Sensor Gain Research Articles

Acoustic Source Localization in the Circular Harmonic Domain Using Deep Learning Architecture

The problem of direction of arrival (DOA) estima- tion with a circular microphone array has been addressed with classical source localization methods, such as the model-based methods and the parametric methods. These methods have an ad- vantage in estimating the DOAs in a blind manner, i.e. with no (or limited) prior knowledge about the sound sources. However, their performance tends to degrade rapidly in noisy and reverberant environments or in the presence of sensor array limitations, such as sensor gain and phase errors. In this paper, we present a new approach by leveraging the strength of a convolutional neural network (CNN)-based deep learning approach. In particular, we design new circular harmonic features that are frequency- invariant as inputs to the CNN architecture, so as to offer improvements in DOA estimation in unseen adverse environments and obtain good adaptation to array imperfections. To our knowledge, such a deep learning approach has not been used in the circular harmonic domain. Experiments performed on both simulated and real-data show that our method gives significantly better performance, than the recent baseline methods, in a variety of noise and reverberation levels, in terms of the accuracy of the DOA estimation.

Modeling and Detection of Phase Current Sensor Gain Faults in PMSM Drives

The fault of current sensors in AC electric drives can cause inaccurate tracking of the control reference and torque oscillations, that can lead to damage of mechanical components. Therefore, the development of accurate detection techniques of these faults plays a crucial role for the proper management of the electric drive and for the fault isolation and estimation. In the paper, a model-based method for the detection of phase current sensor gain faults in a Permanent Magnet Synchronous Motor (PMSM) drive with Field Oriented Control (FOC) is proposed. At first, a mathematical model is presented, which allows an accurate determination of the analytical closed-form expression of the steady-state stator currents, taking into account the effects both of the current control regulators and of any current sensors gain faults. Starting from this model, a low-computation algorithm has been carried out, which allows not only to detect and isolate the current sensors affected by the gain fault, but also to estimate the gain values starting from the measured phase currents and motor speed. The model and the diagnostic algorithm performance is verified by means of numerical and experimental results.

Robust adaptive beamforming algorithm based on damped singular value decomposition regularization

When adaptive beamformers are applied to actual problems, various model mismatches are present in the sample covariance matrix and signal steering vector, which severely deteriorates the performance of the beamformer. To address this issue, we develop a novel adaptive beamforming algorithm in which the weight vector is a linear combination of the presumed steering vector and basis vectors of the orthogonal supplement subspace of the steering vector. Then, we convert the common constrained optimization problem into an unconstrained one to establish the new beamformer. Moreover, we use damped singular value decomposition regularization, which employs the L-curve method to adaptively determine the regularized factor (loading level), for suppressing the effects of model mismatches. In addition, we perform simulations of the proposed algorithm and other existing beamforming algorithms by considering several commonly encountered mismatches (e.g., the direction-of-arrival mismatch, sensor gain, phase error and location perturbation, coherent local scattering, and mutual coupling) and demonstrate the superior performance of the new beamforming algorithm.

Positionally-independent and extended read range resonant sensors applied to deep soil moisture monitoring

Here we detail an inductively coupled extender (ICE) and resonant (LC) sensor to monitor soil moisture using a portable reader. Significant advances of this ICE-LC design are extending typical LC sensor read range over a meter and reducing positional alignment sensitivity between reader and sensor. An analytical model validates the working principle and feasibility of the ICE-LC system. Prototypes of the ICE-LC sensor were built and optimized in terms of sensitivity and power transfer (single and four turns for ICE top and bottom coils, respectively). Soil moisture tests validated the ICE-LC improvements on minimized positional alignment sensitivity and extended read range, transducing a decrease in resonant frequency with increasing soil moisture. When calibrating with existing wired approaches, the ICE-LC sensor had a reproducible, linear sensor gain of 4.52%moisture content/MHz with an R2 of 0.745 and RMSE of 2.41%. A smaller, planar form factor of the ICE-LC sensor was also tested and exhibited reduced positional alignment sensitivity between reader and sensor at shorter read ranges. This initial study demonstrates the feasibility of the ICE-LC resonant sensor as a cost-effective method to monitor soil moisture content throughout the growing season at many field locations.

