Estimation Of Signal Parameters Research Articles

AoA Estimation for OAM Communication Systems With Mode-Frequency Multi-Time ESPRIT Method

Radio orbital angular momentum (OAM) communications require accurate alignment between the transmit and receive beam directions. Accordingly, a key feature of OAM receivers is the ability to reliably estimate the angle of arrival (AoA) of multi-mode OAM beams. Considering the limitations of existing AoA estimation techniques, in this paper, we propose an easier-to-implement AoA estimation method based on applying multiple times the estimating signal parameters via rotational invariance techniques (ESPRIT) algorithm to the received training signals in OAM mode and frequency domains, which is denoted as the mode-frequency (M-F) multi-time (MT)-ESPRIT algorithm. With this method, the misalignment error of real OAM channels can be greatly reduced and the performance approaches that of ideally aligned OAM channels.

Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

Scalable ESPRIT Processor for Direction-of-Arrival Estimation of Frequency Modulated Continuous Wave Radar

The estimation of signal parameters via rotational invariance techniques (ESPRIT) is an algorithm that uses the shift-invariant properties of the array antenna to estimate the direction-of-arrival (DOA) of signals received in the array antenna. Since the ESPRIT algorithm requires high-complexity operations such as covariance matrix and eigenvalue decomposition, a hardware processor must be implemented such that the DOA is estimated in real time. Additionally, the ESPRIT processor should support a scalable number of antenna configuration for DOA estimation in various applications because the performance of ESPRIT depends on the number of antennas. Therefore, we propose an ESPRIT processor that supports two to eight scalable antenna configuration. In addition, since the proposed ESPRIT processor is based on multiple invariances (MI) algorithm, it can achieve a much better performance than the existing ESPRIT processor. The execution time is reduced by simplifying the Jacobi method, which has the most significant computational complexity for calculating eigenvalue decomposition (EVD) in ESPRIT. Moreover, the ESPRIT processor was designed using hardware description language (HDL), and an FPGA-based verification was performed. The proposed ESPRIT processor was implemented with 10,088 slice registers, 18,207 LUTs, and 80 DSPs, and the slice register, LUT, and DSP were reduced by up to 71.45%, 54.5%, and 68.38%, respectively, compared to the existing structure.

Joint angle and range estimation for bistatic FDA-MIMO radar via real-valued subspace decomposition

The frequency diverse array (FDA) is mainly applied to achieving target localization under the complicated electromagnetic jamming condition. The bistatic multiple-input-multiple-output (MIMO) radar with FDA has gained comprehensive attention in recent years. An innovative method covering the transmitting subarray scheme and the unitary estimation of signal parameters via rotational invariance technology (ESPRIT) is proposed for joint direction of departure (DOD), direction of arrival (DOA), and range estimation. First, a non-overlapping subarray scheme is designed for the transmitting array to decouple DOD and range. For the purpose of enhancing the estimation accuracy and reducing the computational complexity, the real-valued rotational invariance matrix is obtained via applying the unitary transformation to the extended data. The removal method is proposed to avoid the periodic phase ambiguity. The pairing method is put forward to achieving the match of three-dimensional parameter (DOD, DOA and range) for multiple targets. The computational complexity of the proposed algorithm and conventional ESPRIT algorithm are also provided. Finally, extensive experiment results indicate that the proposed algorithm shows the superior performance than the ESPRIT algorithm.

A novel [formula omitted] norm method for LFM signal detection and parameter estimation: Principle, algorithm and application

Linear frequency modulation (LFM) signal is a typical non-stationary signal, its detection and parameter estimation play a key role in many fields. In this paper, a novel ℓp- norm method is firstly proposed for LFM signal detection and parameter estimation. Theoretical analysis and simulation experiments show that the proposed method can detect the signal effectively, and then obtain the estimated parameters with high accuracy and stability. Moreover, the proposed parameter estimation method is successfully applied to interferometry, the measurement errors are lower than that of existing FRFT-based methods.

Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data

AbstractThis paper studies the problem of parameter estimation for the multifrequency sine signals, which have multiple characteristic parameters such as the amplitudes, phases, and frequencies. It is noted that the signal output is nonlinear with respect to the phase and frequency parameters while it is linear with respect to the amplitude parameters. This feature inspires us to separate all of the characteristic parameters into a linear parameter set and a nonlinear parameter set, where the linear set is composed of the amplitude parameters and the nonlinear set is composed of the phase parameters and the frequency parameters. After the parameter separation, two identification submodels are constructed for optimizing the linear parameter set and the nonlinear parameter set. Then the nonlinear identification model becomes a linear identification submodel and a nonlinear identification submodel. Therefore, the nonlinear optimization for minimizing the objective function is converted into the combination of the quadratic optimization and nonlinear optimization. Based on the separable identification submodels, a recursive least squares subalgorithm and a recursive gradient subalgorithm are proposed for identifying the linear parameters and nonlinear parameters, respectively. Moreover, an interactive estimation algorithm is designed to remove the related parameter sets between the subalgorithms and a hierarchical identification method is presented by combining the subalgorithms. For the purpose of tracking the time‐varying, a forgetting factor is introduced to improve the convergence speed. The numerical examples are provided to qualify the performance of the proposed method based on some performance measures.

