Class Of Estimation Algorithms Research Articles

Angular velocity observer design using vector measurements with application to attitude tracking

AbstractThis article addresses the design of angular velocity observer for rigid bodies using vector measurements directly. The main contributions of our observer compared with the classical estimation algorithms lie in that it does not need to reconstruct the attitude information from the measurements nor be implemented with velocity sensors. Meanwhile, it provides three‐degrees‐of‐freedom (3DOF) full state estimates. To avoid the unstable critical points for the attitude estimation errors on SO(3), the synergistic potential functions based hybrid method is adopted. Afterwards, an output feedback attitude tracking controller with a proportional‐derivative (PD) plus feedforward form is built based on the estimates from the observer, which can also be implemented using vector measurements directly. Rigorous analysis of the stability of the observer and the combined observer‐controller are presented in detail, with a separation property shown between the observer and the controller. Simulations are conducted to illustrate the effectiveness of the proposed observer and the combined attitude tracking law.

An automated multi-layer perceptron discriminative neural network based on Bayesian optimization achieves high-precision one-source single-snapshot direction-of-arrival estimation

This paper proposes an innovative global solution which is a pioneering work applying automated machine learning algorithms to remarkable precision sparse underwater direction-of-arrival (DOA) estimation that views the subaquatic sparse-sampling DOA estimation problem as a classification prediction task. The proposed solution, termed automated multi-layer perceptron discriminative neural network (AutoMPDNN), is built upon a Bayesian optimization framework. AutoMPDNN transforms sparsely sampled time-domain signals into the complex domain, preserving essential components in a one-source single-snapshot scenario. Leveraging Bayesian optimization principles, the algorithm embeds necessary hyperparameters into the loss function, effectively defining it as a maximum likelihood problem using the upper confidence bound function and incorporating sparse signal features. We also explore the model space architecture and introduce variants of AutoMPDNN, denoted as AutoMPDNNs_ln (n = 2,3,4). Through a series of plane wave simulation experiments, it is demonstrated that AutoMPDNN achieves the highest prediction performance for one-source single-snapshot scenarios compared to classical DOA estimation algorithms that incorporate sparse representation approaches, as well as contemporary deep learning DOA methods under varying conditions.

Angular and linear velocity observer design using vector and landmark measurements

AbstractThis paper presents an approach for designing angular and linear velocity observers for rigid bodies using two inertial vectors and one landmark measurements directly. Compared with the classical velocity estimation algorithms, the proposed observer does not need to reconstruct the pose information from measurements nor be coupled with velocity sensors. The linear‐time varying dynamics of the estimation error equation are analyzed, rigorously showing the uniform local exponential stability of the observer. Simulations are conducted to illustrate the significant performance of the proposed method.

The expectation-maximization algorithm for autoregressive models with normal inverse Gaussian innovations

In this paper, we study the autoregressive (AR) model with normal inverse Gaussian (NIG) innovations. The NIG distribution is semi heavy-tailed and is helpful in capturing the extreme observations present in the data. The expectation-maximization (EM) algorithm is used to estimate the parameters of the considered AR(p) model. The efficacy of the estimation procedure is shown on the simulated data for AR(2) and AR(1) models. A comparative study is presented, where the classical estimation algorithms are also incorporated, namely, Yule-Walker and conditional least squares methods along with EM method for model parameter estimation. In simulation study, the maximum likelihood estimation (MLE) of NIG distribution by EM algorithm and iterative Newton-Raphson method are also compared. The real-life applications of the introduced model are demonstrated on the NASDAQ stock market index data and US gasoline price data. The studies show that AR(1) model with NIG residuals is good fit for financial data with extreme values as well as for gasoline price data.

Hybrid-Field Channel Estimation for Extremely Large-Scale Massive MIMO System

Hybrid-field channel estimation for extremely large-scale massive MIMO (XL-MIMO) system is discussed in this letter. By exploiting the structural characteristics of far-field path components in the angle domain and the sparsity of near-field path components in the polar domain, a channel estimation algorithm combining support detection and orthogonal matching pursuit (SD-OMP) is proposed. Specifically, the supports of the far-field path components are firstly detected according to components structure characteristics of the angle domain in the XL-MIMO system and then can be used to obtain the far-field path components. Next, effect on the XL-MIMO system induced by far-field path components can be removed firstly and the OMP algorithm is employed to obtain the near-field path components by exploiting its polar domain sparsity. Finally, the hybrid-field channel is recovered by superposing the given far-field path components and near-field path components. Experiment results show that the proposed algorithm can accurately recover channel with relatively low pilot overhead and computation complexity comparing with some classical channel estimation algorithms.

