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This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

The authors present a low-cost unmanned aerial vehicle (UAV) for autonomous flight and navigation in GPS-denied environments using an off-the-shelf smartphone as its core on-board processing unit. Thereby, the approach is independent from additional ground hardware and the UAV core unit can be easily replaced with more powerful hardware that simplifies setup updates as well as maintenance. The paper aims to discuss these issues.

The UAV is able to map, locate and navigate in an unknown indoor environment fusing vision-based tracking with inertial and attitude measurements. The authors choose an algorithmic approach for mapping and localization that does not require GPS coverage of the target area; therefore autonomous indoor navigation is made possible.

The authors demonstrate the UAVs capabilities of mapping, localization and navigation in an unknown 2D marker environment. The promising results enable future research on 3D self-localization and dense mapping using mobile hardware as the only on-board processing unit.

The proposed autonomous flight processing pipeline robustly tracks and maps planar markers that need to be distributed throughout the tracking volume.

Due to the cost-effective platform and the flexibility of the software architecture, the approach can play an important role in areas with poor infrastructure (e.g. developing countries) to autonomously perform tasks for search and rescue, inspection and measurements.

The authors provide a low-cost off-the-shelf flight platform that only requires a commercially available mobile device as core processing unit for autonomous flight in GPS-denied areas.

In conjunction with the usual rate forms of the conservation equations, the time derivative form of the equation of state is investigated from a numerical consideration point of view. First, the derivation of the rate form of the equation of state is presented. Systematic comparison between the new method and the traditional iterative method is made by applying the method to a simple flow problem. The comparison is then extended to a practical engineering problem requiring accurate prediction of pressure. The rate method is found to be more advantageous in many aspects. It is more intuitive for system analysis, more appropriate for eigenvalue extraction, as well as easier to program and to implement. Numerically, the rate method is found to be more efficient and accurate than the traditional method.

Output stability or drift overtime has long been a major performance deficiency for gas sensors irrespective of what technology or methodology is used for their conception. Software correction may alleviate the problem somewhat but it is not always applicable. It has long been the objective of many researchers in this field to overcome this problem fundamentally and for good. The purpose of this paper is to show that this objective has now finally been achieved.

Conventional non‐dispersive infrared (NDIR) dual beam methodology utilizes the ratio of signal channel output over reference channel output for signal processing. The signal filter overlaps the absorption band of the gas of interest while the reference filter does not. However, this ratio changes as the source ages. The current methodology uses an absorption bias between signal and reference channel outputs. This absorption bias is created by using a path length for the signal channel greater than that for the reference channel. Both the signal and reference detectors carry an identical spectral filter overlapping the absorption band of the gas to be measured.

Implementation of the currently patented NDIR gas‐sensing methodology has been carried out in different gas sensor configurations for over a year in the laboratory. Performance results for these sensors showing insignificant output drifts overtime have been repeatedly demonstrated via simulated aging for the source.

The paper puts forward the view that the recent breakthrough of the Near Zero Drift methodology for NDIR gas sensors will very quickly change the hierarchy of technology dominance and utility for gas sensors at large.

The purpose of this study, which is designed for the implementation of models in the implicit finite element framework, is to propose a robust, stable and efficient explicit integration algorithm for rate-independent elasto-plastic constitutive models.

The proposed automatic substepping algorithm is founded on an explicit integration scheme. The estimation of the maximal subincrement size is based on the stability analysis.

In contrast to other explicit substepping schemes, the algorithm is self-correcting by definition and generates no cumulative drift. Although the integration proceeds with maximal possible subincrements, high level of accuracy is attained. Algorithmic tangent stiffness is calculated in explicit form and optionally no analytical second-order derivatives are needed.

The algorithm is convenient for elasto-plastic constitutive models, described with an algebraic constraint and a set of differential equations. This covers a large family of materials in the field of metal plasticity, damage mechanics, etc. However, it cannot be directly used for a general material model, because the presented algorithm is convenient for solving a set of equations of a particular type.

The estimation of the maximal stable subincrement size is computationally cheap. All expressions in the algorithm are in explicit form, thus the implementation is simple and straightforward. The overall performance of the approach (i.e. accuracy, time consumption) is fully comparable with a default (built-in) ABAQUS/Standard algorithm.

The estimated maximal subincrement size enables the algorithm to be stable by definition. Subincrements are much larger than those in conventional substepping algorithms. No error control, error correction or local iterations are required even in the case of large increments.

Attitude and heading are very important measurements on board aircraft. In modern solutions they are measured by the attitude and heading reference system (AHRS). In some small unmanned systems, the GPS track angle is used for heading corrections instead of the magnetometer; then, the system measures the track angle instead of heading. With a temporary lack of correction signals, the measurement error increases very quickly. Similarly, a quick increase in the measurement error is observed when a magnetic heading sensor used for correction stops working properly. This study aims to propose measurement of the roll angle for yaw angle correction.

AHRS algorithms were designed; typical maneuvers were analyzed. The method was verified by simulation and in flight testing analysis. For quantitative analyses, a performance index was proposed.

The method enables reduction of the yaw angle error caused by the gyros bias error. This study presents the idea, results of simulations and flight testing data analysis and discusses advantages and limitations of the presented method.

The presented methodology can be implemented in AHRS systems for manned and unmanned aircraft.

This study enables more accurate measurement of the yaw angle in the case of missing correction signals.

This paper aims to solve the problem that strong noise interference seriously affects the direction of arrival (DOA) estimation in complex underwater acoustic environment. In this paper, a combined noise reduction algorithm and micro-electro-mechanical system (MEMS) vector hydrophone DOA estimation algorithm based on singular value decomposition (SVD), variational mode decomposition (VMD) and wavelet threshold denoising (WTD) is proposed.

Firstly, the parameters of VMD are determined by SVD, and the VMD method can decompose the signal into multiple intrinsic mode functions (IMFs). Secondly, the effective IMF component is determined according to the correlation coefficient criterion and the IMF less than the threshold is processed by WTD. Then, reconstruction is carried out to achieve the purpose of denoising and calibration baseline drift. Finally, DOA estimation is achieved by the combined directional algorithm of preprocessed signal.

Simulation and field experiments results show that the algorithm has good noise reduction and baseline drift correction effects for nonstationary underwater signals, and high-precision azimuth estimation is realized.

This research provides the basis for MEMS hydrophone detection and positioning and has great engineering significance in underwater detection system.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.

In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.

The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO₂ concentrations below 250 ppb and the failure was treated as a correct response.

This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.