Accurate time delay estimation based on SINC filtering

This study dealt with the problem of estimating constant time delay embedded into a received signal that was noisy, delayed and damped image of a known reference signal. The received signal was filtered, normalized with respect to the peak value it achieved and then transformed by the Discrete Cosine Transform (DCT) into DCT coefficients. Those DCT coefficients that were most sensitive to time delay variations were selected and grouped to form the Reduced Discrete Cosine Transform Coefficients set (RDCTC). The time delays embedded in the filtered signals were efficiently encoded into those RDCTC sets. The RDCTC sets were applied to a pre trained multi layer feedforward Neural Network (NN), which computed the time-delay estimates. The network was initially trained with large sets of RDCTC vectors, in which each RDCTC vector corresponded to a signal delayed by a randomly selected constant time-delay. Using the RDCTC as input to the NN instead of the full length incoming signal itself resulted in a major reduction in the NN size. Accurate time delay estimates were obtained through simulation and compared against estimates obtained through classical cross-correlation technique.

The problem of time delay estimation (TDE) in the presence ofzero mean white Gaussian noise is addressed. A novel and efficient algorithm for obtaining Conditional Maximum Likelihood (CML) estimates of delays, attenuation factors and the number of paths from a transmitter to a receiver is introduced. This approach is quite suitable for the cases in which time delay difference is less than the duration of signal autocorrelation.

In passive sonar systems, a target is localized using time delays obtained from signals received at spatially separated points. The variance of the time delay estimates about the true time delay at low signal-to-noise ratio (SNR) depends on SNR, bandwidth, center frequency, and spectral slopes of signal and noise. The accuracy of time delay estimation can be enhanced using a pre-shaping filter. We shall show in this paper that if signal and noise spectral fall off rates are close to each other (within 3 dB/oct), then the preshaping filter does not appreciably enhance the accuracy of time delay estimation. However, if the signal and noise spectral slopes are significantly different (about 6 dB/oct), then the preshaping filter increases the accuracy.

The time delay estimation (TDE) is one of the most important techniques of characterization signal parameter. Delay parameter estimation is active in the field of signal processing. In this paper, On the basis of the basic theory method of time delay, the correlated time delay estimation, mutual-cumulants estimation and bispectrum estimation are analysed and discussed. Theoretical analysis and experimental results show that three methods is effectively used in time delay test, but higher order statistics (HOS) method can achieve the best effect. The HOS estimation method has many advantages, such as time delay estimation accuracy, high relative error small for Gaussian noise elimination ability.

Partial discharge (PD) localization in substations based on the ultra-high frequency (UHF) method can be used to efficiently assess insulation conditions. Localization accuracy is affected by the accuracy of the time delay (TD) estimation, which is critical for PD localization in substations. A review of existing TD estimation methods indicates that there is a need to develop methods that are both accurate and computationally efficient. In this paper, a novel TD estimation method is proposed to improve both accuracy and efficiency. The TD is calculated using an improved cross-correlation algorithm based on full-wavefronts of array UHF signals, which are extracted using the minimum cumulative energy method and zero-crossing points searching methods. The cross-correlation algorithm effectively suppresses the TD error caused by differences between full-wavefronts. To verify the method, a simulated PD source test in a laboratory and a field test in a 220 kV substation were carried out. The results show that the proposed method is accurate even in case of low signal-to-noise ratio, but with greatly improved computational efficiency.