Distant calibration of low-cost PM and NO2 sensors; evidence from multiple sensor testbeds

Air quality improved significantly over the past decades. Nevertheless, air pollution continuous to have significant health impacts worldwide. To better assess people's exposure to air pollution, there is a need for higher, more personalized monitoring granularity. IoT sensor technologies can meet these requirements and pave the way towards more fine-grained air quality monitoring, improving our understanding while creating a higher public awareness driving behavioural change. This work tested the validity of scalable PM and NO2 calibration algorithms on various types of sensors (SDS011, OPC-N3, SPS30, NO2-A43F) in five different sensor testbeds deployed at various locations in Belgium and the Netherlands. The calibration models account for sensor gain and offset, while compensating for observed sensitivities of low-cost optical and electrochemical sensors. The calibration improves sensor data considerably (accuracy, linearity and correlation) up to sensitizing and supplementary (EU Class 1) categories at hourly and daily resolutions. Thanks to its cloud implementation and openly available input data, this calibration can be provided “as a service” on top of existing sensor networks in any city, on any sensor. Although distant calibration approaches improve sensor data, the ultimate performance will still depend on the applied sensor type, unit (design of sensor box) and granularity of the available reference monitoring network.

High-sensitivity gas leak detection sensor based on a compact microphone array

Early detection of tiny leaks in gas-pressurized vessels and pipelines can prevent disasters. Algorithm and sensor developments can enable acoustic methods to achieve high-sensitivity online leak detection by monitoring the sound generated by a leak and propagating in air. In this study, a compact MEMS microphone array-based sensor is proposed to detect very small gas leaks. Numerical calculation and experiment results show that the sensor gain (compared to a single microphone) is equal to the array element number when the azimuth of the leak source relative to the sensor is close to 0°. The proposed sensor can detect leaks as small as 0.67 mL/s emitted from a 0.1 mm hole at a distance of 20 cm. The bubble method can be used to enable the sensor to detect even smaller leaks, as low as 0.1 mL/s, at a distance of 10 cm using the burst sound of an invisible bubble.

Direction Finding by Covariance Matrix Sparse Representation With Sensor Gain and Phase Uncertainties in Unknown Non-Uniform Noise

The perfectly partly calibrated antenna array is a frequently assumption in most of the existing array gain/phase calibration methods. In practice, however, the partly calibrated array is usually not available. In this letter, a tail optimization method for direction finding with unknown gains and phases in the presence of spatially non-uniform noise is proposed. Specifically, the unknown gain/phase entry is firstly merged into the signal power by using the sparse representation. Subsequently, a tail optimization method that can significantly suppress the occurrence of pseudo-peaks is designed to determine the signal DOAs without a priori information of unknown sensor gain and phase errors. In addition, the spatially non-uniform noise can be removed by a linear transformation to improve the robustness against the noise. Numerical simulations examples are presented to demonstrate the effectiveness and superior performance of the proposed approach over the other existing counterparts.

Nonintrusive Current Sensing for Multicore Cables Considering Inclination With Magnetic Field Measurement

Magnetic sensor arrays have been employed to measure currents for multicore cables due to their advantages of compact size, lightweight, low power consumption, and large dynamic range. However, the existing methods are applicable only to cases where the cable must be perpendicular to the sensor array plane due to the lack of capability of the 2-D magnetic field model to consider conductor inclination. To solve this problem, this article presents a method using a circular array of single-axis magnetic sensors to measure magnetic fields along and perpendicular to the sensor array plane. A 3-D magnetic field model that involves conductor incline angles is first derived. On the basis of the derived model, two main steps are performed to realize current sensing. First, an off-site calibration method is used to calibrate magnetic sensor parameters, including sensor position, orientation, gain factor, and phase shift. Second, not only the conductor currents and positions but also the incline angles are simultaneously calculated based on the detected magnetic fields and calibrated sensor parameters. Both steps are formulated as nonlinear least-square problems and solved efficiently. Both simulation and experiment are performed to demonstrate the superiority of the proposed method compared to the existing ones based on the 2-D magnetic field model when inclinations are introduced. The proposed method may have the potential for designing a clamp-on current sensor dedicated to multicore cables similar to hand-held current probes with a relatively large inner diameter for single-conductor systems.