Performance analysis of deep neural networks for direction of arrival estimation of multiple sources

AbstractRecently, popular machine learning algorithms have successfully been applied to the direction of arrival (DOA) estimation. An implementation of determination of DOA estimation is presented based on deep neural networks (DNNs) to reduce the computational complexity of traditional superresolution DOA estimation methods. The classical DOA estimation algorithms have limitations due to unforeseen effects, such as array perturbations. Instead of computing an inverse mapping based on the incomplete forward mapping that relates the signal directions to the array outputs, the DOA problem is approached as a mapping, which can be approximated using a suitable DNN trained with input output pairs. The neural network architecture is based on a multilayer perception and a group of parallel DNNs to perform detection and DOA estimation, respectively. Simulation results are performed to investigate the effect of network parameters on estimation accuracy so that they can be roughly determined in the case of one signal scenario. Based on a set of simulations and experimental measurements, the performance of the optimum network is also assessed and compared to that of the classical DOA estimation methods for multiple signals. It has been shown that the proposed method can not only achieve reasonably high DOA estimation accuracy, but also dramatically reduce the computational complexity and the memory space.

DOA Estimation of Radar Array Signals Based on Blind Source Separation

Target orientation estimation is the premise to achieve target positioning and tracking. The classical DOA estimation algorithm needs to know the array structure and other informations. When those information cannot be obtained accurately, the performance of the algorithm will be degraded or even invalid. In this paper, a DOA estimation method for array signals based on blind source separation is proposed. The received mixed signals are separated by blind source separation, and the array steering matrix is estimated. Then the signal receiving channel is dimensionally reduced and transformed, and the covariance matrix is reconstructed. Finally use the MUSIC algorithm to achieve DOA estimation. Through simulation experiments, the results show that the method can realize DOA estimation under the condition of unknown array steering matrix, and it is robust.

Adaptive State Observer for Linear Time-Varying System with Partially Unknown State Matrix and Input Matrix Parameters

In this paper the problem of adaptive state observer synthesis for linear time-varying SISO (single-input-single-output) dynamical system with partially unknown parameters was considered. It is assumed that the input signal and output variable of the system are measurable. It is also assumed that the state matrix of the plant contains known variables and unknown constants when the input matrix (vector) is unknown. Observer synthesis is based on GPEBO (generalized parameter estimation based observer) method proposed in [1]. Observer synthesis provides preliminary parametrization of the initial system and its conversion to a linear regression model with further unknown parameters identification. For identification of the unknown constant parameters classical estimation algorithm — least squares method with forgetting factor — was used. This approach works well in cases, when the known regressor is " frequency poor" (i.e. the regressor spectrum contains r/2 harmonics, where r is a value of the unknown parameters) or does not meet PE (persistent excitation) condition. To illustrate performance of the proposed method, an example is provided in this paper. A time-varying second-order plant with four unknown parameters was considered. Parametrization of the initial dynamical model was made. A linear static regression with six unknown parameters (including unknown state initial conditions vector) was obtained. An adaptive observer was synthesized and the simulation results were provided to illustrate the purpose reached. The main difference with the results, that were published earlier in [2], is the new assumption that not only does the state matrix of the linear time-varying system contain unknown parameters, but input matrix (vector) contains unknown constant coefficients.

Quantum-enhanced algorithms for classical target detection in complex environments

Quantum computational approaches to some classic target identification and localization algorithms, especially for radar images, are investigated, and are found to raise a number of quantum statistics and quantum measurement issues with much broader applicability. Such algorithms are computationally intensive, involving coherent processing of large sensor data sets in order to extract a small number of low profile targets from a cluttered background. Target enhancement is accomplished through accurate statistical characterization of the environment, followed by optimal identification of statistical outliers. The key result of the work is that the environmental covariance matrix estimation and manipulation at the heart of the statistical analysis actually enables a highly efficient quantum implementation. The algorithm is inspired by recent approaches to quantum machine learning, but requires significant extensions, including previously overlooked `quantum analog--digital' conversion steps (which are found to substantially increase the required number of qubits), `quantum statistical' generalization of the classic phase estimation and Grover search algorithms, and careful consideration of projected measurement operations. Application regimes where quantum efficiencies could enable significant overall algorithm speedup are identified. Key possible bottlenecks, such as data loading and conversion, are identified as well.

