Fading Factor Research Articles

Mathematical study of the mean communication distances at lognormal shadow fading and Nakagami fading

<p><span>Over some few decades, the communication distance and connectivity at wireless sensor network (WSN) has got noticeable attention from the researchers. In general, the communication process in WSN ad-hoc occurs without some wired infrastructure and, the communication process done through a ‘single-hop’ transmission, where the intermediate nodes called as relay nodes are used in long distance communication and the nodes are capable to transmit and receive the data packets. In this paper, we presented a computational analysis of communication distance in WSN in the presence of fading effects such as nakagami-m fading and lognormal fading. An extensive investigation at both nakagami-m fading and lognormal fading is carried out to provide optimize communication in ad-hoc WSN. In addition, individual wireless nodes have the similar range of communication, is assumed to get the precise communication distance and coverage area in a WSN, where it is required to get mean communication distance. In result analysis section, several validation parameters such as transmitted power, attenuation constant and different number of fading factor is considered to provide proper analytical view. </span></p>

A Strong Tracking Mixed-Degree Cubature Kalman Filter Method and Its Application in a Quadruped Robot.

The motion state of a quadruped robot in operation changes constantly. Due to the drift caused by the accumulative error, the function of the inertial measurement unit (IMU) will be limited. Even though multi-sensor fusion technology is adopted, the quadruped robot will lose its ability to respond to state changes after a while because the gain tends to be constant. To solve this problem, this paper proposes a strong tracking mixed-degree cubature Kalman filter (STMCKF) method. According to system characteristics of the quadruped robot, this method makes fusion estimation of forward kinematics and IMU track. The combination mode of traditional strong tracking cubature Kalman filter (TSTCKF) and strong tracking is improved through demonstration. A new method for calculating fading factor matrix is proposed, which reduces sampling times from three to one, saving significantly calculation time. At the same time, the state estimation accuracy is improved from the third-degree accuracy of Taylor series expansion to fifth-degree accuracy. The proposed algorithm can automatically switch the working mode according to real-time supervision of the motion state and greatly improve the state estimation performance of quadruped robot system, exhibiting strong robustness and excellent real-time performance. Finally, a comparative study of STMCKF and the extended Kalman filter (EKF) that is commonly used in quadruped robot system is carried out. Results show that the method of STMCKF has high estimation accuracy and reliable ability to cope with sudden changes, without significantly increasing the calculation time, indicating the correctness of the algorithm and its great application value in quadruped robot system.

Robust cubature Kalman filter method for the nonlinear alignment of SINS

Nonlinear initial alignment is a significant research topic for strapdown inertial navigation system (SINS). Cubature Kalman filter (CKF) is a popular tool for nonlinear initial alignment. Standard CKF assumes that the statics of the observation noise are pre-given before the filtering process. Therefore, any unpredicted outliers in observation noise will decrease the stability of the filter. In view of this problem, improved CKF method with robustness is proposed. Multiple fading factors are introduced to rescale the observation noise covariance. Then the update stage of the filter can be autonomously tuned, and if there are outliers exist in the observations, the update should be less weighted. Under the Gaussian assumption of KF, the Mahalanobis distance of the innovation vector is supposed to be Chi-square distributed. Therefore a judging index based on Chi-square test is designed to detect the noise outliers, determining whether the fading tune are required. The proposed method is applied in the nonlinear alignment of SINS, and vehicle experiment proves the effective of the proposed method.

Multi-Sensor Combined Measurement While Drilling Based on the Improved Adaptive Fading Square Root Unscented Kalman Filter.

In the process of the attitude measurement for a steering drilling system, the measurement of the attitude parameters may be uncertain and unpredictable due to the influence of server vibration on bits. In order to eliminate the interference caused by vibration on the measurement and quickly obtain the accurate attitude parameters of the steering drilling tool, a new method for multi-sensor dynamic attitude combined measurement is presented. Firstly, by using a triaxial accelerometer and triaxial magnetometer measurement system, the nonlinear model based on the quaternion is established. Then, an improved adaptive fading square root unscented Kalman filter is proposed for eliminating the vibration disturbance signal. In this algorithm, the square root of the state covariance matrix is used to replace the covariance matrix in the classical unscented Kalman filter (UKF) to avoid the filter divergence caused by the negative definite state covariance matrix. The fading factor is introduced into UKF to adjust the filter gain in real-time and improve the adaptive ability of the algorithm to mutation state. Finally, the computational method of the fading factor is optimized to ensure the self-adaptability of the algorithm and reduce the computational complexity. The results of the laboratory test and the field-drilling data show that the proposed method can filter out the interference noise in the attitude measurement sensor effectively, improve the solution accuracy of attitude parameters of drilling tools in the case of abrupt changes in the measuring environment, and thus ensuring the dynamic stability of the well trajectory.

