High-Precision Time Delay Estimation Based on Closed-Form Offset Compensation

To improve the estimation accuracy,a novel time delay estimation(TDE)method based on the closed-form offset compensation is proposed.Firstly,we use the generalized cross-correlation with phase transform(GCC-PHAT)method to obtain the initial TDE.Secondly,a signal model using normalized cross spectrum is established,and the noise subspace is extracted by eigenvalue decomposition(EVD)of covariance matrix.Using the orthogonal relation between the steering vector and the noise subspace,the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE.Finally,the offsets are compensated via simple least squares(LS).Compared to other state-of-the-art methods,the proposed method significantly reduces the computational complexity and achieves better estimation performance.Experiments on both simulation and real-world data verify the efficiency of the proposed approach.

Time Delay Estimation in Radar System using Fuzzy Based Iterative Unscented Kalman Filter

RSs(Radar Systems)identify and trace targets and are commonly employed in applications like air traffic control and remote sensing.They are necessary for monitoring precise target trajectories.Estimations of RSs are non-linear as the parameters TDEs(time delay Estimations)and Doppler shifts are computed on receipt of echoes where EKFs(Extended Kalman Filters)and UKFs(Unscented Kalman Filters)have not been examined for computations.RSs,certain times result in poor accuracies and SNRs(low signal to noise ratios)especially,while encountering complicated environments.This work proposes IUKFs(Iterated UKFs)to track onlinefilter performances while using optimization techniques to enhance outcomes.The use of cost functions can assist state corrections while lowering costs.A new parameter is optimized using MCEHOs(Mutation Chaotic Elephant Herding Optimizations)by linearly approximating system non-linearity where OIUKFs(Optimized Iterative UKFs)predict a target's unknown parameters.To obtain optimal solutions theoretically,OIUKFs take less iteration,resulting in shorter execution times.The proposed OIUKFs provide numerical approximations which are derivative-free implementations.Simulation evaluation results with estimators show better performances in terms of reduced NMSEs(Normalized Mean Square Errors),RMSEs(Root Mean Squared Errors),SNRs,variances,and better accuracies than current approaches.

SUBSAMPLE TIME DELAY ESTIMATION BASED ON PHASE SPECTRUM OF BAND LIMITED STOCHASTIC SIGNALS

The accuracy of conventional time delay estimation （TDE） algorithms is limited by the sampling interval. A novel algorithm of subsample TDE suitable for widehand signals is presented to improve the accuracy. This algorithm applies periodogram and parabolic interpolation to the cross correlation spectrum of band limited stochastic signals, and can obtain a continuous time delay estimator. Simulations are carried out to compare the performance of the proposed algorithm with that of other subsample TDE algorithms. Results show that the proposed algorithm outperforms other algorithms and reachs the Cramer-Rao lower bound （CRLB） at a high signal- to-noise ratio. For the wideband characteristic and the randomness of the transmitting signal, the proposed algo- rithm is suitable for the low probability of intercept radars.

A True-Time-Delay Array Architecture for Wideband Multi-Beam Tracking

The true-time delay(TTD)units are critical for solving beam squint and frequency selective fading inWideband Large-Scale Antenna Systems(LSASs).In this work,we propose a TTD array architecture for wideband multi-beam tracking that eliminates the beam squint phenomenon and filters out interference signals by applying a spatial filter and time delay estimations(TDEs).The paper presents a novel approach to spatial filter design by introducing a transformation matrix that can optimize the beam response in a specific direction and at a specific frequency.Using the variable fractional delay(VFD)filters,we propose a TDE algorithm with a Newton-Raphson iteration update process that corrects the arrival time delay difference between sensors.Simulations and examples have demonstrated that the proposed architecture can achieve beam tracking within 10 ms at the low signalto-noise ratio(SNR)and demodulation loss is less than 0.5 dB in wideband multi-beam scenarios.