Earthquake magnitude and location estimation from real time seismic waveforms with a transformer network

SUMMARY Precise real time estimates of earthquake magnitude and location are essential for early warning and rapid response. While recently multiple deep learning approaches for fast assessment of earthquakes have been proposed, they usually rely on either seismic records from a single station or from a fixed set of seismic stations. Here we introduce a new model for real-time magnitude and location estimation using the attention based transformer networks. Our approach incorporates waveforms from a dynamically varying set of stations and outperforms deep learning baselines in both magnitude and location estimation performance. Furthermore, it outperforms a classical magnitude estimation algorithm considerably and shows promising performance in comparison to a classical localization algorithm. Our model is applicable to real-time prediction and provides realistic uncertainty estimates based on probabilistic inference. In this work, we furthermore conduct a comprehensive study of the requirements on training data, the training procedures and the typical failure modes. Using three diverse and large scale data sets, we conduct targeted experiments and a qualitative error analysis. Our analysis gives several key insights. First, we can precisely pinpoint the effect of large training data; for example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Secondly, the basic model systematically underestimates large magnitude events. This issue can be mitigated, and in some cases completely resolved, by incorporating events from other regions into the training through transfer learning. Thirdly, location estimation is highly precise in areas with sufficient training data, but is strongly degraded for events outside the training distribution, sometimes producing massive outliers. Our analysis suggests that these characteristics are not only present for our model, but for most deep learning models for fast assessment published so far. They result from the black box modeling and their mitigation will likely require imposing physics derived constraints on the neural network. These characteristics need to be taken into consideration for practical applications.

Performance Evaluation of COVID-19 Proximity Detection Using Bluetooth LE Signal.

The risk of COVID-19 transmission increases when an uninfected person is less than 6 ft from an infected person for longer than 15 minutes. Infectious disease experts working on the COVID-19 pandemic call this high-risk situation being Too Close for Too Long (TCTL). Consequently, the problem of detecting the TCTL situation in order to maintain appropriate social distance has attracted considerable attention recently. One of the most prominent TCTL detection ideas being explored involves utilizing the Bluetooth Low-Energy (BLE) Received Signal Strength Indicator (RSSI) to determine whether the owners of two smartphones are observing the acceptable social distance of 6 ft. However, using RSSI measurements to detect the TCTL situation is extremely challenging due to the significant signal variance caused by multipath fading in indoor radio channel, carrying the smartphone in different pockets or positions, and differences in smartphone manufacturer and type of the device. In this study we utilize the Mitre Range Angle Structured (MRAS) Private Automated Contact Tracing (PACT) dataset to extensively evaluate the effectiveness of Machine Learning (ML) algorithms in comparison to classical estimation theory techniques to solve the TCTL problem. We provide a comparative performance evaluation of proximity classification accuracy and the corresponding confidence levels using classical estimation theory and a variety of ML algorithms. As the classical estimation method utilizes RSSI characteristics models, it is faster to compute, is more explainable, and drives an analytical solution for the precision bounds proximity estimation. The ML algorithms, Support Vector Machines (SVM), Random Forest, and Gradient Boosted Machines (GBM) utilized thirteen spatial, time-domain, frequency-domain, and statistical features extracted from the BLE RSSI data to generate the same results as classical estimation algorithms. We show that ML algorithms can achieve 3.60%~19.98% better precision, getting closer to achievable bounds for estimation.