An Upper Bound Capacity Algorithm for Multi-hop Wireless Ad Hoc Networks

The upper bound capacity based on the SINR model is a very important parameter when evaluating the performance of the multi-hop wireless ad hoc networks. In this paper, a new upper bound capacity algorithm is proposed. Firstly, we analyzed the classical physics SINR model over the Rayleigh fading channels to establish the relationship between density of nodes and path outage probability. Then, we developed the closed-form expression of the upper bound transmission capacity based on the Weber model for the random networks. Finally, we evaluated effects of parameters in the communication range, density, SINR threshold and fading factors, and consequently transmission capacity of the network by sensitivity analysis. The numerical simulation showed that the optimum density of nodes was existed to obtain the upper bound transmission capacity. The capacity increased firstly and then decreased over the density of nodes increasing. It affected by channel fading factor and communication range sensitively.

Vehicle path tracking Based on Strong Tracking Cubature Kalman Filter While Encountering An Emergency Collision Avoidance

Considering the high speed emergency avoidance situations, the vehicle path tracking was studied in this paper. The three degrees of freedom nonlinear vehicle model and the cubature Kalman filter are applied to tracking vehicle emergency avoidance path. For the degradation of adaptive tracking performance in the vehicle emergency avoidance, an improved strong tracking cubature Kalman filter algorithm is proposed by introducing the fading factor into filtering process which is learnt from strong tracking filter. The algorithm has a simple implementation, high estimation accuracy and good robustness. Different target paths are selected and the cubature Kalman filter is applied to simulate path tracking in the vehicle emergency avoidance. The simulation results show that the proposed path tracking method can control the vehicle tracking the ideal collision avoidance path rapidly without collisions.

Online Estimation Method for Permanent Magnet Temperature of High‐Density Permanent Magnet Synchronous Motor

Since it can be difficult to directly estimate the temperature of permanent magnets (PM) in high‐density permanent magnet motor rotors, this paper proposes a method based on lumped parameter thermal networks (LPTN) for online estimation of PM temperature. Firstly, a parameter model is established that contains a four‐node thermal network model and a loss model. For the online estimation, an extended Kalman filter algorithm with fading factor is used to dynamically update the thermal network parameter model in order to estimate the PM temperature. The simulation and experimental results show that the parameter model can quickly converge to a value that is close to the true value. Therefore, this online estimation method for PM temperature can accurately monitor the PM temperature online without increasing the required motor hardware; thus not affecting the motor operation. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Hybrid Robust Sequential Fusion Estimation for WSN-Assisted Moving-Target Localization With Sensor-Node-Position Uncertainty

This article presents a hybrid robust sequential fusion estimation (HRSFE) method for moving-target localization in mobile asynchronous wireless sensor networks (WSNs). The estimation error or inconsistency, caused by the uncertainties of sampling, communication, and sensor-node position, is considered fully and compensated by incorporating the sensor-node position with uncertainty into the state vector as well as introducing an adaptive fading factor into the cubature Kalman filter (CKF) to maintain the estimation consistency. In addition, a QR-decomposition-based square-root form of the cubature strong localization filter (SR-CSLF) is proposed to improve the accuracy and stability of the CSLF. Moreover, an HRSFE method is designed to estimate the augment state of the WSN-assisted moving-target-localization system, and the proposed estimation method combines the merits of both the SR-CSLF and SR-CKF effectively and is able to deal with the uncertainties of sampling, communication, and sensor-node position in a unified framework. Simulations and experiments of a WSN-assisted moving-target-localization system under various uncertainty settings are employed to demonstrate the effectiveness and superiorities of the proposed method.

Constellation coordination and pilot reuse for multi‐cell large‐scale MIMO systems

To alleviate pilot contamination for multi-cell large-scale multiple-input multiple-output (MIMO) systems, here the authors propose a constellation coordination scheme according to a constellation coordination constraint (CC constraint) on the large-scale fading factors. In fact, a detailed analysis of the uplink process introduces the CC constraint, which reveals that if the CC constraint cannot be satisfied, the error probability will be larger than a threshold. Otherwise, the error probability goes to zero if both the number of antennas at the base station and signal-to-noise ratio go to infinity. Furthermore, by modelling the location of users as a Poisson point process, the authors derive a safe area threshold according to the CC constraint, through which an adaptive pilot reuse is proposed. In this scheme, users outside the dynamic safe area threshold are allowed to reuse the pilot, while other users are not allowed. Simulation results show that the CC scheme alleviates the pilot contamination effectively and the proposed pilot reuse scheme based on the safe area threshold improves the uplink achievable rate of the system significantly.