Pragmatic spatial sampling for wearable MEG arrays

Several new technologies have emerged promising new Magnetoencephalography (MEG) systems in which the sensors can be placed close to the scalp. One such technology, Optically Pumped MEG (OP-MEG) allows for a scalp mounted system that provides measurements within millimetres of the scalp surface. A question that arises in developing on-scalp systems is: how many sensors are necessary to achieve adequate performance/spatial discrimination? There are many factors to consider in answering this question such as the signal to noise ratio (SNR), the locations and depths of the sources, density of spatial sampling, sensor gain errors (due to interference, subject movement, cross-talk, etc.) and, of course, the desired spatial discrimination. In this paper, we provide simulations which show the impact these factors have on designing sensor arrays for wearable MEG. While OP-MEG has the potential to provide high information content at dense spatial samplings, we find that adequate spatial discrimination of sources (< 1 cm) can be achieved with relatively few sensors (< 100) at coarse spatial samplings (~ 30 mm) at high SNR. After this point approximately 50 more sensors are required for every 1 mm improvement in spatial discrimination. Comparable discrimination for traditional cryogenic systems require more channels by these same metrics. We also show that sensor gain errors have the greatest impact on discrimination between deep sources at high SNR. Finally, we also examine the limitation that aliasing due to undersampling has on the effective SNR of on-scalp sensors.

Distributed Fusion Estimation with Sensor Gain Degradation and Markovian Delays

This paper investigates the distributed fusion estimation of a signal for a class of multi-sensor systems with random uncertainties both in the sensor outputs and during the transmission connections. The measured outputs are assumed to be affected by multiplicative noises, which degrade the signal, and delays may occur during transmission. These uncertainties are commonly described by means of independent Bernoulli random variables. In the present paper, the model is generalised in two directions: (i) at each sensor, the degradation in the measurements is modelled by sequences of random variables with arbitrary distribution over the interval [0, 1]; (ii) transmission delays are described using three-state homogeneous Markov chains (Markovian delays), thus modelling dependence at different sampling times. Assuming that the measurement noises are correlated and cross-correlated at both simultaneous and consecutive sampling times, and that the evolution of the signal process is unknown, we address the problem of signal estimation in terms of covariances, using the following distributed fusion method. First, the local filtering and fixed-point smoothing algorithms are obtained by an innovation approach. Then, the corresponding distributed fusion estimators are obtained as a matrix-weighted linear combination of the local ones, using the mean squared error as the criterion of optimality. Finally, the efficiency of the algorithms obtained, measured by estimation error covariance matrices, is shown by a numerical simulation example.

Fault estimation for nonlinear systems with sensor gain degradation and stochastic protocol based on strong tracking filtering

This paper focuses on the fault estimation problem for a class of nonlinear systems with sensor gain degradation and stochastic protocol (SP) based on strong tracking filtering. The phenomenon of the sensor gain degradation is described by sequences of stochastic variables in a known interval. The stochastic protocol (SP) is used to deal with possible data conflicts in multi-signal transmission. The augmented system is constructed by combining the original system state vectors and the related faults into an augmented state vectors. The strong tracking filter (STF) is designed by introducing a fading factor into the filter structure to solve the problem of burst faults. Finally, a simulation example is given to verify the effectiveness and applicability of the proposed filter.

DOA Estimation Using Virtual ESPRIT with Successive Baselines and Coprime Baselines

In this paper, we exploit the idea of virtual ESPRIT (VESPA) to develop a multi-baseline VESPA (MB-VESPA) approach for direction finding. Specially, we define several cumulant matrices to provide ambiguous direction estimates under different baselines. Fine and unambiguous estimation is then obtained by a simple refinement step. Two refine approaches, termed as successive baseline approach and coprime baseline approach, are subsequently introduced. MB-VESPA shares all the advantages of the VESPA. It is simple, closed-form, search-free, and is applicable to irregularly linear array. In addition, it is free of the impact on the sensor gain uncertainties.

