Estimation Of Signal Parameters Research Articles

Recursive ESPRIT Algorithm for Multi-User OAM Low-Overhead AoA Estimation

Multi-user orbital angular momentum (MU-OAM) wireless backhaul systems require precise alignment of transmitted and received OAM beams in line-of-sight (LoS) scenarios. Therefore, a crucial feature for a macro base station (MBS) is to accurately obtain the angle of arrival (AoA) of OAM beams from the small base stations (SBSs) in the system. Considering the limitations of existing OAM-based AoA estimations, we propose a novel super-resolution AoA estimation method, denoted as the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">recursive estimation of signal parameters via rotational invariance techniques (ESPRIT)</i> method. The proposed technique is fundamentally different from traditional ESPRIT algorithm: It is not based on the displacement-invariant array structure, but on the recursive characteristic of Bessel functions in OAM signals. With this method, the inter-mode interference of the LoS MU-OAM system can be significantly suppressed by less training overhead, and the performance is close to that of the ideal system.

Accounting for Acceleration—Signal Parameters Estimation Performance Limits in High Dynamics Applications

The derivation of estimation lower bounds is paramount to designing and assessing the performance of new estimators. A lot of effort has been devoted to the range-velocity estimation problem, a fundamental stage on several applications, but very few works deal with acceleration, being a key aspect in high dynamics applications. Considering a generic band-limited signal formulation, we derive a new general compact form Cramér–Rao lower bound (CRB) expression for joint time-delay, Doppler stretch, and acceleration estimation. This generalizes and expands upon known delay/Doppler estimation CRB results for both wideband and narrowband signals. This new formulation, especially easy to use, is created based on baseband signal samples, making it valid for a variety of remote sensors. The new CRB expressions are illustrated and validated with representative GPS L1 C/A and linear frequency modulated chirp band-limited signals. The mean-square error of a misspecified estimator (conventional delay/Doppler) is compared with the derived bound. The comparison indicates that for some acceleration ranges the misspecified estimator outperforms a well-specified estimator that accounts for acceleration.

Few-shot learning based blind parameter estimation for multiple frequency-hopping signals

Few-shot learning based blind parameter estimation for multiple frequency-hopping signals

LFM Signal Parameter Estimation via FTD-FRFT in Impulse Noise

LFM signals are widely applied in radar, communication, sonar and many other fields. LFM signals received by these systems contain a lot of noise and outliers. In order to suppress the interference of strong impulse noise on target signals and realize the accurate estimation of LFM signal parameters, the impulse noise of echo signals need to be filtered. In this paper, to solve the problem of poor performance of LFM signal parameter estimation based on fractional Fourier transform in impulse noise, alpha stable distribution is used to establish the mathematical model of impulse noise. The proposed fastest tracking differentiator with an adaptive tracking factor is used to suppress the strong impulse noise, and fractional Fourier transform is used to estimate the parameter of the LFM signals. The experimental results show that the proposed fastest tracking differentiator with an adaptive tracking factor has a good filtering performance. It can effectively filter the impulse noise in the echo signal and allows the FrFT method to accurate estimate the parameters of the LFM signals in strong impulse noise.

Harmonic/inter-harmonics detection using modified exact model order-based ESPRIT

Problems related to harmonics/interharmonics are growing rapidly in power system with the increasing use of power electronic devices. This paper focuses on estimating the harmonics/interharmonics using modified exact model order - estimation of signal parameters via rotational invariance technique (MEMO-ESPRIT). The modified exact model order (EMO) method is used to reduce the computational burden in detecting the harmonics/interharmonics components. Comparison has been made between EMO and MEMO to demonstrate the effectiveness of MEMO based method. Moreover, various parameter values like noise, size of autocorrelation matrix, etc. are varied to see their effects on the computational burden and accuracy of MEMO method. The performance of MEMO-ESPRIT algorithm is analysed with synthetic signals, single-phase PWM inverter and 250 kW grid-connected PV arrays using MATLAB/Simulink. The root-mean-square relative error has been obtained to validate the accuracy of the algorithm.

Harmonic and Interharmonic Estimation using Poincaré Filtering Assisted ESPRIT Method

Accurate estimation of harmonics/interharmonics components is a crucial task in the electric power system. The parametric methods available for this purpose like Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) give accurate result but suffer from very high computational burden. While on the other hand non-parametric methods like Fast Fourier Transform (FFT) are fast but have low spectral resolution. This paper proposes a Poincare plot-based periodicity filter to detect the presence of interharmonics and then selecting an algorithm based on the requirement of spectral resolution. The proposed Poincare plot-based method has been thoroughly tested for its accuracy by varying different parameters associated with interharmonics components. The method has been tested on synthetic signals as well as experimental validation has also been performed by analyzing the current signal from aeration blower installed at GIFT city, Gujarat.