Estimating road profiles in quarter car model using two methods

Vehicle controllability analysis on real roads can be obtained only if valid road profile and tyre road friction model are known. This work determines the time-vary road profiles, called inputs, in a nonlinear system using two input estimation methods. Both algorithms use the extended Kalman filter (EKF) with two different recursive estimators to determine inputs and states. Based on the two regression equations, a recursive least-squares estimator is used with a tuneable fading factor called a conventional input estimation (CIE) and an adaptive weighting fading factor called an adaptive weighting input estimation (AWIE). Numerical simulations of a nonlinear system, quarter car model, demonstrate the accuracy of the proposed methods. Simulation results show that proposed methods accurately estimate road profiles, tyre forces, and states, and the AWIE approach has superior robust estimation capability to the CIE method in the nonlinear system. The simulation results are the same with the single degree of freedom.

Estimating road profiles in quarter car model using two methods

Vehicle controllability analysis on real roads can be obtained only if valid road profile and tyre road friction model are known. This work determines the time-vary road profiles, called inputs, in a nonlinear system using two input estimation methods. Both algorithms use the extended Kalman filter (EKF) with two different recursive estimators to determine inputs and states. Based on the two regression equations, a recursive least-squares estimator is used with a tuneable fading factor called a conventional input estimation (CIE) and an adaptive weighting fading factor called an adaptive weighting input estimation (AWIE). Numerical simulations of a nonlinear system, quarter car model, demonstrate the accuracy of the proposed methods. Simulation results show that proposed methods accurately estimate road profiles, tyre forces, and states, and the AWIE approach has superior robust estimation capability to the CIE method in the nonlinear system. The simulation results are the same with the single degree of freedom.

A Unified Performance Analysis of Relaying Communication System for IoT Application With Hybrid Fading

Power line and wireless hybrid communication address the problem that wireless and power line cannot communicate with each other in the Internet of Things (IoT). However, the theoretical performance of amplify-and-forward (AF) relay communication based on hybrid fading condition has no closed expression. This article takes a relay system model of wireless and power line hybrid media as a representative and investigates a unified performance analysis method for AF relay systems with hybrid fading. The mixture lognormal (LogN) approximate algorithm based on the moment-generating function (MGF) is applied to approximate a wireless fading coefficient to mixture LogN distribution. Moreover, we propose a joint optimization algorithm for determining the key parameters given that the adjustable variables in the MGF equations evidently affect the approximation accuracy. Importantly, this article is then extended to other fading models, taking general gamma (GG) as an example. The effectiveness and reliability of the algorithm are verified by the Monte Carlo simulation. Finally, closed analytic expressions of the system performance, such as bit error rate (BER), outage probability, and average channel capacity, are derived. The effects of hybrid fading factor, impulse noise parameter, and power allocation on system performance are analyzed using the theoretical formulas.

Research on simultaneous localization and mapping for AUV by an improved method: Variance reduction FastSLAM with simulated annealing

At present, simultaneous localization and mapping (SLAM) for an autonomous underwater vehicle (AUV) is a research hotspot. Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion, an improved method of variance reduction fast simultaneous localization and mapping (FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation, particle depletion and particle loss in traditional FastSLAM, which lead to the reduction of AUV location estimation accuracy. The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling. By increasing the weight of small particles and decreasing the weight of large particles, the variance of particle weight can be reduced, the number of effective particles can be increased, and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles. The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation, maintains the diversity of particles, weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.

Robust fading cubature Kalman filter and its application in initial alignment of SINS

Strapdown inertial navigation system (SINS) is a commonly used autonomous positioning instrument. As an integral calculating system, initial alignment is an essential work of SINS. Cubature Kalman filter (CKF) is a common algorithm for nonlinear initial alignment. Under the condition of inaccurate system model and non-Gaussian observation noise, the filter will appear to unstable or even divergence. In order to solve this problem, an anti-jamming improvement suitable for initial alignment is proposed in this paper. In which the prior covariance matrix and the observation noise covariance matrix are tuned by multiple fading factors. A Chi-square test method is designed to check the filter’s state, determining the introducing method of the fading factors autonomously. Experiment results show that, the proposed algorithm performs better in terms of robustness and adaptability even under the condition of inaccurate model and non-Gaussian observation noise.

Channel Characteristics of High-Speed Railway Station Based on Ray-Tracing Simulation at 5G mmWave Band

In order to satisfy the increasing demand for the higher transmission capacity of “smart station”, millimeter wave (mmWave) technology is expected to play a significant role in the high data rate communication system. Based on the ray-tracing simulation technology, this paper would study wireless channel characteristics of the three-dimensional (3D) model of high-speed railway station at the mmWave band. Key parameters such as path loss exponent, shadow fading factor, delay spread, Rician K-factor, angular spread, power angle spectrum, and spatial correlation are extracted and investigated. These channel characteristics are of value for the selection of antenna arrays and even the design of future 5G communication networks in the railway environment.

