Blind Calibration Research Articles

Spectrum distribution‐based blind calibration method of mismatches for time‐interleaved ADCs

AbstractThis brief proposes a calibration method for offset, gain, and timing‐skew mismatches in time‐interleaved analog‐to‐digital converters. The calibration method includes the estimation method and the correction method. The estimation method is based on the difference between the spectral distribution of the error harmonics and the input signal. The correction method adopts sequential correction. Simulation results demonstrate that the proposed method calibrated all three mismatches simultaneously and eased the limit for input signals compared with the traditional method.

An all-digital low-complexity blind background calibration of timing mismatch in time-interleaved ADCs

This paper proposed a novel all-digital blind background calibration to mitigate timing mismatch in time-interleaved analog-to-digital converter (TIADC). In estimation module, the adoption of a subtraction-based error extraction function and the design of Variable-Step-Size Least Mean Squares algorithm contribute to reducing the computational complexity and enhancing the convergence speed with optimal output accuracy respectively. In compensation module, a dual-stage Taylor series expansion structure has been introduced to effectively maintain the overall output performance. The proposed architecture is applied to a 12-bit 3 GS/s four-channel TIADC model. Its effectiveness for single-tone and multi-tone signals is proven through systematical testing and analysis. The simulation results exhibit that the Spurious Free Dynamic Range is significantly improved by 54.53 dB in the single-tone signal case, and the timing mismatch is converged after 1000 samples. The proposed calibration circuit has been synthesized utilizing a 28 nm standard cell library for assessing its hardware consumption, area (0.051 mm2) and average power dissipation (67.5 mW) within the integrated chip architecture. Our technology provides a viable optimization solution to improve efficiency of TIADC in high-speed systems.

Blind Calibration of Environmental Acoustics Measurements Using Smartphones.

Environmental noise control is a major health and social issue. Numerous environmental policies require local authorities to draw up noise maps to establish an inventory of the noise environment and then propose action plans to improve its quality. In general, these maps are produced using numerical simulations, which may not be sufficiently representative, for example, concerning the temporal dynamics of noise levels. Acoustic sensor measurements are also insufficient in terms of spatial coverage. More recently, an alternative approach has been proposed, consisting of using citizens as data producers by using smartphones as tools of geo-localized acoustic measurement. However, a lack of calibration of smartphones can generate a significant bias in the results obtained. Against the classical metrological principle that would aim to calibrate any sensor beforehand for physical measurement, some have proposed mass calibration procedures called "blind calibration". The method is based on the crossing of sensors in the same area at the same time, which are therefore supposed to observe the same phenomenon (i.e., measure the same value). The multiple crossings of a large number of sensors at the scale of a territory and the analysis of the relationships between sensors allow for the calibration of the set of sensors. In this article, we propose to adapt a blind calibration method to data from the NoiseCapture smartphone application. The method's behavior is then tested on NoiseCapture datasets for which information on the calibration values of some smartphones is already available.

Blind Array Calibration of Mutual Coupling, Phase, and Gain for Automotive Radar

Blind Array Calibration of Mutual Coupling, Phase, and Gain for Automotive Radar

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

FEC-Aided Decision Feedback Blind Mismatch Calibration of TIADCs in Wireless Time-Varying Channel Environments

Joint Blind Calibration of Unknown Gain and Mutual Coupling Errors for MWC System

Joint Blind Calibration of Unknown Gain and Mutual Coupling Errors for MWC System

A Variational Bayesian Blind Calibration Approach for Air Quality Sensor Deployments

Air pollution has become a global threat to urban environments and public health. Low-cost air quality sensor systems have been deployed to support fine-grained monitoring, and in-field calibration methods are necessary to assure the accuracy of sensor observation. Existing methods use in-field reference to train some specific calibration models. Nevertheless, collecting sufficient reference data after deployment is challenging. First, the synchronized data pair of reference observation is hard to obtain. Thus, many blind calibration approaches rely on some alternative reference like a historical reference before sensor observation. However, since the relationship model between alternative reference and sensor observation becomes more complicated, the amount of collected data is still insufficient to learn the complex model. To address the above challenge, we propose a Variational Bayesian Blind Calibration Algorithm. Our method introduces a supplement set that only involves historical reference and synchronized reference measurement without sensor observation to alleviate the few-reference pressure. Large amounts of the introduced supplement sets have been collected in different cities by accurate stations, which we adopt to conduct prediction tasks and build a Bayesian model to learn how to combine the formulated prediction task and target calibration task in a theoretically better way. Furthermore, we design a variational Bayesian framework to make good use of the easily obtained supplement set to train a better prediction model for improving calibration performance and relieving the pressure of collecting costly reference measurements after deployment. Evaluations of real-world and synthetic datasets show that the proposed approach has a better performance than previous baselines.