Crosstalk Suppression in Semi-Intrusive Load Monitoring Systems Using Hall Effect Sensors

Semi-intrusive load monitoring (SILM) is an appliance load monitoring approach using multiple meters, each meter measuring power for a subgroup of appliances. As an effective solution for demand response programs, SILM is used to get granular power measurements at the level of individual appliances in buildings. Hall effect sensors (HES) on each wire attached to a circuit breaker in distribution panels are one means of providing SILM. However, HES are greatly affected by crosstalk noise generated by neighboring wires, up to 35% of interfering signals. To remove crosstalk noise, this work proposes a blind source separation (BSS) approach designed to deal with sparse matrices, making SILM measurements accurate for home energy management systems. Our approach leverages two key elements: (i) a BSS algorithm based on non-correlation for sparse mixing matrix; (ii) a sensor gain compensation that leverages smart meter readings. The results demonstrate that the total power estimation error is reduced from 15% to 2% on the Tracebase dataset, and from 55% to 9% on our HES dataset monitored in a family home. Furthermore, the proposed approach outperforms standard BSS algorithms such as FastICA and InfoMax. This work shows that HES can be used for load monitoring in smart buildings.

Design considerations for advanced MWIR target acquisition systems.

In this paper, mid-wave infrared (MWIR) sensor optimization is provided as a function of the parameter Fλ/d, where F is the f-number, λ is the effective wavelength, and d is the detector pitch. For diffraction limited systems, acquisition range is related to the instantaneous field of view (detector limited operation) when Fλ/d<1, and to the optical properties (optics limited operation) when Fλ/d>2.0. Range performance is a combination of detector and optics resolution limits when Fλ/d is in between. When the system is not strictly diffraction or sampling limited, the optimal Fλ/d depends on other system component characteristics and conditions. Optical system aberrations affect system resolution and decrease range performance. As background shot noise, dark current shot noise, and read noise increase, range decreases. In the infrared spectral region, atmospheric absorption leads to reemission of thermal energy. The detected reemission creates additional shot noise. Atmospheric attenuation greatly affects MWIR sensor range performance. Next-generation MWIR sensors will have smaller detectors, larger arrays, and better sensitivity to enable Fλ/d-based optimization. Previous studies (ΔT=4K for tracked vehicles) suggest that an initial design point is Fλ/d≈2.0. When detecting low contrast targets (ΔT∼0.1K), sensor gain is used to increase the signal for a desired displayed contrast. This gain increases displayed noise and reduces acquisition range. This is typically not an issue for long-wave infrared sensors due to the excess number of photons in the 8-12 µm band but poses a problem for MWIR sensors, which are photon starved. Under such scenarios, the optimum Fλ/d appears to be about 1.5 for MWIR sensors. The results here provide reasonable strategies for MWIR system optimization and a direction associated with future MWIR focal plane development.

On the use of a calibration emitter for direct position determination with single moving array in the presence of sensor gain and phase errors

The problem of direct position determination (DPD) with single moving array in the presence of deterministic sensor gain and phase errors is addressed. To reduce the loss in localization accuracy due to these errors, a calibration emitter with known position is introduced. The Cramér-Rao lower bound (CRLB) for estimating these errors using this calibration emitter are first derived in two situations that the calibration emitter signals are exactly known or unknown. An interesting property of the CRLB is concluded that its value is independent with the sensor phase errors. The paper then proceeds to propose a two step procedure where the considered errors are estimated in the first step and the positions of other emitters are determined by DPD given the estimated errors in the second step. In the first step, the two situations introduced above are respectively considered. For the former, the errors are jointly estimated based on Maximum Likelihood with analytical solution. For the latter, a method that estimating the sensor gain and phase errors in sequence is proposed, which performs independently with the phase errors as the CRLB does. Furthermore, analytical solutions of the errors without estimation accuracy degradation can be provided by this method. Besides, the theoretical localization bias caused by these errors is also provided. Numerical simulations are used to examine the performance of the proposed method and corroborate the theoretical results at last.

Self-Calibration direct position determination using a single moving array with sensor gain and phase errors

The problem of direct position determination (DPD) using a single moving array in the presence of deterministic sensor gain and phase errors is considered. To eliminate the localization bias caused by these errors, an eigenstructure based self-calibrating DPD method is first introduced, in which the sensor gain and phase errors and the emitter positions are jointly estimated by an iterative process. Considering the performance deterioration of eigenstructure methods when the signal to noise ratio or the number of samples is not sufficiently large, a maximum likelihood (ML) based two-step self-calibration approach for DPD is subsequently proposed. The sensor gain errors are provided using the diagonal of the covariance matrix of the array output by a closed form solution at the first step. Then, the phase errors and the emitter positions are jointly estimated by an iterative scheme based on ML, in which the phase errors are also determined by a closed form solution in each iteration. Besides, detailed analyses and discussions about the differences between the introduced eigenstructure based and the proposed ML based self-calibration DPD methods are also provided. At last, numerical simulations are involved to examine their performance.