Three-Point Synchrophasor Estimator With Better Off-Nominal Frequency Leakage Reduction

Synchronous phasor measurements have long been an important tool in power flow modelling and network state estimation as well as in many other tasks that support power grid observability. Classic phasor estimation algorithms are often preferred over complex alternatives. They are based on a variant of the Discrete Fourier Transform (DFT) while relying on a sampling frequency locked to the system nominal frequency. The spectral leakage caused by the small off-nominal frequency excursions in such settings introduces errors that are handled by the estimation algorithms to a different extent. Well-known three-point averaging filter (3P) is a low-cost spectral leakage reduction technique in one of the simplest estimators based on a single-bin DFT. However, it only partially removes the oscillating components. When an independent standard-based frequency tracking is available, the errors can be further compensated with minimal cost. We propose a frequency-corrected three-point estimator (F3P) to eliminate the induced spurious spectrum progressively with the increasing sampling rate while building on the analytical expression of the remaining error. We show performance benefits of the F3P under finite sampling rates when compared to the baseline 3P and to the classical Interpolated DFT (IPDFT) in static conditions. The F3P removes most of the leakage without the need of data resampling. In less ideal conditions with additive 50-dB noise, up to a 10-fold reduction of the 3P error is achieved. The error ratio of the reference IPDFT over the F3P is approximately 1.4 under the same conditions and largely independent of the frequency excursion. The presented simulation results prove the algorithm's superior performance in most of the steady-state tests defined by the relevant Standard, whereas the dynamic performance can be brought to an acceptable level by the use of a single-cycle observation window, with the response times comparable to those of the IPDFT.

Genetic algorithms and bundle adjustment for the enhancement of 3D reconstruction

In this paper, we present a new technique of tridimensional reconstruction from a sequence of uncalibrated stereo images taken with cameras having varying parameters. At first, our system allows to recover initial coordinates of a set of 3D points. In this context, we have used our method of self-calibration based on the use of unknown 3D scene with its image projections and genetic algorithms to estimate all intrinsic parameters. After that extrinsic parameters are estimated based on classical pose estimation algorithms. Matching points and estimated value of intrinsic and extrinsic parameters are used to estimate initial 3D model that helps us in the initialization step. In order to have a reliable and relevant 3D reconstruction the proposed method is based on good and new exploitation of bundle adjustment (without camera poses initialization) technique based on Levenberg-Marquardt optimization with the aim to estimate our optimal 3D model that has special features compared to the classical case because it masks the pose parameters estimation in the optimization process. Finally, 3D mesh of the 3D scene is constructed with Delaunay algorithm and the 2D image is projected on the 3D model to generate the texture mapping. Experiments is conducted on real data to achieve demonstrate the validity and the performance of the proposed approach in terms of convergence, simplicity, stability and reconstruction quality.

PolInSAR Complex Coherence Nonlocal Estimation Using Shape-Adaptive Patches Matching and Trace-Moment-Based NLRB Estimator

The traditional nonlocal estimations have been demonstrated to be effective and widely used in polarimetric synthetic aperture radar interferometry (PolInSAR) data. However, there still exist some problems about two key steps: 1) in the homogeneous pixels selection step, the regular square (RS) patches matching strategy shows the limited performance in textured area and 2) in the central pixel value estimation from the selected pixels, the well-known Lee estimator, which only uses the intensity statistic, tends to be unstable. To overcome these restrictions, we put forward two robust strategies and then propose an improved PolInSAR complex coherence nonlocal estimation: 1) the shape-adaptive (SA) patch is utilized for flexibly matching the similar pixels in a large search window, which is constructed by combining the likelihood ratio test (LRT) and the region growing (RG) algorithm and 2) the trace-moment-based nonlocal reduced bias (TMB-NLRB) estimator is employed, which considers the interchannel correlations and evaluates more accurately the homogeneity level between the selected pixels. The denoising effect of both strategies is quantitatively analyzed on the simulated data set, and the proposed algorithm is compared with classical estimation algorithms on a TerraSAR-X/TanDEM-X PolInSAR data set. These experimental results show that the proposed method provides better performance in speckle reduction, detail preservation, and complex coherence estimation.

Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection

Ship detection is of considerable significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) with region proposal mechanism, e.g., Faster R-CNN, performs outstandingly in ship detection with high-resolution images. However, the accuracy limitation is induced by the region proposal restricted by the training set for multi-scale target detection. Therefore, the method of multi-scale ship object detection is proposed based on saliency estimation in our work. Saliency Estimation Algorithms (SEAs) are often used to extract saliency features in images. In ship detection of high-resolution remote sensing images, these algorithms can extract information such as the scale and position of the targets, and then help the DCNN-based ship detection method to obtain better performance. To verify the effectiveness of the saliency estimation algorithm in multi-scale ship detection of high-resolution remote sensing images, and analyze the advantages of different SEAs. This paper introduces 13 classic saliency estimation algorithms and 2 DCNN-based ones to compare them by using evaluation indicators. At last, in order to demonstrate the performance of different SEAs, the extracted saliency feature maps are used to assist the DCNN-based target detection under multi-scale ships condition. In general, this framework can improve the detection accuracy of large-scale ships under scenarios of training only with small scale ships or without enough datasets.