Parameter Estimation of Wide-Band FM Signals From Knowledge About Their Sinusoidal Modulation

Parameter Estimation of Wide-Band FM Signals From Knowledge About Their Sinusoidal Modulation

Semantic Learning for Analysis of Overlapping LPI Radar Signals

The increasingly complex radio environment may cause the received low probability of intercept (LPI) radar signals to overlap in time–frequency domains. Analyzing overlapping LPI radar signals requires identifying the modulation type and estimating the parameters of each component. Prior research performs overlapping signal analysis as a multistage task, where each stage is designed to perform a part of the task. The multistage system will increase the calculation burden and cannot be optimized as a whole. Instead, this article proposes a novel framework for analyzing overlapping signals in a single stage. Specifically, we develop a joint semantic learning deep convolutional neural network (JSLCNN) that jointly learns three tasks, i.e., feature restoration, modulation classification, and parameter regression. Since the whole cognitive pipeline is a single network, it can be optimized end-to-end directly on cognitive performance. To verify the validity of the proposed JSLCNN, numerous comparative experiments are carried out in terms of modulation recognition and parameter estimation of overlapping signals. Experimental results demonstrate that the JSLCNN has desirable extensibility for identifying unseen signal combinations and robustness against unknown jamming. Furthermore, we show that the JSLCNN outperforms other existing approaches in generic real-time parameter estimation for LPI radar signals.

Feature Extraction and Analysis of Speech Signal Based on Fractional Fourier Transform

The extraction of speech features is the basis of speech signal processing, and the extraction of speech features is to obtain the parameters representing speech signals through the analysis of speech signals. The shape information of the signal can be extracted and processed by selecting appropriate structural elements and adopting mathematical morphological transformation. Selecting different structural elements will result in different shape transformation results, thus extracting the shape information of different components. In this paper, the feature extraction and analysis of speech signals are further studied based on FRFT (fractional fourier transform). The simulation results show that in different noise environments, such as when the speech is bathed with signal-to-noise ratio of 0dB and 10dB respectively, the recognition rate of this method is higher than that of the traditional parametric method. In this paper, based on the strength of FRFT signal components, the component signals are detected one by one in order, and then according to the detection results, the strongest component signals are removed from the observed signals in order to reduce their influence on the weak component signals, thus improving the effectiveness and reliability of multi-component signal detection and parameter estimation.

DOA Estimation Method in Echo-localization Ability for Human-beings

The principle of echo-localization processing of human beings is quite similar with animals like bats and dolphins, which has been widely applied in sonar. Direction of Arrival (DOA) methods plays an important role in estimating the radar, sonar and many other echo-localization communication fields. Typical methods in resolving echo-localization problems including classical (Multiple Signal Classification) MUSIC, (Estimation of Signal Parameter via Rotational Invariance Technique), ESPRIT and compressed sensing. The echo-localization methods and the resolving ambiguity methods via uniform array are systematically introduced in this paper. On this basis, the fast and accurate algorithms for human-being echo-localization in parameter (azimuth angle, elevation angle and range) estimation of signal are further introduced from time domain and frequency domain. The accuracy of the angle and distance estimation of the algorithm calculated by computer simulation experiment is over 99.9% and 97% respectively. The effectiveness of DOA method was verified according to MUSIC algorithm, which provides a rapid and accuracy information in echo-localization ability of human being.

Performance Analysis of 2D-ESPRIT Algorithm for URA in Estimation of 2D-DOA in Massive-MIMO Systems

Purpose: The performance of smart antenna is affected by antenna array configuration and the direction of arrival estimation (DOA) algorithm used .To have a suitable DOA for an uniform rectangular array (URA),The performance of two dimensional estimation of signal parameters via rotational invariance techniques (2D-SPRIT) that are applied for two different planar dispositions of antenna for URA in Massive Multiple Inputs Multiple Output (Massive MIMO) systems when subjected to the same simulation conditions is comparatively analyzed. The performance of the proposed 2D-ESPRIT algorithm with URA in vertical plane (the case of full-dimensional MIMO) has been compared with that of the existing 2D-ESPRIT algorithm that is applied with URA in horizontal plane.&#x0D; Methodology: Implementation and simulation of analyzed 2D-ESPRIT algorithms was done in MATLAB and compared considering computational complexity efficiency, DOA estimation precision, estimation failure and variation of Root mean square Error (RMSE) with Signal to Noise Ratio (SNR) and angular spread. MATLAB simulation results got from similar input parameters in a multipath environment are compared to derive a conclusion.&#x0D; Findings: Simulation results show that the proposed 2D-ESPRIT algorithm applied in vertical plane outperforms the existing 2D-ESPRIT algorithm applied in horizontal plane for all considered performance criteria that include estimation precision, running time and Root mean square error (RMSE).&#x0D; Recommendation: Other researchers interested in working on 2D-ESPRIT algorithms are recommended to improve the work reported in this paper by further reducing computational complexity observed in 2D-ESPRIT algorithm with URA in vertical plane at large antenna array size to reflect the standard of full-dimension MIMO.