Factor extraction using Kalman filter and smoothing: This is not just another survey

Dynamic factor models have been the main “big data” tool used by empirical macroeconomists during the last 30 years. In this context, Kalman filter and smoothing (KFS) procedures can cope with missing data, mixed frequency data, time-varying parameters, non-linearities, non-stationarity, and many other characteristics often observed in real systems of economic variables. The main contribution of this paper is to provide a comprehensive updated summary of the literature on latent common factors extracted using KFS procedures in the context of dynamic factor models, pointing out their potential limitations. Signal extraction and parameter estimation issues are separately analyzed. Identification issues are also tackled in both stationary and non-stationary models. Finally, empirical applications are surveyed in both cases. This survey is relevant to researchers and practitioners interested not only in the theory of KFS procedures for factor extraction in dynamic factor models but also in their empirical application in macroeconomics and finance.

Additional Damping Control of a Hybrid Multi-infeed DC System with a Wind Farm

Background: In a hybrid multi-infeed direct current (HMIDC) system, the interaction between the AC and DC systems will have an influence on the damping characteristics of the system, while a doubly-fed induction generator (DFIG) -based wind turbine connected to the grid complicates the coupling between AC and DC. Objective: Based on the basic principles of wind power generation and low-frequency oscillation (LFO), a DFIG -based wind turbine is respectively connected to the LCC-HVDC side and VSC-HVSC side of an HMIDC system to study the influence of the oscillation mode on a hybrid system with different wind power access locations, and two kinds of additional DC damping controllers are designed to suppress the LFO. Comparing the effects of different additional DC damping controllers on the suppression of the LFO in the system. Methods: First, the total least squares- estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) is used to obtain the system’s oscillation mode. Second, the transfer function is determined by the Butterworth bandpass filter. Third, the LCC-HVDC and VSC-HVDC additional damping controllers are designed based on the H2/H∞ hybrid control theory. Results: The designed additional DC damping controller has a good suppression effect on LFO in the HMIDC system and can meet the system damping deficit. The effect of the LCC-HVDC additional damping controller is better than that of VSC-HVDC. Conclusion: Through a simulation analysis on PSCAD/EMTDC simulation software, the effectiveness of the designed additional DC damping controller is verified.

Source localization for underwater acoustics using esprit algorithm

In order to perform the underwater source localization, vector sensors play a major role. If vector sensors are used as an array, the gain can be improved and the accuracy can also be improved. In furtherance of finding the direction of arrival (DOA) of the sound signal which is nothing but the source localization, there are several algorithms like MVM, MUSIC, etc., are used. These algorithms have some computational complexity and also the resolution performance is not good. In encouragement of accomplishing superior determination and to decrease the computational complexity, the Estimation of Signal Parameters through Rotational Invariance Transform (ESPRIT) algorithm is utilized in this paper. This paper gives the details about ESPRIT algorithm and the azimuth angle obtained from the experimental data.

DOA estimation based on fractional low-order multi-sensor time-frequency analysis in heavy tailed noise

In order to improve the performance of Direction-of-arrival (DOA) estimation in heavy-tailed noise, a novel DOA estimation method based on fractional low-order multi-sensor time-frequency distribution is proposed. We first use the high-resolution of the fractional low-order multi-sensor time-frequency analysis (FLOM-MTFA) to obtain the time-frequency matrices of the array signal. Then, the high-energy points of the fractional low-order multi-sensor time-frequency matrices is selected to improve the MSNR. We finally compute the averaged FLOM-MTFA matrix and exploit estimation signal parameter via rotational invariance techniques to estimate DOA. Theoretical analysis and simulation results show that the proposed method can effectively estimate DOA in heavy tailed noise.

Calibration for Parameter Estimation of Signals with Complex Noise via Nonstationarity Measure

The signals in numerous complex systems of engineering can be regarded as nonlinear parameter trend with noise which is identically distributed random signals or deterministic stationary chaotic signals. The commonly used methods for parameter estimation of nonlinear trend in signals are mainly based on least squares. It can cause inaccurate estimation results when the noise is complex (such as non-Gaussian noise, strong noise, and chaotic noise). This paper proposes a calibration method for this issue in the case of single parameter via nonstationarity measure from the perspective of the stationarity of residual sequence. Some numerical studies are conducted for validation. Results of numerical studies show that the proposed calibration method performs well for various models with different noise strengths and types (including random noise and chaotic noise) and can significantly improve the accuracy of initial estimates obtained by least squares method. This is the first time that the nonstationarity measure is applied to the parameter calibration. All these results will be a guide for future studies of other parameter calibrations.