Carrier-phase estimation for simultaneous quantum key distribution and classical communication using a real local oscillator

The simultaneous quantum and classical communication (SQCC) protocol is an appealing protocol since it allows continuous-variable quantum key distribution (CV-QKD) and classical communication to be implemented simultaneously by using the same communication infrastructure and on a single wavelength. However, the main challenge of the SQCC protocol is its low tolerance of phase noise, limiting the transmission distance under the real local oscillator (LO) design. To eliminate the phase drift without the aid of the reference signal, the classical carrier phase estimation (CPE) algorithm in the field of coherent optical communication is employed for phase recovery and data extraction. Besides, the power of the residual phase noise in classical communication and the power of the excess phase noise in CV-QKD are derived, including the effects of the employed laser linewidth, the transversal filter length, and the classical signal power. On this basis, the performances of the SQCC protocol under the trusted and the untrusted phase noise model are discussed, respectively. Our work confirms the feasibility of the CPE algorithm in the SQCC protocol and demonstrates the conditions required for this protocol to operate on a coherent optical communication system.

A method for efficient maximization of PPS estimation functions

Parameter estimation of polynomial phase signals is usually performed by maximizing the cost function. To reduce the calculation complexity, the maximization consists of coarse and fine estimation stages. To refine coarse estimate, in this paper, we propose a method based on filtering and phase unwrapping of transform function. Simulation experiments have shown excellent results compared to classical fine estimation algorithms such as Aboutanios–Mulgrew and dichotomous search.

A Bandwise Noise Model Combined With Low-Rank Matrix Factorization for Hyperspectral Image Denoising

Hyperspectral image (HSI) is usually polluted by dense additive white Gaussian noise (AWGN). The noise intensity of AWGN is different for different bands of the HSI, and it has been utilized by many denoising algorithms. However, many of them did not convey this phenomenon in the objective function. In this paper, a bandwise noise model is proposed to overcome the problem. Furthermore, based on the low-rank structure of the HSI, the proposed bandwise noise model is combined with the low-rank matrix factorization to obtain a new efficient HSI denoising algorithm. Additionally, as the noise estimation is an important preprocessing step for the proposed algorithm, a classical noise estimation algorithm is improved to increase its efficiency. The proposed algorithm is used on the HSI corrupted by mixed noise. Both simulated and real experiments demonstrate the state-of-the-art performance and efficiency of the proposed algorithm.

A recursive technique for tracking the feasible parameter set in bounded error estimation

SummaryIn this paper, a new recursive algorithm is proposed for tracking parameter changes of a time‐varying linear system. Since a bounded error approach is adopted for both modeling the measurement noise and the parameter change process, the problem addressed amounts to the design of a procedure for updating an estimate of the feasible parameter set. The approximating regions considered are in the form of outbounding orthotopes. The novelty of the approach lies in the use of a selection technique which keeps track only of a special subset of the constraints defining the feasible set. These inequalities represent the binding constraints of suitable linear programs of limited size. The devised algorithm is tested on several numerical examples, showing remarkable performance both in computational burden, which is comparable to that of classical recursive estimation algorithms like recursive least squares (RLS) and quality of the set estimate as compared to alternative techniques available in the literature.

Low‐Infrastructure Real‐Time Embedded Control via Variational Integrators

AbstractThis paper presents a discrete time receding horizon control scheme that leverages the numerical properties of a variational integrator to facilitate real‐time control generation on an embedded system. The variational integrator employed is well‐suited to classical estimation and control algorithms, e.g. LQR, extended Kalman filters, and particle filters. The structure‐preserving properties of this variational integrator lead to increased performance of estimation and control routines, especially in low‐bandwidth applications. Several experimental examples are presented that illustrate the features of this receding horizon control scheme when leveraging the desirable numerical properties of the variational integrator. © 2016 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)