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The purpose of this study is to explore a signal processing method to improve the angular rate accuracy of micro-electro-mechanical system (MEMS) gyroscope by combining numerous gyroscopes.

To improve the dynamic performance of the signal processing method, the interacting multiple model (IMM) can be applied to the fusion of gyroscope array. However, the standard IMM has constant Markov parameter, which may reduce the model switching speed. To overcome this problem, an adaptive IMM filter is developed based on the kurtosis of the gyroscope output, in which the transition probabilities are adjusted online by utilizing the dynamic information of the rate signal.

The experimental results indicate that the precision of the gyroscope array composed of six gyroscopes increases significantly and the kurtosis-based adaptive Markov parameter IMM filter (K-IMM) performs better than the baseline methods, especially under dynamic conditions. These experiments prove the validity of the proposed fusion method.

The proposed method can improve the accuracy of MEMS gyroscopes without breakthrough on hardware, which is necessary to extend their utility while not restricting the overwhelming advantages.

A K-IMM algorithm is proposed in this paper, which is used to improve the angular rate accuracy of MEMS gyroscope by combining numerous gyroscopes.

The purpose of this study is to establish an effective tracking algorithm for small unmanned aerial vehicles (UAVs) based on interacting multiple model (IMM) to take timely countermeasures against illegal flying UAVs.

In this paper, based on the constant velocity model (CV), the maneuvering adaptive current statistical model (CS) and the angular velocity adaptive three-dimensional (3D) fixed center constant speed rate constant steering rate model, a small UAV tracking algorithm based on adaptive interacting multiple model (AIMM-UKF) is proposed. In addition, an adaptive robust filter is added to each model of the algorithm. The linear Kalman filter algorithm is attached to the CV model and the CS model and the unscented Kalman filter algorithm (UKF) is attached to the CSCDR model to solve the nonlinearity of the 3D turning model.

Monte-Carlo simulation comparison with the other two IMM tracking algorithms shows that in the case of different movement modes and maneuvering strength of the UAV, the AIMM-UKF algorithm makes a good trade-off between the amount of calculation and filtering accuracy, which can maintain more accurate and stable tracking and has strong robustness. At the same time, after testing the actual observation data of the UAV, the results show that the AIMM-UKF algorithm state estimation trajectory can be regarded as an actual trajectory in practical engineering applications, which has good practical value.

This paper presents a new small UAV tracking algorithm based on IMM and the advantages and practicability of this algorithm compared with existing algorithms are proved through experiments.

Continuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.

First, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.

Compared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.

In this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

The purpose of the paper is to present an approach to detect and isolate the aircraft sensor and control surface/actuator failures affecting the mean of the Kalman filter innovation sequence.

The extended Kalman filter (EKF) is developed for nonlinear flight dynamic estimation of an F‐16 fighter and the effects of the sensor and control surface/actuator failures in the innovation sequence of the designed EKF are investigated. A robust Kalman filter (RKF) is very useful to isolate the control surface/actuator failures and sensor failures. The technique for control surface detection and identification is applied to an unstable multi‐input multi‐output model of a nonlinear AFTI/F‐16 fighter. The fighter is stabilized by means of a linear quadratic optimal controller. The control gain brings all the eigenvalues that are outside the unit circle, inside the unit circle. It also keeps the mechanical limits on the deflections of control surfaces. The fighter has nine state variables and six control inputs.

In the simulations, the longitudinal and lateral dynamics of an F‐16 aircraft dynamic model are considered, and the sensor and control surface/actuator failures are detected and isolated.

A real‐time detection of sensor and control surface/actuator failures affecting the mean of the innovation process applied to the linearized F‐16 fighter flight dynamic is examined and an effective approach to isolate the sensor and control surface/actuator failures is proposed. The nonlinear F‐16 model is linearized. Failures affecting the covariance of the innovation sequence is not considered in the paper.

An approach has been proposed to detect and isolate the aircraft sensor and control surface/actuator failures occurred in the aircraft control system. An extended Kalman filter has been developed for the nonlinear flight dynamic estimation of an F‐16 fighter. Failures in the sensors and control surfaces/actuators affect the characteristics of the innovation sequence of the EKF. The failures that affect the mean of the innovation sequence have been considered. When the EKF is used, the decision statistics changes regardless the fault is in the sensors or in the control surfaces/actuators, while a RKF is used, it is easy to distinguish the sensor and control surface/actuator faults.

Intelligent and connected vehicle technology is in the ascendant. High-level autonomous driving places more stringent requirements on the accuracy and reliability of environmental perception. Existing research works on multitarget tracking based on multisensor fusion mostly focuses on the vehicle perspective, but limited by the principal defects of the vehicle sensor platform, it is difficult to comprehensively and accurately describe the surrounding environment information.