A novel approach for estimating constant time delay through the use of neural networks (NN) is introduced. A desired reference signal and a delayed, damped, and noisy replica of it are both filtered by a fourth-order digital infinite impulse response (IIR) filter. The filtered signals are normalized with respect to the highest values they achieve and then applied as input for an NN system. The output of the NN is the estimated time delay. The network is first trained with one thousand training data set in which each data set corresponds to a randomly chosen constant time delay. The estimated time delay obtained by the NN is an accurate estimate of the exact time-delay values. Even in the case of noisy data, the estimation error obtained was a fraction of the sampling time interval. The delay estimates obtained by the NN are comparable to the estimated delay values obtained by the cross-correlation technique. The main advantage of using this technique is that accurate estimation of time delay results from performing one pass of the filtered and normalized data through the NN. This estimation process is fast when compared to the classical techniques utilized for time-delay estimation. Classical techniques rely on generating the computationally demanding cross-correlation function of the two signals. Then a peak detector algorithm is utilized to find the time at which the peak occurs.

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.

Adaptive pulsar time delay estimation using wavelet-based RLS

Measuring delay using cross correlation technique with signals that are corrupted by noise and are received at two spatially separated sensors often result incorrect measurement. This paper presents a new technique that is Hilbert transform of FFT pruned cross correlation function to improve the accuracy of time delay estimates. To investigate the performance of this technique, frequency algorithms are provided to generate synthetic complex base band signals. For each iteration, it generates two sets of signal. We have to specify input parameters to the simulation model any time delay of signal 2 from signal 1, length and number of complex base band signals to be generated. Such model imitate real-life system, and by varying the uncorrelated complex base band signals to various SNR conditions, further analysis can be performed to compare the performance of this new technique with other existing techniques such as parabolic interpolation, zero crossing of Hilbert transform and FFT pruning.

Neuro-fuzzy based time-delay estimation using DCT coefficients

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 .

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.

The three-hydrophone array is often used for underwater passive ranging. It uses the time delay estimation (TDE) between hydrophone pairs to calculate the target range. Traditionally, the generalized cross correlation (GCC) algorithm is used for TDE. However, when the available bandwidth is limited, the accuracy of GCC decreases severely and thus, fails to meet the performance requirement. To solve this problem, we propose a new TDE algorithm based on time-dimensional Capon beamforming. The proposed method uses Capon beamforming to process sampled signals at hydrophones to obtain the time-domain output and accordingly calculates the time delays between hydrophone pairs. Specifically, we extract several subband components from GCC outputs in the frequency domian. To suppress the amplitudes fluctuation between different subbands, the subband amplitude modification and the diagonal loading (DL) algorithm are used to improve the robustness of time-dimensional Capon beamforming. Finally, we estimate the range of target using the TDE result obtained by the proposed method. What's more, to analyze the TDE accuracy of the time-dimensional Capon beamforming, we compare the TDE variance of both GCC and the proposed method with Cramér-Rao lower bound (CRLB) under different simulation conditions. The results demonstrate that the proposed algorithm could significantly improve the performance of passive ranging in three-hydrophone array, in the case of relatively short bandwidth associated with subband amplitude fluctuations.

The localization of acoustic reflectors is a fundamental component in various applications, including room acoustics analysis, sound source localization, and acoustic scene analysis. Time delay estimation (TDE) is essential for determining the position of reflectors relative to a sensor array. Traditional TDE algorithms generally yield time delays that are integer multiples of the operating sampling period, potentially lacking sufficient time resolution. To achieve subsample TDE accuracy, various interpolation methods, including parabolic, Gaussian, frequency, and sinc interpolation, have been proposed. This paper presents a comprehensive study on time delay interpolation to achieve subsample accuracy for acoustic reflector localization in reverberant conditions. We derive the Whittaker-Shannon interpolation formula from the previously proposed sinc interpolation in the context of short-time windowed TDE for acoustic reflector localization. Simulations show that sinc and Whittaker-Shannon interpolation outperform existing methods in terms of time delay error and positional error for critically sampled and band limited reflections. Performance is evaluated on real-world measurements from the MYRiAD dataset, showing that sinc and Whittaker-Shannon interpolation consistently provide reliable performance across different sensor-source pairs and loudspeaker positions. These results can enhance the precision of acoustic reflector localization systems, vital for applications, such as room acoustics analysis, sound source localization, and acoustic scene analysis.

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.