An ARMA modeling method for estimation of coherence and time delay

The authors introduce a test for detecting the presence of a common signal on M noisy channels that generalizes a popular technique using the magnitude-squared coherence (MSC) estimate for detecting a common signal on two channels. The basis of this test is the generalized coherence estimate, a statistic involving the normalized M*M Gram matrix determinant, which reduces to the MSC estimate for M=2. The distribution of the generalized coherence estimate is derived under the assumptions of independent noise and no common signal on the M channels.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Accurate localization in harsh indoor environments has long been a challenging problem due to the presence of multipath. Since joint direction-of-arrival (DOA) and time delay (TD) estimation has the capability to separate the line-of-sight signal from multipath signals in the TD space, it has recently become a key technique for accurate indoor localization in next-generation WiFi and 5G networks. This paper addresses the problem of joint azimuth, elevation, and TD estimation of multiple reflections of a known signal. First, we propose an efficient approximate maximum likelihood algorithm for this problem, which updates the DOA and TD parameters alternatingly. This algorithm applies to arbitrarily distributed (planar or 3-D) arrays. Then, we present the closed-form Cramer–Rao bound for joint DOA and TD estimation, based on which we provide further analysis to show the benefit of joint DOA and TD estimation over DOA-only estimation. Although the benefit of joint estimation has been empirically shown long ago, our analysis is the first theoretical proof of it. Finally, simulation results have been provided to demonstrate the theoretical finding and the effectiveness of the new algorithm. Matlab code for the new algorithm is available at https://github.com/FWen/JADE.git .

This correspondence presents a simple functional model for obtaining the magnitude squared coherence (MSC) estimate. The decrease in the approximated mean of the estimate with signal misalignment is shown to be a function of the signal spectrum and misalignment time, τ. The mean of the estimate in the absence of a signal common to both channels is shown to be a product of the bandwidth of the noise W and the averaging time T. An alternate process employing clipping for normalization is shown to provide a similar result and provides inherent constant false alarm rate (CFAR) properties.

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.

A time delay estimation-based PD-sliding mode control method is proposed to implement high performance trajectory tracking control for a hybrid robot used for automobile electro-coating conveying. First, to exempt the difficulty of real-time dynamic control from calculations of hybrid robot dynamics equations due to its complex mechanism, the time delay estimation-based dynamic model is established by linearizing the dynamic model with time delay estimation technology. Then, a sliding mode control method for the hybrid robot based on time delay estimation-based linearized dynamic model is presented for solving the problem of time delay estimation errors and overcoming the external interferences dramatically. Moreover, the calculation on system dynamics of sliding mode control for the hybrid robot is avoided by substituting the PD control item for the equivalent item of sliding mode control. Lyapunov stability theorem is used to prove the stability of the proposed control law. Finally, the PD control and the proposed control method for the hybrid robot are simulated by MATLAB, and the experiment is carried out on the prototype. The results demonstrate that the proposed control method is independent of complex dynamic model which could avoid the calculation on system dynamics of sliding mode control for the hybrid robot, and it could realize high-accuracy trajectory tracking control compared to PD control under external disturbances.KeywordsHybrid robotPD controlSliding mode controlTime delay estimation