Enhancing Protocol Privacy With Blind Calibration of Quantum Devices

To mitigate the noise in quantum channels, calibration is used to tune the devices to minimize error. Generally, calibration is performed by transmitting pre-agreed-upon calibration states and determining an error cost so the two parties can tune their devices accordingly. The calibration states can be the same ones used for the desired protocol, and so an untrusted party could potentially learn which protocol is being performed by gathering knowledge of the calibration states and cost function. Here, we assume privacy of the protocol is the goal and therefore the receiver should not be allowed to determine the protocol states. We limit the information that is revealed to the receiver, and in this regard, we propose a simple protocol that hides the calibration states and cost function from the receiver, but still allows for calibration to be performed efficiently, thereby increasing the privacy of the protocol. We show various numerical results demonstrating the ability of the protocol under various channel noise parameters and communication scenarios.

Gain and phase calibration of sensor arrays from ambient noise by cross-spectral measurements fitting.

We address the problem of blind gain and phase calibration of a sensor array from ambient noise. The key motivation is to ease the calibration process by avoiding a complex procedure setup. We show that computing the sample covariance matrix in a diffuse field is sufficient to recover the complex gains. To do so, we formulate a non-convex least-square problem based on sample and model covariances. We propose to obtain a solution by low-rank matrix approximation, and two efficient proximal algorithms are derived accordingly. The first algorithm solves the problem modified with a convex relaxation to guarantee that the solution is a global minimizer, and the second algorithm directly solves the initial non-convex problem. We investigate the efficiency of the proposed algorithms by numerical and experimental results according to different sensing configurations. These results show that efficient calibration highly depends on how the measurements are correlated. That is, estimation is achieved more accurately when the field is spatially over-sampled.

Lagrangian turbulence statistics using 3D-PTV: Realistic virtual experiment assessment

The outcome of a three-dimensional particle tracking velocimetry (3D-PTV) experiment can be significantly improved if the influence of the camera locations, calibration, and light volume can be determined before conducting the experiment. Such a-priory assessment will also reduce the time for troubleshooting a 3D-PTV experiment. In this paper, we report a “virtual experiment” approach. Multiple frames of synthetic particle images are generated using triangulation and optical spread functions based on the Lagrangian trajectories in 3D velocity fields with known spatio-temporal characteristics. We use an analytical 3D Burger’s vortex, and the homogeneous isotropic turbulence (HIT) data-set from the turbulence database hosted by Johns Hopkins University (JHU). The 3D particle positions are projected on two-dimensional (2D) images using artificial extrinsic and intrinsic camera matrices, depth of field, and volume illumination. These 2D images served as images taken by a four-camera 3D-PTV system in a virtual experiment. Blind test calibration using OpenPTV, an open source software, reconstructs the camera positions for a simulated experimental set-up and the calibrated set-up is used to create Lagrangian trajectories from the particle image sequences. Finally, we compare the turbulent statistics reconstructed from the virtual experiment with that of the ground truth data. We show that reconstruction from a virtual 3D-PTV experiment reproduces the mean flow and turbulent statistics faithfully; however, the disparity increases for higher order and multi-point statistics. The proposed approach can be used to fine-tune the camera parameters and the light volume in order to increase the fidelity of turbulent flow measurements using 3D-PTV.

Blind PV temperature model calibration

The determination of module temperature in a photovoltaic (PV) system is a crucial factor in PV modelling and the assessment of system health status. However, the scarcity of on-site temperature measurements poses a challenge, and existing PV temperature models encounter difficulties in accurately estimating temperatures in systems characterized by unique structural or locational attributes. This paper introduces a novel approach that enables the calibration of PV temperature models without relying on direct temperature measurements. Referred to as blind calibration, this method eliminates the requirement for temperature measurements, thus offering a promising solution to the aforementioned challenges. The method is validated using three datasets, demonstrating accurate PV temperature (TPV) estimation with mean absolute errors below 2 °C. The findings highlight the suitability of the proposed approach for various PV system types, while acknowledging limitations regarding certain system configurations.