Parameter estimation of harmonics arising in electrical instruments of smart grids using cuckoo search heuristics

The accurate estimation of power signal parameters allows smart grids to optimize power delivery efficiency, improve equipment utilization, and control power flow among generation nodes and loads. However, practically it becomes a challenging task because of the presence of harmonic distortions. In this study, a parameter estimation of the power system harmonics is investigated through swarm intelligence–based optimization strength of the cuckoo search algorithm. The performance evaluation is conducted in detail for different generations and particle sizes and for different signal-to-noise ratios. The simulation results reveal that the cuckoo search optimization heuristic accurately estimates the amplitude and phase parameters of the power system harmonics and is robust against different signal-to-noise ratios.

Parameter estimation method for a linear frequency modulation signal with a Duffing oscillator based on frequency periodicity

In view of the complexity of existing linear frequency modulation (LFM) signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio (SNR), a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper. This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency, and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator. On this basis, the corresponding relationship between the reference frequency of the frequency-aligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method, and the frequency information of the LFM signal is determined. Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of −25 dB.

Concurrent estimation of noise and compact-binary signal parameters in gravitational-wave data

Gravitational-wave parameter estimation for compact binary signals typically relies on sequential estimation of the properties of the detector Gaussian noise and of the binary parameters. This procedure assumes that the noise variance, expressed through its power spectral density, is perfectly known in advance. We assess the impact of this approximation on the estimated parameters by means of an analysis that simultaneously estimates the noise and compact binary parameters, thus allowing us to marginalize over uncertainty in the noise properties. We compare the traditional sequential estimation method and the new full marginalization method using events from the GWTC-3 catalog. We find that the recovered signals and inferred parameters agree to within their statistical measurement uncertainty. At current detector sensitivities, uncertainty about the noise power spectral density is a subdominant effect compared to other sources of uncertainty.

Toeplitz rectification based DOA estimation under the coexistence of mutual coupling and nonuniform noise

It is studied that both the array mutual coupling (MC) and nonuniform noise could degrade direction-of-arrival (DOA) estimators’ performance substantially, and thus it is of necessity to eliminate them before performing DOA estimation. In this paper, two Toeplitz rectification (TR) based methods for DOA estimation under the coexistence of MC and nonuniform noise are proposed. The first method jointly employs the middle subarray scheme and TR to immunize the effects of MC and nonuniform noise, and further applies the estimation of signal parameters via rotational invariance techniques (ESPRIT) to obtain closed-form DOA estimates. This method can yield a good and low-complexity result. The second method first exploits the least squares (LS) criterion to eliminate the nonuniform noise, and then enhances the major part of sample covariance matrix (SCM) via the TR. With this enhanced SCM and subsequent two-stage spectral searches, an improved DOA estimation is provided. Simulation results validate the effectiveness of the proposed two methods.

Measurability of neutron star tidal deformability from merging neutron star-black hole binaries

The neutron star-black hole binary (NSBH) system has been considered one of the promising detection candidates for ground-based gravitational-wave (GW) detectors such as LIGO and Virgo. The tidal effects of neutron stars (NSs) are imprinted on the GW signals emitted from NSBHs as well as binary neutron stars. In this work, we study how accurately the parameter $\lambda_{\rm NS}$ can be measured in GW parameter estimation for NSBH signals. We set the parameter range for the NSBH sources to $[4M_{\odot}, 10M_{\odot}]$ for the black hole mass, $[1M_{\odot}, 2M_{\odot}]$ for the NS mass, and $[-0.9, 0.9]$ for the dimensionless black hole spin. For realistic populations of sources distributed in different parameter spaces, we calculate the measurement errors of $\lambda_{\rm NS}$ ($\sigma_{\lambda_{\rm NS}}$) using the Fisher matrix method. In particular, we perform a single-detector analysis using the advanced LIGO and the Cosmic Explorer detectors and a multi-detector analysis using the 2G (advanced LIGO-Hanford, advanced LIGO-Livingstone, advanced Virgo, and KAGRA) and the 3G (Einstein Telescope and Cosmic Explorer) networks. We show the distribution of $\sigma_{\lambda_{\rm NS}}$ for the population of sources as a one-dimensional probability density function. Our result shows that the probability density function curves are similar in shape between advanced LIGO and Cosmic Explorer, but Cosmic Explorer can achieve $\sim 15$ times better accuracy overall in the measurement of $\lambda_{\rm NS}$. In the case of the network detectors, the probability density functions are maximum at $\sigma_{\lambda_{\rm NS}} \sim 130$ and $\sim 4$ for the 2G and the 3G networks, respectively, and the 3G network can achieve $\sim 10$ times better accuracy overall.

