains its maximum modulus. This cross ambiguity function has an analytic expression which allows its evaluation at any point, and we may apply Newton's method to calculate accurately and efficiently a point where the maximum modulus is attained. In the numerical experiments we conducted, our method generally outperformed other methods for estimation of both time delay and Doppler shift.

In multiple-input multiple-output (MIMO) radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, maximum-likelihood (ML) estimation yields the best performance. For this problem, the ML estimation requires the joint estimation of spatial location and Doppler shift, which is a two dimensional search problem. Therefore, the computational complexity of ML estimation is prohibitively high. In this work, to estimate the parameters of a target, a reduced complexity optimum performance algorithm is proposed, which allow two dimensional fast Fourier transform to jointly estimate the spatial location and Doppler shift. To asses the performances of the proposed estimators, the Cramer-Rao-lower-bound (CRLB) is derived. Simulation results show that the mean square estimation error of the proposed estimators achieve the CRLB.

The problem of joint estimation of time delay and Doppler shift is considered from the point of view of the Wigner distribution of the signal. A very efficient method of obtaining the optimum signal with minimum estimation error based on the convexity of the design region is developed. Practical applications, however, require the signal to satisfy other constraints which present complications in acquiring the optimum signal. A design approach based on the method of simulated annealing is suggested to solve for the optimum signal under constraints. The performance of the signals so obtained is evaluated and compared with that of signals obtained by synthesis.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The article presents a study investigating the level of variation in sea state parameters encountered by sailing ships crossing the oceans. The sea state parameters have been obtained from a reanalysis, in this case the ERA5. The study is based on the use of different interpolation schemes to compute parameters in geographical positions off the fixed grid. It is shown that the variation in sea state parameters can be significant. Consequently, in case of sailing ships, covering relatively long distances in a short time (30–60 min), it is recommended to rely on bilinear interpolation rather than nearest neighbour. The variation in the sea state parameters is, in fact, at a level which means that the normal assumption of a stationary seaway in periods up to 3 h likely is violated for ships sailing the typical service speed (15–20+ kt).

Due to the high mobility of high-speed trains (HSTs), Doppler shift estimation has been a big challenge for HSTs. In this paper, we consider an orthogonal frequency-division multiplexing (OFDM) system based on the long-term evolution (LTE) railway standard and design the novel Doppler shift estimation algorithm. By exploiting features of HSTs, i.e., regular and repetitive routes and timetables, resulting in a predictable Doppler shift curve, a radio environment map (REM) including the Doppler shift information can be constructed via field tests. Based on REM, a maximum a posteriori estimator (MAPE) is proposed to provide an accurate estimation of Doppler shift. It uses the estimation from REM (REME) as a priori knowledge and exploits the cyclic prefix (CP) structure of OFDM to provide a maximum a posteriori estimation. The Cramer–Rao lower bounds (CRLBs) are derived. The performance of MAPE is evaluated via simulations and compared to that of REME, the classical CP-based estimator, and other existing methods. It is shown that MAPE significantly outperforms the existing methods in terms of both estimation mean square error (MSE) and bit error rate.

In order to solve the problem of doppler shift caused by high-speed movement of maglev train and high carrier frequency of train-ground wireless communication system, this paper studies the Doppler shift estimation algorithm of SC-FDE system and proposes a two-stage estimation algorithm based on the excellent correlation characteristics of gray sequence. Firstly, the coarse estimation and compensation of doppler shift are carried out by using the gray sequence in the preamble training sequence. Then the fine estimation and compensation of Doppler shift are carried out by using the gray sequence inserted in the data block. Simulation analysis and experimental verification are carried out. The results show that the proposed algorithm can significantly improve the throughput performance of train-ground wireless communication system in high-speed mobile environment. The algorithm has high estimation accuracy and is easy to be implemented by hardware. It can be extended to SC-FDE systems in other large doppler scenarios.

A novel Nonlinear Adaptive Doppler Shift Estimation Technique (NADSET) is introduced in this paper. The quality of Doppler shift and power estimations by conventional Fourier-transform-based spectrum estimation methods deteriorates rapidly in low signal-to-noise-ratio (SNR) environment. The new NADSET algorithm compensates such deterioration in the quality of wind parameter estimates by adaptively utilizing the statistics of Doppler shift estimate in strong SNR ranges and identifying sporadic range bins where good Doppler shift estimates are found. NADSET is based on the nature of continuous wind profile and significantly improves the accuracy and the quality of Doppler shift estimates in low SNR ranges. The authenticity of NADSET is established by comparing the trend of wind parameters with and without NADSET applied to the lidar returns acquired over a long period of time by the coherent Doppler lidar system VALIDAR at NASA Langley Research Center in Virginia.