Estimation Of Signal Parameters Research Articles

Review on application of fractional Fourier transform in LinearFrequencyModulation signal and communication system

Traditional Fourier transform often apply to analyze and process stationary signals, however, it is weak for time-varying non-stationary signals, and fractional Fourier transform (FRFT) can better solve such problems. The FRFT can be comprehended as the expressive methods on the fractional Fourier domain constituted by the spinning coordinate axis of the signal anticlockwise about the origin at arbitrarily Angle in the time-frequency plane. In this paper, the improved fractional Fourier transform is combined with other calculation methods to achieve high precision estimation of chirp signal parameters. And the communication system built on weighted fractional Fourier transform and discrete fractional Fourier transform is studied and simulated respectively, which verifies the feasibility and improves the anti-jamming and anti-interception ability of the communication system.

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Inferring Binary Properties from Gravitational-Wave Signals

This review provides a conceptual and technical survey of methods for parameter estimation of gravitational-wave signals in ground-based interferometers such as Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo. We introduce the framework of Bayesian inference and provide an overview of models for the generation and detection of gravitational waves from compact binary mergers, focusing on the essential features that are observable in the signals. Within the traditional likelihood-based paradigm, we describe various approaches for enhancing the efficiency and robustness of parameter inference. This includes techniques for accelerating likelihood evaluations, such as heterodyne/relative binning, reduced-order quadrature, multibanding, and interpolation. We also cover methods to simplify the analysis to improve convergence, via reparameterization, importance sampling, and marginalization. We end with a discussion of recent developments in the application of likelihood-free (simulation-based) inference methods to gravitational-wave data analysis.

A High-Precision, Ultra-Short Baseline Positioning Method for Full Sea Depth

To fulfill the demand for high-precision underwater acoustic positioning at full sea depth, an ultra-short baseline (USBL) positioning method with the square array based on the least squares estimating signal parameters via rotational invariance techniques (LS-ESPRIT) algorithm is presented in this paper. A combination of beam tracking and beamforming is employed to improve the accuracy of direction-of-arrival (DOA) estimation and, consequently, enhance overall positioning accuracy. In order to mitigate the issue of position jumping resulting from phase ambiguity in traditional four-element cross arrays, we have improved the stability of the positioning algorithm by utilizing a multi-element square array and employing the LS-ESPRIT algorithm for DOA estimation. Furthermore, the signal detection method integrating the correlation coefficient and ascending/descending chirp signals is employed to enhance the reliability of the location algorithm. Simulation analysis and experimental results demonstrate that the proposed algorithm effectively enhances positioning accuracy and improves the problem of jumping in positioning results.

A fast method for intra-pulse parameters estimation of polyphase-coded radar signals based on FRFT

A fast method for intra-pulse parameters estimation of polyphase-coded radar signals based on FRFT

The Fast and Reliable Detection of Multiple Narrowband FH Signals: A Practical Framework.

Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.

CML based parameter estimation of FH signals in alpha stable noise environment

CML based parameter estimation of FH signals in alpha stable noise environment

Обобщенная математическая модель радиосигнала в различных базисах

The purpose of this article is to develop a generalized mathematical model of a radio signal in various bases. The proposed model allows us to generalize the known models of radio signals in basis functions of sine/cosine, basis functions of spline characters, bases of spline-Vilenkin-Chrestenson functions, bases of spline-Pontryagin-Vilenkin-Chrestenson functions, etc., constructed on the basis of spline-algebraic harmonic methods analysis. For the generalized model, special cases are formulated for the considered spaces of basis functions. Block diagrams and timing diagrams are presented that explain the main stages of the formation and processing of radio signals in various bases. Using the simulation method in the Matlab environment, the noise immunity of the proposed radio signals was assessed. Analysis of the obtained results showed that radio signals generated on the basis of a generalized mathematical model in various noise immunity bases under AWGN conditions are comparable (and even have a slight advantage) with similar signals generated in the bases of classical exponential functions. An assessment of structural secrecy is presented. It is shown that the structural secrecy of radio signals generated in various bases is ensured by the formation of signals in a priori unknown bases, the parameters of which: group, ring, spline order and number of the basis function change in accordance with a given code word, also unknown to potential unauthorized processing systems. Structural secrecy manifests itself in the incorrect choice of basis when processing signal data for reception, which entails an expansion of the signal spectrum. As a result, incorrect estimation of signal parameters and incorrect setting of the demodulator occurs.

Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation

Accelerated Curve Fitting Approach for Denoised NQR Signal Parameter Estimation

The Optimization of LSTM Model by Wavelet Transform and Simulated Annealing Algorithm

The main objective of this paper is to study the integration of data processing methods and intelligent algorithms to optimize the results of LSTM prediction models. In this paper, continuous wavelet transform is used to clean and preprocess time series data and improve data quality. Wavelet reconstruction is used to restore the results. At the same time, the simulated annealing algorithm is introduced as an intelligent algorithm to search globally for the best solution to achieve the optimal prediction result. The application of this comprehensive approach can also improve the quality and precision of data analysis in various fields, such as the parameter estimation of pulse signals in physics. The core challenge of the research is to optimize the data prediction results, and for this purpose, a multi-level method of continuous wavelet transform, deep learning model (LSTM) and simulated annealing algorithm is adopted.

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data.

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

Innovative Approaches for Dynamic System Monitoring: Signal Processing and Parameter Estimation Strategies

Dynamic system monitoring is essential for ensuring the optimal performance and reliability of various systems across multiple domains. This Abstract introduces innovative approaches focusing on signal processing and parameter estimation strategies for dynamic system monitoring. Signal processing techniques such as wavelet transform and adaptive filtering are utilized for noise reduction and feature extraction from sensor data. Additionally, parameter estimation strategies including Kalman filtering and Bayesian inference aid in accurately estimating system parameters and states in real-time. These advanced methods, integrating machine learning and statistical inference, promise enhanced monitoring capabilities, facilitating proactive maintenance and fault detection in complex dynamic systems. Through case studies and simulation results, the effectiveness and versatility of these approaches in addressing real-world challenges are demonstrated, illustrating their potential for advancing the field of dynamic system monitoring.

Geometric Algebra based 2D-DOA Estimation for Non-circular Signals with an Electromagnetic Vector Array

This paper presents two novel methods based on geometric algebra (GA) to estimate two-dimensional (2D) direction-of-arrival (DOA) of non-circular (NC) signals for uniform rectangular array (URA). Traditional methods treat the received NC signals as a long vector which will inevitably lose orthogonality inside each electromagnetic vector sensor (EMVS) and thus miss some information of second-order statistical properties. Furthermore, the computational complexity will also increase. By contrast, the GA-based estimating signal parameter via rotational invariance techniques (ESPRIT) and propagation method (PM) algorithms are proposed to estimation DOA of NC signals. Taking advantage of GA, the relationship among multidimensional signals can be maintained. First, the six components of the EMVS are represented as a multivector in GA space. Then, we construct the GA-based extended covariance matrix to utilize the signal information more completely. The DOA parameters can be estimated through the ESPRIT and PM principle. The proposed GA-based estimation of signal parameter via rotational invariance techniques for NC signals processing (GANC-ESPRIT) can estimate DOA with high estimation accuracy. The proposed GA-based propagation method for NC signals estimation (GANC-PM) uses linear transformation to calculate angle parameters. Our model has much lower memory requirements and less computational burden compared with long vector models. Simulation results demonstrate the robustness and superiority of the proposed GANC-ESPRIT algorithm in terms of angular resolution. Complexity analysis shows that the proposed GANC-PM algorithm performances better with less computations.

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

Optimizing Path Loss Prediction: Advancements in Signal Parameter Estimation through Wavelet Neural Networks

Received Signal and Channel Parameter Estimation in Molecular Communications

Received Signal and Channel Parameter Estimation in Molecular Communications

Research on Direction Finding Method under Impulsive Noise Based on Nonuniform Linear Array

Direction of arrival (DOA) estimation under impulsive noise has always been an important research area in array signal processing. The traditional methods under impulsive noise mostly rely on prior parameters and have high computational complexity. Based on the filtering theory, we present an effective pretreatment filtering technology to cut out the impulse mixed in the array received data and employ the nonuniform linear array to improve the estimation performance further. First, according to the amplitude characteristics of impulse noise, the pretreatment filtering technology is proposed to cut out the impulse based on the median filter and sliding average filter, which is valid for both strong and weak impulsive noise. Second, the minimum redundant array is adopted to carry out array virtual expansion so that the array aperture can be increased and the estimation performance can be improved. Finally, based on the idea of matrix reconstruction, we propose the improved estimation of signal parameters via rotational invariance techniques algorithm and an improved root multiple signal classification algorithm for DOA estimation. Theoretical analysis and simulation results show that the proposed method has a simple processing process, small calculation load, good array expansion ability, and excellent noise adaptability. Moreover, the proposed methods greatly improve the direction-finding performance under the condition of low signal-to-noise ratio and strong impulsive noise.