Identification of Joint Discrepancy in Steel Truss Bridge Using Hilbert Transform with root-MUSIC and ESPRIT Techniques

In the present paper, the first three modal frequencies obtained through recorded vibration signal of a steel truss bridge is investigated at three different vehicular speeds. The denoising and extraction of modal frequencies from vibration response signals of steel truss bridge is performed using Hilbert transform (HT) in combination with high-frequency resolution enhancing techniques. The modal frequency extracting techniques applied includes Fast Fourier transform (FFT), multiple signal classification (MUSIC), and estimation of signal parameters by rotational invariance technique (ESPRIT). The comparisons of the outcomes of HT-FFT, HT-root-MUSIC, and HT- ESPRIT are computed. It is observed that HT-FFT computed results with the low resolution which hindered in obtaining distinct modal frequencies while HT-MUSIC and HT-ESPRIT achieved the most denoised, robust and reliable outcomes. The HT-ESPRIT outperforms HT-MUSIC in the elimination of unwanted noise. Further, the application of the sliding window HT-ESPRIT method clearly shows the variation of modal frequencies with the change in vehicular speeds. The first three analytical modal frequencies of 4.56 Hz, 10.44 Hz and 16.66 Hz frequencies are compared with the experimental frequencies obtained through vibrations at 10 km/h, 20 km/h and 30 km/h vehicular speeds, respectively. The joints (also denoted as nodes) 6, 7, and 8 exhibited irregular behaviour with no or minimum frequency peaks due to increased flexibility of member at these locations.

DOA and Polarization Estimation for Non-Circular Signals in 3-D Millimeter Wave Polarized Massive MIMO Systems

In this article, a multiple signal classification (MUSIC) based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signals in three-dimensional (3-D) millimeter wave polarized massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signals or the DOA for non-circular signals by using spectrum search. By contrast, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. Moreover, only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, the DOA estimation can be achieved based on the derivation principle. Then the closed-form expression of the polarization estimation can be obtained based on the chain rule of the derivation w.r.t. the polarization parameters. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC has much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when large-scale/massive MIMO systems are employed.

Laser frequency scanning interferometry based on estimating signal parameters via rotational invariance technique

The laser frequency scanning interferometry, as a non-contact method, has non-ranging blind zone and achieves multi-target testing in a single measurement. The beat frequency of target can be extracted by Fourier transform, and then the distance can be solved. However, due to the limitation of laser frequency modulation bandwidth, the resolution of target obtained by Fourier transform is limited to the inherent resolution. In order to solve this problem, in this paper we propose to use the estimating signal parameter via rotational invariance technique (ESPRIT) to perform spectrum analysis on the measured signal. In the experiment, the resampling method is adopted to correct the non-linearity of the measured signal beat frequency, and then the ESPRIT algorithm is used to obtain the target distance. The results show that the Fourier transform algorithm cannot distinguish the target signal from the frequencies of adjacent target, but the ESPRIT algorithm can do. The thickness of the measured target is 2.08 mm. This provides ideas for measuring, such as damage point in the proximity of the fiber, height of thin step, or small hole.

A Novel ESPRIT Algorithm for Analysis of Low Frequency Oscillations in Power System

With rapidly increasing load and randomly varying load scenario in a densely connected power system may lead to low-frequency oscillations which can further make a power system unstable. To keep the power system in stable operating limits identifying such dominant low-frequency oscillations is essential. Numerous signal processing techniques are used to detect low-frequency oscillations in a power system. Different techniques have their advantages and disadvantages. Estimation of signal parameters via rotational invariance technique (ESPRIT) is a signal processing technique by which low-frequency oscillations can be identified even under noisy conditions . In this paper, a novel ESPRIT algorithm for identifying low-frequency oscillations is proposed. This methodology is based on the ESPRIT algorithm, whose key concept is that real modes existing in oscillations emerge consistently regardless of the algorithm's order. The performance of this algorithm is verified through a set of synthetic signals generated in MATLAB with different noise levels and varying PMU rates and compared with traditional Prony algorithm. The performance of the proposed algorithm is also verified on real-time test data obtained from the Western Electricity Coordinating Council (WECC). This technique performs fairly well in finding low-frequency oscillation characteristics in the power grid.