A 28-nm 6-GHz 2-bit Continuous-Time ΔΣ ADC With −101-dBc THD and 120-MHz Bandwidth Using Blind Digital DAC Error Correction

In this article, a 6-GHz, 2-bit, fourth-order continuous-time delta–sigma (CT <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> ) analog-to-digital converter (ADC) fabricated in 28-nm CMOS is presented. It achieves −101- and −105-dBc total harmonic distortion (THD)/third-order inter-modulation (IM3) typically and 72.3-dB signal to noise and distortion ratio (SNDR) in 120-MHz bandwidth (BW). The ADC comprises four cascaded integrators with inverter-based amplifiers, an offset compensated 2-bit quantizer, and calibrated 2-bit feedback (FB) digital-to-analog converter (DAC). The DAC and quantizer employ blind digital calibration techniques enabling the wideband linearity performance. The ADC does not require any external test signal during calibration. The power dissipation of the modulator core, including demultiplexer, is 108.8 mW.

Wideband Blind Calibration for UCA OAM Communication System

This letter presents a wideband blind calibration method for uniform circular array (UCA) orbital angular momentum (OAM) systems without the prior knowledge of the positions and attitudes of the transmit and receive UCAs. The basic idea is to rewrite the maximum-likelihood estimation (MLE) for all parameters as an optimization problem over a Lie group which can represent the positions and attitudes of the UCAs. Then an accelerated Levenberg-Marquardt algorithm is implemented to solve this optimization problem by utilizing the sparsity in the iteration process. Simulation results show that the proposed wideband blind calibration method can estimate the gain/phase errors and the positions and attitudes of the transmit and receive UCAs simultaneously and improve the achievable spectral efficiency of the UCA OAM communication system efficiently.

A Blind Background Calibration Technique for Super-Regenerative Receivers

In this brief, a blind background calibration technique for super-regenerative receivers (SRRs) is presented. The proposed calibration scheme is designed to help SRRs to maintain their high sensitivity and immunity to negative transconductance (<inline-formula> <tex-math notation="LaTeX">${\mathrm {-}\mathrm {G}}_{\mathrm {m}}$ </tex-math></inline-formula>) variations under process-voltage- temperature (PVT) variations. Unlike the conventional foreground <inline-formula> <tex-math notation="LaTeX">${-\mathrm {G}}_{\mathrm {m}}$ </tex-math></inline-formula> variations calibration techniques that require interruption of the receiver input, the proposed calibration technique employs input signal statistics and does not require interruption of the input bit-stream for extraction of the errors. The proposed scheme is based on an adaptive algorithm that compares the probability distribution of the pseudorandom-input (PI) stream and the output of the super-regenerative oscillator (SRO) and forces them to coincide at the end of the calibration. The proposed technique is implemented using a mixed-signal detection circuit and a finite state machine (FSM) that drives an 8-bit successive approximation register (SAR) to adjust the compensation current in the SRO. The simulation results successfully verify the effectiveness and reliability of the proposed calibration technique and show significant improvements in terms of SRR sensitivity under different process corners.

Fully Digital On-Chip Wideband Background Calibration for Channel Mismatches in Time-Interleaved Time-Based ADCs

This letter presents a fully integrated on-chip digital mismatch compensation system for time-based time-interleaved (TI) data converters. The proposed digital compensation features blind calibration of gain, offset, and timing mismatches. The implemented system uses time-based sampling clock mismatch detection, achieving convergence within 32K samples, which is on par with analog-assisted background methods. A specialized filter structure compensates for timing mismatches of magnitude up to 0.21 of the sampling period, nearly triple the range of other published digital compensation methods, and is effective for input signals up to 0.92 Nyquist bandwidth. The on-chip digital correction achieves suppression of all mismatch tones to levels below −60 dBc while running fully in the background. The operation is demonstrated with an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8\times $ </tex-math></inline-formula> TI 2-GS/s analog-to-digital converter (ADC) prototype chip implemented in a 28-nm CMOS process.