A High Degree of Freedom Radiation Near-Field Source Localization Algorithm with Gain–Phase Error

The limitation of the number of estimable sources in the localization of radiation near-field sources with gain–phase error is examined in this paper. When only the reference element has no gain–phase error, a new method based on an accurate model is proposed to enhance the maximum number of estimable sources. Based on the location parameter details of the auxiliary source, the method first derives the gain–phase error estimate matrix. Second, the source steering vector including errors is estimated using the total least square estimating signal parameter via rotational invariance techniques (TLS-ESPRIT), and the time-shifted data matrix is built utilizing the space–time combination idea, thus increasing the degree of freedom of the array. Then, the source steering vector containing the error is modified by the error compensation matrix constructed according to the moment of gain–phase error estimation. Finally, the estimated values of the source position parameters are obtained by using the closed formula of the gain phase of the modified source steering vector and the source position parameters. The experimental results show that the maximum estimable source number of the proposed algorithm is significantly improved compared with the previous results when only the reference array element has no gain–phase error. When the array number is 5 and 9, the maximum estimable source number of the algorithm is 9 and 17, respectively.

Parameter Estimation of Power System Oscillation Signals under Power Swing Based on Clarke–Discrete Fourier Transform

Accurate knowledge of oscillation parameters (i.e., frequency, amplitude, phase, and damping factor) is crucial for control strategies of power systems under power swing. This paper presents a method for the parameter estimation of power system oscillation signals under power swing based on Clarke–DFT. The proposed method provides accurate parameter estimation of damped sinusoidal signals for both balanced and unbalanced systems, which performs well even in the presence of harmonics. In the meantime, the negative frequency components in the spectra of the damped sinusoidal signals, which are caused by system imbalance, are calculated accurately using complex-valued interpolated DFT. To verify the performance of the proposed method, simulations are performed under balanced and unbalanced conditions. The results of the simulations confirm the effectiveness of the proposed method either in unbalanced or harmonic conditions.

Multi-tier dynamic sampling weak RF signal estimation theory

This paper presents a theoretical analysis in discrete time for a multi-tier weak radiofrequency (RF) signal estimation process with N simultaneous signals. Discrete time dynamic sampling is introduced and is shown to provide the capability to extract signal parameter values with increased accuracy compared with accuracy of estimates obtained in prior work. This paper advances phase measurement approaches by proposing discrete time dynamic sampling which our paper shows offers the desirable capability for more accurate weak signal parameter estimates. For N=2 simultaneous signals with a strong signal at 850 MHz and a weak signal at 855 MHz, the results show that dynamically sampling the instantaneous frequency at 24 times the Nyquist rate provides weak signal frequency estimates that are within 1.7 times 10^{-5} of the actual weak signal frequency and weak signal amplitude estimates that are within 428 PPM of the actual weak signal amplitude. Results are also presented for situations with N=2 simultaneous 5G signals. In one case, the strong signal is 3950 MHz, and the weak signal is 3955 MHz; in the other case the strong case is 5950 MHz, and the weak signal is 5955 MHz. The results for these cases show that estimates obtained with dynamic sampling are more accurate than estimates provided using a single sample rate of 65 MSPS. This work has promising applications for weak signal parameters estimation using instantaneous frequency measurements.

A Study on Restoration Plan after Blackout of a System with 100% Inverter Based Resources Considering Bus Inertia

This paper introduces a sequential black-start system restoration procedure in a system with 100% inverter based resources considering system inertia. First, the capability of virtual inertia supported by controlled grid-forming inverters at various locations in a 100% inverter based resources system is calculated through estimation of signal parameters via rotational invariance and nonlinear least square technique; second, a set of radiality constraints is designed for network topology to track inertia value at each bus to find the best restoration paths. Case studies on the modified 39 bus New England system demonstrate that the proposed approach outperforms the existing state-of-the-art approaches in tracking speed and accuracy.