Preface. 1 Spread spectrum signals and systems. 1.1 Basic definition. 1.2 Historical sketch. 2 Classical reception problems and signal design. 2.1 Gaussian channel, general reception problem and optimal decision rules. 2.2 Binary data transmission (deterministic signals). 2.3 M-ary data transmission: deterministic signals. 2.4 Complex envelope of a bandpass signal. 2.5 M-ary data transmission: noncoherent signals. 2.6 Trade-off between orthogonal-coding gain and bandwidth. 2.7 Examples of orthogonal signal sets. 2.8 Signal parameter estimation. 2.9 Amplitude estimation. 2.10 Phase estimation. 2.11 Autocorrelation function and matched filter response. 2.12 Estimation of the bandpass signal time delay. 2.13 Estimation of carrier frequency. 2.14 Simultaneous estimation of time delay and frequency. 2.15 Signal resolution. 2.16 Summary. Problems. 3 Merits of spread spectrum. 3.1 Jamming immunity. 3.2 Low probability of detection. 3.3 Signal structure secrecy. 3.4 Electromagnetic compatibility. 3.5 Propagation effects in wireless systems. 3.6 Diversity. 3.7 Multipath diversity and RAKE receiver. Problems. 4 Multiuser environment: code division multiple access. 4.1 Multiuser systems and the multiple access problem. 4.2 Frequency division multiple access. 4.3 Time division multiple access. 4.4 Synchronous code division multiple access. 4.5 Asynchronous CDMA. 4.6 Asynchronous CDMA in the cellular networks. Problems. 5 Discrete spread spectrum signals. 5.1 Spread spectrum modulation. 5.2 General model and categorization of discrete signals. 5.3 Correlation functions of APSK signals. 5.4 Calculating correlation functions of code sequences. 5.5 Correlation functions of FSK signals. 5.6 Processing gain of discrete signals. Problems. 6 Spread spectrum signals for time measurement, synchronization and time-resolution. 6.1 Demands on ACF: revisited. 6.2 Signals with continuous frequency modulation. 6.3 Criterion of good aperiodic ACF of APSK signals. 6.4 Optimization of aperiodic PSK signals. 6.5 Perfect periodic ACF: minimax binary sequences. 6.6 Initial knowledge on finite fields and linear sequences. 6.7 Periodic ACF of m-sequences. 6.8 More about finite fields. 6.9 Legendre sequences. 6.10 Binary codes with good aperiodic ACF: revisited. 6.11 Sequences with perfect periodic ACF. 6.12 Suppression of sidelobes along the delay axis. 6.13 FSK signals with optimal aperiodic ACF. Problems. 7 Spread spectrum signature ensembles for CDMA applications. 7.1 Data transmission via spread spectrum. 7.2 Designing signature ensembles for synchronous DS CDMA. 7.3 Approaches to designing signature ensembles for asynchronous DS CDMA. 7.4 Time-offset signatures for asynchronous CDMA. 7.5 Examples of minimax signature ensembles. Problems. 8 DS spread spectrum signal acquisition and tracking. 8.1 Acquisition and tracking procedures. 8.2 Serial search. 8.3 Acquisition acceleration techniques. 8.4 Code tracking. Problems. 9 Channel coding in spread spectrum systems. 9.1 Preliminary notes and terminology. 9.2 Error-detecting block codes. 9.3 Convolutional codes. 9.4 Turbo codes. 9.5 Channel interleaving. Problems. 10 Some advancements in spread spectrum systems development. 10.1 Multiuser reception and suppressing MAI. 10.2 Multicarrier modulation and OFDM. 10.3 Transmit diversity and space-time coding in CDMA systems. Problems. 11 Examples of operational wireless spread spectrum systems. 11.1 Preliminary remarks. 11.2 Global positioning system. 11.3 Air interfaces cdmaOne (IS-95) and cdma2000. 11.4 Air interface UMTS. References. Index.

The use of active vibration control may induce a delay leading to detrimental degradation of the performance of active vibration control. This is particularly true in the case of mechanical systems subjected to friction-induced vibration and noise for which such time-delays can lead to the appearance of undesirable instability. Furthermore, conducting a stability analysis of time-delay systems and estimation of the critical time delay are challenging, due to the infinite nature of the characteristic (quasi) polynomial of the associated closed-loop system, having an infinite number of roots.The objective of this paper is to discuss a strategy for the estimation of the critical time delay for the problem of Friction-Induced Vibration and noisE (FIVE). To achieve such an objective, the prediction of the stability analysis of time delay systems and the estimation of the associated critical time delay are first performed by applying the frequency sweep test and the eigenvalue problem approximation using the Taylor series expansion of the delayed term. In a second time, a mixed approach is proposed to predict effectively the real critical time delay of autonomous controlled systems subjected to friction-induced vibration. The efficiency of the proposed approach is illustrated by numerical examples for the prediction of self-sustaining vibrations of a phenomenological model with two degrees of freedom for which it is possible to provide a clear understanding and illustration of the phenomena involved and observed.

This paper addresses issues in improving hands-free speech recognition performance in car environments. A three-microphone array has been used to form a beamformer with normalised least-mean squares (NLMS) to improve Signal to Noise Ratio (SNR). The array has been implemented with a Voice Activity Detector (VAD) which uses time-delay estimation together with magnitude-squared coherence (MSC). An experiment clearly shows the ability of the composite system to reduce noise outside of a defined active zone.