In this paper, a multitarget tracking method based on roadside multisensor fusion is proposed, including a multisensor fusion method based on measurement noise adaptive Kalman filtering, a global nearest neighbor data association method based on adaptive tracking gate, and a Track life cycle management method based on M/N logic rules.

Compared with fixed-size tracking gates, the adaptive tracking gates proposed in this paper can comprehensively improve the data association performance in the multitarget tracking process. Compared with single sensor measurement, the proposed method improves the position estimation accuracy by 13.5% and the velocity estimation accuracy by 22.2%. Compared with the control method, the proposed method improves the position estimation accuracy by 23.8% and the velocity estimation accuracy by 8.9%.

A multisensor fusion method with adaptive Kalman filtering of measurement noise is proposed to realize the adaptive adjustment of measurement noise. A global nearest neighbor data association method based on adaptive tracking gate is proposed to realize the adaptive adjustment of the tracking gate.

The purpose of this paper is to design the fault detection algorithm for multidimensional dynamic systems using a new approach for checking the statistical characteristics of Kalman filter innovation sequence.

The proposed approach is based on given statistics for the mathematical expectation of the spectral norm of the normalized innovation matrix of the Kalman filter.

The longitudinal dynamics of an aircraft as an example is considered, and detection of various sensor faults affecting the mean and variance of the innovation sequence is examined.

A real‐time detection of sensor faults affecting the mean and variance of the innovation sequence, applied to the linearized aircraft longitudinal dynamics, is examined. The non‐linear longitudinal dynamics model of an aircraft is linearized. Faults affecting the covariances of the innovation sequence are not considered in the paper.

The proposed approach permits simultaneous real‐time checking of the expected value and the variance of the innovation sequence and does not need a priori information about statistical characteristics of this sequence in the failure case.

In this paper, the authors aim to explore how students learn how to learn in a team-based graduate course Designing for Open Innovation using a theoretical framework that focuses on the cognitive functions of team-based processes and team performance.

An automated assessment methodology for the structural and semantic analysis of individual and shared knowledge representations serves as a foundation for the approach. A case study is presented that explores the development of individual mental models and shared mental models over the course.

An assessment of the mental models indicates that in this course three types of learning took place, namely individual learning, team-based learning, and learning from each other.

The automatically generated graphical representations provide insight into the complex processes of the learning-dependent development of individual mental models and shared mental models.

Channel conflicts between initial providers (IPs) and independent middlemen (IMM) can hinder the market penetration of IPs, especially for small- and medium-sized enterprises with low brand awareness.

A case study is used to describe the introduction of an agent-friendly graphic user interface (AFGUI) as a compromise for the basic needs of the IP and IMM, both for the successful market entry of IPs and to encourage the IMM to accept original brand manufacturing (OBM), which can maximize the benefits of IPs after a successful product/service launch.

This case study shows that the AFGUI significantly contributes to increasing the IMM’s OBM selection. Comparing the “before AFGUI introduction” group to the group of IMMs who have been SeaHerb’s – a manufacturer of brown seaweed extract – IMM for fewer than three years shows that the latter group’s OBM selection can positively affect the order amount.

The AFGUI can integrate traditional offline distribution channel systems into online distribution systems. Regarding the AFGUI’s functional effectiveness for preventing fake sellers and fake products, further research on its adoption by online platform providers (e.g. Amazon and eBay) is recommended.

The purpose of this paper is to establish a connection between the business model (BM) and B2B marketing research by developing a new approach to the BM analysis and improvement, which is based on inter-organizational networks and value chains.

The methodology is based on mutual enrichment of methods and results of BM and business-to-business marketing studies that are relatively isolated from each other, and on integration of them to the unified structured approach that could be applied to analyze both BM and a set of interfirm relationships of networked market players. This paper is based on extensive literature review in the areas of BM and relationship/industrial marketing. The review is based on most cited and contemporary articles found in the Scopus and EBSCO databases.

This paper formulates the key BM research directions and visualizes their development over time. It is demonstrated in the paper that currently, there is little involvement of marketing researchers in the study of BM; however, there are multiple touchpoints between these areas of knowledge, which can help in further developing BM studies. On the basis of these findings, a conceptual model and new network-based approach to BM analysis is offered, which allows addressing the complex nature of networked interaction among BM participants. The approach includes stepwise algorithm for BM analysis designed for business practitioners.

The proposed approach can be applied by business practitioners to analyze and improve their BM via managing the interactions of inter-organizational network participants with a focus on customer interests. While the approach is of a universal character, the specific tools for evaluating BM on each stage may vary across different markets.

This research contributes to the current conceptual knowledge on BM studies development and their relationship with marketing. It also contributes to theory and practice by the development of a new marketing-based approach to the BM analysis focused on managing business relationships, which allows evaluation of the current state of a BM and provides directions for its improvement. This approach evaluates the alignment of interfirm relationships along the value chain and orients it toward the final consumer.

This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood.

In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.

In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system.

This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.