Least-squares estimators for systems with stochastic sensor gain degradation, correlated measurement noises and delays in transmission modelled by Markov chains

ABSTRACTThis paper addresses the linear least-squares estimation of a signal from measurements subject to stochastic sensor gain degradation and random delays during the transmission. These uncertainty phenomena, common in network systems, have traditionally been described by independent Bernoulli random variables. We propose a model that is more general and therefore has greater applicability to real-life situations. The model has two particular characteristics: firstly, the sensor gain degradation is represented by a white sequence of random variables with values in [0,1]; in addition, the absence or presence of delays in the transmission is described by a homogeneous three-state Markov chain, which reflects a possible correlation of delays at different sampling times. Furthermore, assuming that the measurement noise is one-step correlated, we obtain recursive prediction, filtering and fixed-point smoothing algorithms using the first and second-order moments of the signal and the processes present in the observation model. Simulation results for a scalar signal are provided to illustrate the feasibility of the proposed algorithms, using the estimation error variances as a measure of the quality of the estimators.

Robust Direct position determination against sensor gain and phase errors with the use of calibration sources

The direct position determination (DPD) method can provide high localization performance than conventional two-step localization methods. However, the existing DPD methods only consider the scenario of parameters of the receiving arrays, and the localization performance decreases dramatically when the array model is inaccurate in practice. This paper studies the problem for positioning a stationary emitter in the presence of sensor gain and phase errors (SGPEs) aided by calibration sources. To remove these negative effects caused by SGPEs, calibration sources with known positions are introduced. The extended relationship between parameters of calibration sources and errors is used to establish a structural objective function based on the maximum likelihood estimate. The calibration parameters are jointly optimized with target-related parameters and an alternating iterative algorithm is then developed to decouple the multidimensional search into several low-dimensional optimizations. We also derive the Cramer–Rao bound (CRB) to evaluate the performance of the proposed method. Simulation results demonstrate that the proposed method outperforms the existing DPD methods and two-step methods, which incorporates the error information, and the accuracy attains the associated CRB.

A robust sequential hypothesis testing method for brake squeal localisation.

This contribution deals with the in situ detection and localisation of brake squeal in an automobile. As brake squeal is emitted from regions known a priori, i.e., near the wheels, the localisation is treated as a hypothesis testing problem. Distributed microphone arrays, situated under the automobile, are used to capture the directional properties of the sound field generated by a squealing brake. The spatial characteristics of the sampled sound field is then used to formulate the hypothesis tests. However, in contrast to standard hypothesis testing approaches of this kind, the propagation environment is complex and time-varying. Coupled with inaccuracies in the knowledge of the sensor and source positions as well as sensor gain mismatches, modelling the sound field is difficult and standard approaches fail in this case. A previously proposed approach implicitly tried to account for such incomplete system knowledge and was based on ad hoc likelihood formulations. The current paper builds upon this approach and proposes a second approach, based on more solid theoretical foundations, that can systematically account for the model uncertainties. Results from tests in a real setting show that the proposed approach is more consistent than the prior state-of-the-art. In both approaches, the tasks of detection and localisation are decoupled for complexity reasons. The localisation (hypothesis testing) is subject to a prior detection of brake squeal and identification of the squeal frequencies. The approaches used for the detection and identification of squeal frequencies are also presented. The paper, further, briefly addresses some practical issues related to array design and placement.

Simple discrete time traction control for a two-wheeled mobile robot

Traction controls usually need dynamic parameters as an approximated reference in the model, such as inertia and friction or heavily computational estimator, such as slip ratio estimator and friction estimator. This paper is shown that the dynamic parameters in the traction controller can be negligible in some cases, internal sensor state variables and gain parameters are enough to retain some traction controllability. The controller is derived from the model following control method (MFC) on a discrete domain. Experiments are performed, in order to evaluate the efficiency of the simplified controller using a two-wheeled mobile robot, by comparing slippage between the position and velocity control scheme with and without the traction control scheme. The experimental results show that the proposed traction control scheme is able to suppress the slip and very simple to implement.