Narrowband time delay estimation based on correlation coefficient

The problems of time delay estimation of narrowband signals are presented. The disadvantages of the existing algorithms are analyzed, and a new narrowband time delay estimating algorithm based on correlation coefficient is proposed. The original time delay information is transfered into the delay between the autocorrelation and cross-correlation function, and the precise estimating result by wave-comparison is given. The algorithm proposed here is also compared with other algorithms and its advantages over other algorithms are proved. The theoretical analysis and simulation show the effectiveness of the proposed algorithm.

A Simple Method for Source Depth Estimation with Multi-path Time Delay in Deep Ocean

A method of source depth estimation based on the multi-path time delay difference is proposed. When the minimum time arrivals in all receiver depths are snapped to a certain time on time delay-depth plane, time delay arrivals of surface-bottom reflection and bottom-surface reflection intersect at the source depth. Two hydrophones deployed vertically with a certain interval are required at least. If the receiver depths are known, the pair of time delays can be used to estimate the source depth. With the proposed method the source depth can be estimated successfully in a moderate range in the deep ocean without complicated matched-field calculations in the simulations and experiments.

Underdetermined DOA estimation via multiple time-delay covariance matrices and deep residual network

Higher-order statistics based approaches and signal sparseness based approaches have emerged in recent decades to resolve the underdetermined direction-of-arrival(DOA)estimation problem.These model-based methods face great challenges in practical applications due to high computational complexity and dependence on ideal assumptions.This paper presents an effective DOA estimation approach based on a deep residual network(DRN)for the underdetermined case.We first extract an input feature from a new matrix calculated by stacking several covariance matrices corresponding to different time delays.We then provide the input feature to the trained DRN to construct the super resolution spectrum.The DRN learns the mapping relationship between the input feature and the spatial spectrum by training.The proposed approach is superior to existing model-based estimation methods in terms of calculation efficiency,independence of source sparseness and adaptive capacity to non-ideal conditions(e.g.,low signal to noise ratio,short bit sequence).Simulations demonstrate the validity and strong performance of the proposed algorithm on both overdetermined and underdetermined cases.

High Resolution Multipath Time Delay Estimation Based on FLOCCS-ESPRIT

The non-Gaussian α-stable distribution is introduced to model impulsive noise. Based on the theory of fractional lower order statistics (FLOS), the fractional lower order cross-covariance (FLOCC) sequence for two received signals is obtained and the fractional lower order cross-covariance spectrum (FLOCCS) can be approached by taking a Fourier transform for the FLOCC sequence. When the FLOCCS is treated as a sequence in the time domain, the problem of multipath time delay estimation (TDE) may be converted into one on multi-frequencies estimation or directions of arrival estimation. Accordingly, the high resolution multipath TDE can be realized with the ESPRIT technology. This idea on multipath TDE is referred to as FLOCCS-ESPRIT in this paper. Computer simulations show that this method has good performance both in a Gaussian noise and in an impulsive noise environment.

Adaptive sliding mode control of modular self-reconfigurable spacecraft with time-delay estimation

The reconstruction control of modular self-reconfigurable spacecraft (MSRS) is addressed using an adaptive sliding mode control (ASMC) scheme based on time-delay estimation (TDE) technology. In contrast to the ground, the base of the MSRS is floating when assembled in orbit, resulting in a strong dynamic coupling effect. A TED-based ASMC technique with exponential reaching law is designed to achieve high-precision coordinated control between the spacecraft base and the robotic arm. TDE technology is used by the controller to compensate for coupling terms and uncertainties, while ASMC can augment and improve TDE’s robustness. To suppress TDE errors and eliminate chattering, a new adaptive law is created to modify gain parameters online, ensuring quick dynamic response and high tracking accuracy. The Lyapunov approach shows that the tracking errors are uniformly ultimately bounded (UUB). Finally, the on-orbit assembly process of MSRS is simulated to validate the efficacy of the proposed control scheme. The simulation results show that the proposed control method can accurately complete the target module’s on-orbit assembly, with minimal perturbations to the spacecraft’s attitude. Meanwhile, it has a high level of robustness and can effectively eliminate chattering.

