Fast algorithm for nonstationary delay estimation

In this paper, we present a new computationally efficient and accurate so-called explicit modulated Lagrange adaptive frequency estimation (EMLAFE) algorithm that is based on a variable delay element of Etter and Hush and the modulated Lagrange adaptive frequency estimator (MLAFE) of Dooley and Nandi. Adopting a similar idea as in So et al., on explicit time delay estimation, we introduce explicit frequency estimation in our EMLAFE algorithm in order to speed up the frequency estimation. Our development of the EMLAFE algorithm is based on the fact that the cost function of the modulated Lagrange interpolation delay filter (MLIDF), which is used as a FIR delay system in the frequency estimator, is sinusoidal in form with respect to the product of signal frequency and time delay under a high signal to noise ratio (SNR) condition. This leads to a simple formula relating the estimated delay to the unknown frequency, and thus to an efficient frequency estimator. The superior performance of the new algorithm is demonstrated in the context of tracking and estimating a chirp or constant frequency signal in noise. We also derive the Cramer-Rao bound and the steady state variance, for the frequency estimate.

SummaryThe near‐field electromagnetic ranging systems exploit the near‐field phase behavior of low frequency signals for indoor real‐time ranging. Conventionally, phase detector is used to determine the phase of electric and magnetic fields. Subsequently, the phase difference is used to estimate a distance value. In adaptive time delay estimation approach, the time delay between electric and magnetic signals is used instead of phase difference for an estimation. This method does not require a priori knowledge of the transmitting signals and can obtain better performance in low signal‐to‐noise ratio environment. In this paper, we propose a digital near‐field electromagnetic ranging system design and implement it on field‐programmable gate array. This system is composed of time delay estimator, frequency estimator, and received signal strength indicator. A maximum correntropy criterion–based explicit time delay estimation algorithm with a delay in the coefficient update is implemented to estimate the time delay in real time. The fast Fourier transform and received signal strength indicator modules determine the signal frequency and the signal strength received, respectively. Our experimental results indicate that the delayed maximum correntropy criterion–based explicit time delay estimation algorithm has fast convergence rate and good steady‐state performance in an impulsive noise environment.

A new method of explicitly adaptive time delay estimation (EATDE) algorithm is proposed for estimating a varying time delay parameter. The proposed method is based on the Haar wavelet transform of cross-correlations. The proposed algorithm can be viewed as a gradient-based optimization of lowpass filtered cross-correlations, but requires less computational power. The algorithm shows a global convergence property for wide-band signals with uncorrelated noises. A convergence analysis including mean behavior, mean-square-error behavior, and steady-state error of delay estimate is given. Simulation results are also provided to demonstrate the performance of the proposed algorithm.

The focus of this letter is the estimation of a delay between two signals. Such a problem is common in signal processing and particularly challenging when the delay is non-stationary in nature. Our proposed solution is based on an all-pass filter framework comprising of two elements: a time delay is equivalent to all-pass filtering and an all-pass filter can be represented in terms of a ratio of a finite impulse response (FIR) filter and its time reversal. Using these elements, we propose an adaptive filtering algorithm with an LMS style update that estimates the FIR filter coefficients and the time delay. Specifically, at each time step, the algorithm updates the filter coefficients based on a gradient descent update and then extracts an estimate of the time delay from the filter. We validate our algorithm on synthetic data demonstrating that it is both accurate and capable of tracking time-varying delays.

Hyperbolic localization of incipient tip vortex cavitation in marine propeller using spectral kurtosis

In a multipath communication scenario, it is often relevant to estimate the directions and relative delays of each multipath ray. We derive a closed-form subspace-based algorithm for the joint high-resolution estimation of both angles and delays from measured impulse response data, assuming knowledge of the modulation pulse shape function. The algorithm uses a 2-D ESPRIT-like shift-invariance technique to separate and estimate the phase shifts due to delay and direction-of-incidence, with automatic pairing of the two parameter sets.

The estimation of delay and Doppler shift parameters in the presence of multiple targets in radar applications are addressed in this study. The construction of a new ambiguity function, namely, exponential ambiguity function, is proposed by introducing an exponent $p$ into the traditional ambiguity function. Some important properties of the exponential ambiguity function are derived. Then the joint delay and Doppler shift estimation algorithm is proposed based on the combination of the exponential ambiguity function and wavelet denoising method. The performance of the proposed estimation algorithm is theoretically analyzed, verified via Monte Carlo simulation, and compared with the performance of the traditional ambiguity function. Simulation results and theoretical performance analysis show that the proposed estimation algorithm outperforms the existing methods for closely located targets.

Automated intersection delay estimation using the input–output principle and turning movement data

Global Navigation Satellite System (GNSS) are one of the promising fields of applied astronautics. They give coordinates and time high quality measurements supporting land, sea, air and space applications. In the article, the possible sources of errors in the algorithm for calculating the navigation parameters are analyzed and algorithm for calculating the navigation parameters of the GNSS receiver using raw measurements is introduced. The influence of the troposphere on the GNSS signals is manifested in the form of an additional delay that add to time the signal passing from the satellite to the receiver is taking, this delay depends on temperature, pressure, humidity, as well as on the antennas of the transmitter and receiver. The main feature of the troposphere is that it is a non-dispersive medium to electromagnetic waves up to 15 GHz, thus, tropospheric effects do not change with the frequency of the GNSS signals. Tropospheric delays are usually calculated using Neil mapping function, which has its drawbacks. Thus, this article considers a new algorithm for tropospheric delay estimation by using a genetic algorithm for calculating the navigation parameters of a stationary GNSS receiver. Genetic algorithm, as an effective method of search and optimization, is used to improve the characteristics of the GNSS, reduce the total calculation time and achieve faster convergence to the optimal solution. In the article, a genetic algorithm for tropospheric delay estimation based on the iterative least squares method is presented. In the final part, the results of the genetic algorithm are presented and a comparison of the results of the algorithm with the Niell mapping function.