A 2.5-GS/s Four-Way-Interleaved Ringamp-Based Pipelined-SAR ADC with Digital Background Calibration in 28-nm CMOS

A 2.5-GS/s 12-bit four-way time-interleaved pipelined-SAR ADC is presented in 28-nm CMOS. A bias-enhanced ring amplifier is utilized as the residue amplifier to achieve high bandwidth and excellent power efficiency compared with a traditional operational amplifier. A high linearity front-end is proposed to alleviate the non-linearity of the diode for ESD protection in the input PAD. The embedded input buffer can suppress the kickback noise at high input frequencies. A blind background calibration based on digital-mixing is used to correct the mismatches between channels. Additionally, an optional neural network calibration is also provided. The prototype ADC achieves a low-frequency SNDR/SFDR of 51.0/68.0 dB, translating a competitive FoMw of 0.48 pJ/conv.-step at 250 MHz input running at 2.5 GS/s.

In Situ Blind Calibration of Sensor Networks for Infrastructure Monitoring

The recent development of Internet-of-Things (IoT) technologies has enabled smaller and lower-cost sensor nodes, motivating the deployment of more flexible and scalable sensor networks for infrastructure monitoring applications. However, because these nodes tend to be affected by environmental conditions and aging, they are prone to long-term drift over years of operation; thus, they need to be recalibrated on a regular basis to ensure data accuracy. In this paper, we propose an in situ blind calibration algorithm for infrastructure monitoring sensor networks, which requires neither physical intervention nor the assumption that the sensors are measuring identical ground-truth signals. The algorithm uses a multioutput Gaussian process (MOGP) to model the spatial-temporal distribution of the measurand and drift, thus removing irrelevant short-term fluctuations and decomposing the drift from long-term trends. We evaluate the algorithm on a real-world dataset, and the results show that the proposed method can successfully calibrate a number of drifting sensors more than 20% greater than previous methods while achieving a higher drift estimation accuracy.

Black-Box Optimization of Superconducting Circuits Using Reduced-Complexity Neural Networks

Single-flux quantum (SFQ) logic based high-speedperiodic-threshold flash converter circuits require multiple non-linear and correlated parameters tuned precisely to function optimally. These parameters cannot be pre-determined from simulations and change with the clock frequency, thus requiring manual optimization every time the clock frequency is changed. In this work, we demonstrate automated bias optimization of an 8-bit superconducting SFQ flash ADC for the first time. A closed-loop 4X lower computational complexity hybrid particle-swarm-gradient-descent-optimization is demonstrated that first uses particle swarm optimization (PSO) to coarse tune the ADC biases and then applies gradient descent (GD) optimization for fine tuning. This results in a 12X reduction in the blind calibration time from several days manually, to a few hours using the proposed optimization scheme, with a resultant performance 2 dB better than an optimization done by a highly skilled human.

Correlation-based reconfigurable blind calibration for timing mismatches in TI-ADCs

Mismatches between sub-channels limit the dynamic performance of time-interleaved analog-to-digital converters (TI-ADCs). This paper proposes a correlation-based method of calibration for timing mismatches in M-channel TI-ADCs by using the cross-correlation between sub-channels of the output signals to estimate the temporal deviations. The output signal is calibrated by reducing the arbitrary order distortion, which is approximated by multiplying the distortions with the estimated coefficients of mismatch. Furthermore, a reconfigurable strategy composed of stages of arbitrary calibration is proposed to achieve suitable stages with the requisite dynamic performance. Finally, the calibration performance of the proposed method is verified through simulations that use different input signals and strengths of mismatch.

Opportunistic monitoring of pavements for noise labeling and mitigation with machine learning

Currently, municipalities assess rolling noise on road surfaces using Close-Proximity measurements (CPX). To avoid these labor-intensive measurements, an opportunistic approach based on commodity sensors in a fleet of cars, is proposed. Blind sensor calibration eliminates the effect of measurement vehicle and varying observation conditions. Calibration relies on spatial coherence: modifiers and confounders do not interact strongly with location while the quantity of interest depends on location and less on measurement vehicle. Generalized additive speed models, car offset and de-noising autoencoders (DAE) were investigated. DAE achieves prominent results: (1) ratio of variability of measurements at a single location to the variability of measurements over all locations increases, (2) convergence of mean measurement at a location is faster, and (3) seasonal effects are eliminated. Finally, although the proposed method includes a diversity of tires, below 1600 Hz its results differ from CPX less than the difference between bi-annually repeated CPX measurements.