Joint Noise Reduction and lp-Norm Minimization for Enhancing Time Delay Estimation in Colored Noise

Time delay estimation (TDE) is an important issue in signal processing. Conventional TDE algorithms are usually efficient under white noise environments. In this paper, a joint noise reduction and -norm minimization method is presented to enhance TDE in colored noise. An improved subspace method for colored noise reduction is first performed. Then the time delay is estimated by using an -norm minimization method. Because the clean speech signal form the noisy signal is well extracted by noise reduction and the -norm minimization method is robust, the TDE accuracy can be enhanced. Experiment results confirm that the proposed joint estimation method obtains more accurate TDE than several conventional algorithms in colored noise, especially in the case of low signal-to-noise ratio.

Research on Estimation of Time Delay Difference in Passive Locating for Impulse Signal

Analyzed the relation between time delay difference and time delay estimation errors, based on the principles of three-point locating, a reformed threshold method for time delay difference estimation of impulse signals, called as amendment estimation for short, is developed by introducing channel equalization technique to its conventional version, named as direct estimation in this paper, to improve the estimation stability. After inherent relationship between time delay and phase shift of signals is analyzed, an integer period error compensation method utilized the diversities of both contribution share and contribution mode of concerned estimates is proposed under the condition of high precision phase lag estimation. Finally, a cooperative multi-threshold estimation method composed of amendment and direct estimations to process impulse signals with three thresholds is established. In sea trials data tests of passive locating, this method improves the estimation precision of time delay difference efficiently. The experiments verify the theoretical predictions.

Locating the position of objects in non-line-of-sight based on time delay estimation

Non-line-of-sight imaging detection is to detect hidden objects by indirect light and intermediary surface（diffuser）.It has very important significance in indirect access to an object or dangerous object detection, such as medical treatment and rescue. An approach to locating the positions of hidden objects is proposed based on time delay estimation. The time delays between the received signals and the source signal can be obtained by correlation analysis, and then the positions of hidden objects will be located. Compared with earlier systems and methods, the proposed approach has some modifications and provides significant improvements, such as quick data acquisition, simple system structure and low cost, and can locate the positions of hidden objects as well： this technology lays a good foundation for developing a practical system that can be used in real applications.

LMS ADAPTIVE TIME DELAY ESTIMATION WITH TWO WINDOWS

An LMS adaptive time delay estimation method with two windows is presented. This method can reduce the superfluous calculation greatly when the time of correlation is long. It is suitable for the time delay estimation of white band-limited random signals. The feasibility and the performances of this method are also studied.

Weighting Function Modification Used for Phase Transform-Based Time Delay Estimation

Generalized cross-correlation is considered as the most straightforward time delay estimation algorithm.Depending on various weighting function,different methods were derived and a straightforward method,named phase transform(PHAT)has been widely used.PHAT is well-known for its robustness to reverberation and its sensitivity to noise,which is partly due to the fact that PHAT distributes same weights to the frequencies dominated by signal or noise.To alleviate this problem,two weighting functions are proposed in this paper.By taking a posteriori signal-to-noise ratio(SNR)into account to classify reliable and unreliable frequencies,different weights could be assigned.The first proposed weighting function borrows the idea of binary mask and distributes same weights to frequencies in same set,whereas,the second one assigns weights based on coherence function.Experiments showed the robustness of proposed methods to reverberation and noise for improving the performance of time delay estimation through various criteria.

A HIGH RESOLUTION TIME DELAY ESTIMATION BASED ON THE MAXIMUM ENTROPY POWER SPECTRUM ESTIMATON

Multipath time delay estimation is constrained by the width of the signal correlation function when using correlation based methods. This paper obtains a high resolution time delay estimation by introducing Burg algorithm and Marple algorithm of the maximum entropy power spectral estimation to non-resolvable multipath time delay estimatoin. The principles, the performaces and the results of computer simulation are given.