Supply of uninterrupted power is of present need in progressing India. The most of these interruptions occur mainly due to failures in insulations of electrical apparatus. Therefore, the present work aimed at developing an early warning system for electrical distribution substations by predicting insulation breakdown. This paper, focused on time delay estimation in between the UHF signals received at multiple sensors placed around the substation. The delay estimation is most critical and essential part in detection and localization of electrical component having weakness or some defects in insulation and act as PD source. Unfortunately, the radiated UHF signals are highly contaminated with noise, form mobile, TV, and Radio broadcastings. Previous works on time delay estimation in the area mainly depends on time domain based estimation procedures, such as first peak (FP), cumulative energy (CE), and cross correlation (CC). However, recent study reveals that, each PD sources having less variant and almost unique frequency domain based signature. Therefore, in the work, Generalized Cross Correlation (GCC) which is a frequency domain based correlation technique for estimation of time delay has been investigated in details. In the work different weighing functions for the GCC, such as Smoothed Coherence Transform (SCOT), the Phase Transform (PHAT), the Eckart Filter (EF) and the Maximum Likelihood (ML) estimator. The study reveals that the GCC with ML function provide most accurate and precise estimation of time delay.

Time delay estimation is the basis of target location of multi-sensor network, while the basic analysis of the signal is of key importance to the time delay estimation. In this paper, a time delay analysis system is designed by virtual instrument development tools. The analysis system contains: virtual oscilloscope, virtual spectrum analyzer, virtual filter and virtual time delay analyzer. Using this analysis system on a set of field collected acoustic target signal, spectrum analysis and filtering is made. Then the time delay estimation has been carried out for several sampling data segments selected by certain rules, and curve of the time delay vs. time is given. Analysis shows the main frequency of the target signal: 85Hz, 13Hz, 170Hz and 125Hz. The regular pattern of the time delay curve is consistent with the target motion. Application reveals that the system is helpful in the research of time delay estimation.

In this paper we present a highly accurate, spline-based time delay estimator (TDE) that directly determines subsample time delay estimates from sampled data. The algorithm uses cubic splines to produce a continuous time representation of a reference signal and then computes an analytical matching function between this reference and a delayed signal. The location of the minima of this function yields estimates of the time delay. We also present a more computationally efficient formulation of this algorithm, which is based on FIR filtering to determine the cubic spline coefficients, polynomial approximation to determine the time delay estimates and adaptive search of the delay estimate. The proposed algorithm is particularly useful in applications such as blood flow estimation and tissue elasticity estimation since it can include companding with little additional computational cost.

Relative navigation of spacecrafts may be accomplished by observing X-ray sources and indirectly determining the spacecraftspsila relative position. In this approach, two spacecrafts lock on a known pulsar which irradiates X-ray waveforms that reach them with a differential time delay that is proportional to the distance between the spacecrafts. By observing different pulsar sources geometrically distributed over the galactic disc, it is possible to determine the spacecraftspsila relative inertial position. Our goal is to estimate their relative position by Time Delay Estimation (TDE) between the detected signals. Although there are several off-line TDE methods, like the basic cross-correlation (BCC) and the generalized cross-correlation (GCC) techniques, in this work we formulate TDE as a channel estimation problem and apply adaptive filtering techniques to estimate the time delay online. There are certain benefits in using adaptive filters, especially when the underlying parameters like signalspsila statistics are unknown or change over time. We study different adaptive algorithms and show how they are able to efficiently deliver accurate delay estimates at reduced computational complexity and in real time.

Neuro-fuzzy based time-delay estimation using DCT coefficients

The problem of forward and backward time delays is significantly important for both control and feedback loop of networked control systems. These time delays give rise to latency in performance and thereby may destabilize the system. Therefore numerous methods have been proposed about time delay identification/estimation and compensation for networked control systems, especially for bilateral teleoperation systems. However, most compensation methods have been accomplished by considering offline time delay estimation for linear/nonlinear time delay control systems. In this work, we propose an observer based estimation algorithm for round trip delay which is the sum of forward and backward time delays for a 1 degree-of-freedom nonlinear bilateral teleoperation system. Via Lyapunov based stability analysis, global boundedness of the observer errors along with their ultimate convergence and the convergence of the round trip delay estimator to the vicinity of its real value can be guaranteed in the closed-loop system. Finally, simulation and experimental studies are carried out utilizing the last link of a PHANToM Omni Haptic device moving like a one-link robot in the vertical plane.