Echo Signal Enhancement for ESPRIT to Estimate Angles of Arrival by Virtually Overlapped Subarray Decomposition in ADAS Radar Systems

Angle of arrival (AoA) for target echo signal detection is crucial in vehicular radar systems, where the estimation of signal parameters via rotational invariance (ESPRIT) provides a high-resolution estimation. A subarray decomposition strategy is presented in this article to create two ESPRIT antenna arrays with enhanced echo signal strengths, applicable to multi-input–multioutput (MIMO) operations for creating a large virtual array. The concept intends to maximize the echo signal’s strength with minimum signal interferences by increasing the aperture sizes of the two ESPRIT antenna arrays. The decomposition produces two virtually overlapped subarrays to increase the subarray aperture sizes in the spatial or spectral representations. It is applied to the MIMO-based arrays for two-way radiation patterns for ESPRIT estimation of AoA. Numerical simulation examples are shown to validate the feasibility and demonstrate the applications.

Research on the Range-Frequency Interference Characteristics of Target Scattering Field in a Shallow Water Waveguide

Based on the target scattering model and the normal mode theory in a shallow water waveguide, a mathematical model of the acoustic intensity under the coupling condition of the target and the environment was deduced, and the interference striations in the monostatic and bistatic configuration were obtained by simulations. Further, a field experiment was carried out in a lake, and the data were collected for a spherical target in two frequency bands, i.e., 20−40 kHz and 40−80 kHz. The experiment results showed good agreement with the simulations. The results of the simulation and the experiment showed that the existence of the target made the interference phenomenon in shallow water waveguides more complex, and the range-frequency interference characteristics were closely related to the configuration of the sonar system, the target scattering function, the frequency range, and the target movement trajectory. These interference phenomena were found and theoretically analyzed in the paper. The research results can be applied to target detection and recognition, signal parameter estimation, target tracking, and other fields.

The multichannel maximum-likelihood (MCML) method: a new approach for infrasound detection and wave parameter estimation

SUMMARY We are presenting a new and novel approach to the detection and parameter estimation of infrasonic signals. Our approach is based on the likelihood function derived from a multisensor stochastic model expressed in different frequency channels. Using the likelihood function, we determine, for the detection problem, the generalized likelihood ratio (GLR) and for the estimation of the slowness vector, the maximum likelihood estimation (MLE). We establish new asymptotic results (i) for the GLR under the null hypothesis leading to the computation of the corresponding p-value and (ii) for the MLE by focusing on the two wave parameters: backazimuth and horizontal trace velocity. The multichannel maximum-likelihood (MCML) detection and estimation method is implemented in the time–frequency domain in order to avoid the presence of interfering signals. Extensive simulations with synthetic signals show that MCML outperforms the state-of-the-art multichannel correlation detector algorithms like the progressive multichannel correlation in terms of detection probability and false alarm rate in poor signal-to-noise ratio scenarios. We also illustrate the use of the MCML on real data from the International Monitoring System and show how the improved performances of this new method lead to a refined analysis of events in accordance with expert knowledge.

Gravitational lensing of gravitational waves: Probability of microlensing in galaxy-scale lens population

ABSTRACT With the increase in the number of observed gravitational wave (GW) signals, detecting strongly lensed GWs by galaxies has become a real possibility. Lens galaxies also contain microlenses (e.g. stars and black holes), introducing further frequency-dependent modulations in the strongly lensed GW signal within the LIGO frequency range. The multiple lensed signals in a given lens system have different underlying macro-magnifications (|μ|) and are located in varied microlens densities (Σ•), leading to different levels of microlensing distortions. This work quantifies the fraction of strong lens systems affected by microlensing using realistic mock observations. We study 50 quadruply imaged systems (quads) by generating 50 realizations for each lensed signal. However, our conclusions are equally valid for lensed signals in doubly imaged systems (doubles). The lensed signals studied here have |μ| ∼ [0.5, 10] and Σ• ∼ [10, 103]M⊙ pc−2. We find that the microlensing effects are more sensitive to the macro-magnification than the underlying microlens density, even if the latter exceeds 103M⊙ pc−2. The mismatch between lensed and unlensed GW signals rarely exceeds 1 per cent for nearly all binary black hole sources in the total mass range [10M⊙, 200M⊙]. This implies that microlensing is not expected to affect the detection or the parameter estimation of such signals and does not pose any further challenges in identifying the different lensed counterparts when macro-magnification is ≤10. Such a magnification cut is expected to be satisfied by ∼50 per cent of the detectable pairs in quads and ∼90 per cent of the doubles in the fourth observing run of the LIGO–Virgo detector network.