Time Delay Estimation Based on Entropy Estimation

--This paper presents a novel time delay estimation （TDE） method using the concept of entropy. The relative delay is estimated by minimizing the estimated joint entropy of multiple sensor output signals. When estimating the entropy, the information about the prior distribution of the source signal is not required. Instead, the Parzen window estimator is employed to estimate the density function of the source signal from multiple sensor output signals. Meanwhile, based on the Parzen window estimator, the Renyi＇s quadratic entropy （RQE） is incorporated to effectively and efficiently estimate the high-dimensional joint entropy of the multichannel outputs. Furthermore, a modified form of the joint entropy for embedding information about reverberation （multipath reflections） for speech signals is introduced to enhance the estimator＇s robustness against reverberation.

Multipath Time Delay Estimation Based on MUSIC Algorithm Under Small Sample Conditions

On account of the traditional multiple signal classification(MUSIC)algorithm has poor performance in time delay estimation under the condition of small sampling data and low SNR.In this paper,the traditional MUSIC algorithm is improved.The algorithm combines the idea of spatial smoothing,constructs a new covariance matrix using the covariance information of the measurement data,and constructs a weighted value using the modified noise eigenvalues to weight the traditional estimation spectrum.Simulation results show that the improved algorithm has steeper spectral peaks and better time delay resolution under the condition of inaccurate path number estimation.The time delay estimation accuracy of this algorithm is higher than that of the traditional MUSIC algorithm and the improved SSMUSIC algorithm under the conditions of small sampling data and low SNR.

Underwater Acoustic Communication Based on Pattern Time Delay Shift Coding Scheme

Underwater acoustic communication based on Pattern Tune Delay Shift Coding （PDS） communication scheme is studied. The time delay shift values of the pattern are used to encode the digital information in the PDS scheme, which belongs to the Pulse Position Modulation （PPM）. The duty cycle of the PDS scheme is small, so it can economize the power for communication. By use of different patterns for code division and different frequencies for channel division, the communication system is capable of mitigating the inter-symbol interference （ISI） caused by the muhipath channel. The data rate of communication is 1000 bits/s at 8 kHz bandwidth. The receiver separates the channels by means of bandpass filters, and performs decoding by 4 copy-correlators to estimate the time delay shift value. Based on the theoretical analysis and numerical simulations, the PDS scheme is shown to be a robust and effective approach for underwater acoustic communication.

Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes

Currently, some fault prognosis technology occasionally has relatively unsatisfied performance especially for in- cipient faults in nonlinear processes duo to their large time delay and complex internal connection. To overcome this deficiency, multivariate time delay analysis is incorporated into the high sensitive local kernel principal component analysis. In this approach, mutual information estimation and Bayesian information criterion （BIC） are separately used to acquire the correlation degree and time delay of the process variables. Moreover, in order to achieve prediction, time series prediction by back propagation （BP） network is applied whose input is multivar- iate correlated time series other than the original time series. Then the multivariate time delayed series and future values obtained by time series prediction are combined to construct the input of local kernel principal component analysis （LKPCA） model for incipient fault prognosis. The new method has been exemplified in a sim- ple nonlinear process and the complicated Tennessee Eastman （TE） benchmark process. The results indicate that the new method has suoerioritv in the fault prognosis sensitivity over other traditional fault prognosis methods.

A Novel Technique for Detection of Time Delay Switch Attack on Load Frequency Control

In this paper, we focus on the estimation of time delays caused by adversaries in the sensing loop (SL). Based on the literature review, time delay switch (TDS) attacks could make any control system, in particular a power control system, unstable. Therefore, future smart grids will have to use advanced methods to provide better situational awareness of power grid states keeping smart grids reliable and safe from TDS attacks. Here, we introduce a simple method for preventing time delay switch attack on networked control systems. The method relies on an estimator that will estimate and track time delays introduced by an adversary. Knowing the maximum tolerable time delay of the plant’s optimal controller for which the plant remains stable, a time-delay detector issues an alarm signal when the estimated time delay is larger than the minimum one and directs the system to alarm state. In an alarm state, the plant operates under the control of an emergency controller that is local to the plant and remains in this mode until the networked control system state is restored. This method is an inexpensive and simple way to guarantee that an industrial control system remains stable and secure.