Pre-distortion algorithms for the compensation of AM/AM and AM/PM non-linearities of high power amplifiers (HPA) require a previous estimation of the delay introduced by the analog chains at the input and output of the amplifier. This paper presents a coarse time delay estimation (TDE) algorithm to within one-sample resolution based on a conditional entropy principle. The delay estimate is computed via one dimensional search on the proposed TDE spectrum.

We present a novel maximum-likelihood (ML) algorithm for joint delay estimation and frame synchronization. The algorithm operates on coded signals and exploits the code properties by accepting soft information from the MAP decoder. Issues of convergence are addressed and we show how computational complexity may be reduced without any performance degradation. Simulation results are presented for convolutional and turbo codes, and are compared to performance results of conventional algorithms both in terms of mean square estimation error (MSEE) and BER. We show that code-aided delay estimation always improves the MSEE, but not necessarily the BER. On the other hand, code-aided frame synchronization is mandatory, in order to avoid either significant BER degradations or the need for very long pilot sequences.Key wordsturbo synchronizationdelay estimationframe synchronizationEM algorithm

In most of biomedical signal processing applications the presence of interference signals is inevitable. Processing of lung sounds is not an exemption. In this case the heart sounds are unavoidable and sometimes a severe disturbing noise source. Previous studies indicated that the performance of any interference cancellation scheme relies strongly on an adequate time delay estimation between the reference and the primary signals. Here, a new procedure for time delay estimation using the multi-resolution framework is presented. The proposed methodology possesses such key benefits as the multiple looks information of a signal at different bands, robustness in presence of noise and a validation procedure for the delay estimates. The procedure is applicable to a broad range of problems where time delay estimation is required. Results obtained with lung sounds show the promise of the scheme for the time delay estimation and subsequent interference cancellation.

In order to enhance signals with very low signal-to-noise ratio, a large array of sensors can be used to combine the received signals. SUMPLE, one of important signal combining approaches, uses less hardware and processing power while at the same time providing very low combining loss for weak signals. However, the combining loss of SUMPLE increases when a sensor in an array fails, and the weight amplitude of the sensor cannot drop to zero to automatically compensate for its failure. An enhanced SUMPLE algorithm is proposed in this paper. The gain factor of the explicit time delay and gain estimator is used to detect the failed sensor, and the corresponding weight amplitude is set to zero to minimize the combining loss. Mathematical model and performance analysis for a failed sensor in an array are presented. Simulation results indicate that the proposed algorithm performs better than the traditional state-of-the-art counterparts.

Despite its high-level of robustness and versatility, the human sensorimotor control system regularly encounters and manages various noises, non-linearities, uncertainties, redundancies, and delays. These delays, which are critical to biomechanical stability, occur in various parts of the system and include sensory, signal transmission, CNS processing, as well as muscle activation delays. Despite the relevance of accurate estimation and prediction of the various time delays, the current literature reflects major discrepancy with regards to existing prediction and estimation methods. This scoping review was conducted with the aim of characterizing and categorizing various approaches for estimation of physiological time delays based on PRISMA guidelines. Five data bases (EMBASE, PubMed, Scopus, IEEE and Web of Science) were consulted between the years of 2000 and 2022, with a combination of four related categories of keywords. Scientific articles estimating at least one physiological time delay, experimentally or through simulations, were included. Eventually, 46 articles were identified and analyzed with 20 quantification and 16 qualification questions by two separate reviewers. Overall, the reviewed studies, experimental and analytical, employing both linear and non-linear models, reflected heterogeneity in the definition of time delay and demonstrated high variability in experimental protocols as well as the estimation of delay values. Most of the summarized articles were classified in the high-quality category, where multiple sound analytical approaches, including optimization, regression, Kalman filter and neural network in time domain or frequency domain were used. Importantly, more than 50% of the reviewed articles did not clearly define the nature of the estimated delays. This review presents and summarizes these issues and calls for a standardization of future scientific works for estimation of physiological time-delay.

In this paper, the effects of delay alignment between power amplifiers' input and output waveforms used to synthesize the digital predistorters (DPD) is discussed. Various delay values are applied to align the input and output waveforms; and a memory polynomial based DPD model is synthesized for each delay value. Then, the effects of the residual delay on the performance of the linearized amplifier are evaluated. This reveals that the DPD performance is altered by delay under-estimation. Conversely, the DPD is not sensitive to delay over-estimation by up to one sampling period. This overcomes the need for the signal up-sampling required for sub-sample fine resolution delay alignment, and considerably reduces the computational complexity of the digital signal processing algorithm employed for delay estimation and alignment. A low complexity coarse delay estimation and alignment algorithm is then proposed.