ПОТЕНЦИАЛЬНЫЕ ВОЗМОЖНОСТИ МЕТОДА МАКСИМАЛЬНОГО ПРАВДОПОДОБИЯ

In this paper, the potential possibilities of the maximum likelihood method for solving statistical problems of radio engineering are analyzed. A condition is introduced for the correlation interval of a random vector of parameters of a set of signals, which expands the scope of the maximum likelihood method under conditions of a priori and parametric uncertainty. Proofs are given that the known methods of spectral, correlation analysis, and angular spectral analysis are special cases of the maximum likelihood method. The scope of their application for signal processing is limited. They determine the optimal estimates of the signal parameters only when the adopted implementation contains a single signal. The substantiation of the possibility of obtaining solutions to statistical problems of radio engineering in the field of nonorthogonality of signals, when spectral lines, correlation functions, radiation patterns partially coincide, is given. The issues of solving the problem of separate detection of a set of signals based on the proposed optimal receiver are discussed. The issues of separate estimation of signal parameters in the area of their nonorthogonality are discussed. The conditions of the maximum resolution of signals are analyzed. The possibility of creating maximum likelihood filters based on likelihood equations is discussed. It is shown that such filters allow separating signals in the area of their nonorthogonality. They are the basis for solving the problem of channel sealing in communication systems. They make it possible to develop communication systems with nonorthogonal carrier frequencies. A concrete example shows the possibility of exceeding the speed of information transmission in nonorthogonal communication systems in comparison with the maximum speed, which follows from the Shannon theorem. The paper presents the results of model calculations illustrating the provisions of the theory.

Passive Localization of Moving Target with Channel State Information

With the popularity of wireless networks and smart devices, wireless signal‐based passive target sensing and localization have become a hot research topic and attracted numerous researchers’ interests. The existing passive localization solutions require multiple receivers, which is not practical for real‐world applications. In response to this compelling problem, in this paper, we propose a practical single access point‐based passive moving target localization system. Concretely, it first utilizes multiple antennas of the access point to form an antenna array and extended antenna, to capture channel state information (CSI) at different spatial locations. Then, leveraging the obtained CSI, the signal parameters, including the angle of arrival (AoA) and time of flight (ToF), are estimated. Based on the estimated signal parameters and the locations of the antenna array and extended antenna, finally, the passive localization of the moving target is realized. Comprehensive experiments are conducted under the real‐world scenario with two different test platforms, and the experimental results show the proposed algorithm’s median localization can reach 1.087 m when the number of antennas is 4 and the signal bandwidth is 80 MHz, demonstrating the effectiveness of the proposed algorithm.

A Neural Network Method for Nonconvex Optimization and its Application on Parameter Retrieval

Parameter retrieval is a typical nonconvex optimization problem in a wide range of research and engineering fields. Classic methods tackle the parameter retrieval problem by feature extraction from the subspace or transform domain. In this paper, we proposed a network-based method to directly solve the nonconvex optimization problem on parameters estimation of complex exponential signals, with no requirement of labeled data. The proposed network has an architecture similar to the Autoencoder network but with the decoder sub-network replaced by a complex exponential signal generator. After training the network to fit the signal parameters to the acquired data, one could obtain the parameters, i.e., frequencies, decay rates, and intensities, and reconstruct the signal. By this work, we show that with a simple application of a lightweight neural network, nonconvex optimization problems like parameter retrieval can be solved efficiently, even without any intricately designed algorithms. We also discuss the robustness of the network-based method by repeated experiments and present the failure cases to indicate the limitations of this method.

Application of Rao-Cramer boundary for estimation of signal parameters in technical information protection systems for detection of spy unmanned aerial vehicles

The paper considers the problem of detecting the position of unmanned aerial vehicles, which are equipped with appropriate equipment for unauthorized receipt of confidential information at the object of information activities. The Rao-Cramer boundary was used to determine the bearing angle of spy unmanned aerial vehicles using annular, arcuate and similar antenna arrays for radio direction finding using the maximum likelihood method and Music. Rao-Cramer limits are calculated for the location of signals in azimuth and for the signal-to-noise ratio. The dependence of the standard deviation for azimuth and angular direction finding in the presence of noise is obtained.

CBIR using content frequency and colour features

Due to the diversity of the image content, we propose in this study a new technique for content-based image retrieval (CBIR) to characterise the image. In this scenario, all images are characterised by their frequency content and their colour information. Indeed, using the high resolution spectral analysis methods, especially the 2D estimation of signal parameters via rotational invariance techniques (ESPRIT) we extract from the image its content frequency and with statistical moment its colour information in order to construct a new vector descriptor. The experimental results applied to the coil-100 database show the robustness of our approach, the precision average can be reaches 90.57%.