A novel strong tracking cubature Kalman filter and its application in maneuvering target tracking

The fading factor exerts a significant role in the strong tracking idea. However, traditional fading factor introduction method hinders the accuracy and robustness advantages of current strong-tracking-based nonlinear filtering algorithms such as Cubature Kalman Filter (CKF) since traditional fading factor introduction method only considers the first-order Taylor expansion. To this end, a new fading factor idea is suggested and introduced into the strong tracking CKF method. The new fading factor introduction method expanded the number of fading factors from one to two with reselected introduction positions. The relationship between the two fading factors as well as the general calculation method can be derived based on Taylor expansion. Obvious superiority of the newly suggested fading factor introduction method is demonstrated according to different nonlinearity of the measurement function. Equivalent calculation method can also be established while applied to CKF. Theoretical analysis shows that the strong tracking CKF can extract the third-order term information from the residual and thus realize second-order accuracy. After optimizing the strong tracking algorithm process, a Fast Strong Tracking CKF (FSTCKF) is finally established. Two simulation examples show that the novel FSTCKF improves the robustness of traditional CKF while minimizing the algorithm time complexity under various conditions.

Robust integrated orbit and attitude estimation using geophysical data

Geophysical information such as the Earth geomagnetic field and gravity gradient (GG) data can provide a basis for autonomous concurrent orbit and attitude estimation (COAE) of satellites in low earth orbits (LEO), as magnetometers and gravity gradiometer measurements are in general functions of time, position as well as the vehicle's orientation. While gradiometer has recently been investigated just for orbit estimation (OE), the current study is focused on COAE via only utility of the GG data. To this aim, observability conditions are analyzed, where the sensitivity of the proposed COAE approach with respect to various system and roto-translational elements is also examined. Considering the nonlinear nature of the COAE problem, a modified robust unscented Kalman filter is adopted for state estimation that is enhanced by innovation-based fading factors to be robust against the modeling errors. Subsequently, a centralized fusion of GG and three-axis magnetometer (TAM) measurement data is employed to improve the accuracy and reliability of the proposed approach for space navigation. The results obtained from numerical test cases confirm feasibility and effectiveness of the proposed technique for complete COAE of space navigation in LEO satellites.

Adaptive Consensus-Based Unscented Information Filter for Tracking Target with Maneuver and Colored Noise.

Distributed state estimation plays a key role in space situation awareness via a sensor network. This paper proposes two adaptive consensus-based unscented information filters for tracking target with maneuver and colored measurement noise. The proposed filters can fulfill the distributed estimation for non-linear systems with the aid of a consensus strategy, and can reduce the impact of colored measurement noise by employing the state augmentation and measurement differencing methods. In addition, a fading factor that shrinks the predicted information state and information matrix can suppress the impact of dynamical model error induced by target maneuvers. The performances of the proposed algorithms are investigated by considering a target tracking problem using a space-based radar network. This shows that the proposed algorithms outperform the traditional consensus-based distributed state estimation method in aspects of tracking stability and accuracy.

Performance analysis of secure communications over correlated slow‐fading additive white Gaussian noise channels

SummaryThe broadcast nature of communications in wireless communication networks makes it vulnerable to some attacks, particularly eavesdrop attack. Hence, information security can have a key role to protect privacy and avoid identity theft in these networks, especially in distributed networks. In the wireless systems, the signal propagation is affected by path loss, slow fading (shadowing), and fast fading (multi‐path fading). As we know, there is a correlation between communication channels in the real radio environments. This correlation is defined by the correlation between their shadowing and/or multipath fading factors. So when there are several channels in the wireless systems, there is certainly a correlation between the channels. In this paper, we assume that the transmitter knows the full channel state information (CSI), it means the transmitter knows both the channel gains of the illegitimate (ie, eavesdropper) and the legitimate receivers and study the performance of secure communications of single‐input single‐output (SISO) systems consisting of single antenna devices, in the presence of a single antenna passive eavesdropper over correlated slow fading channels, where the main (transmitter to legitimate receiver) and eavesdropper (transmitter to illegitimate receiver) channels are correlated. Finally, we present numerical results and verify the accuracy of our analysis by Monte‐Carlo simulations.

Research on Underwater Target Tracking Based on Multiple Fading Factors Strong Tracking SCKF

In the research on complex maneuvering underwater target tracking, the single fading factor adjusts the estimation of all the state variables with the same value. However, the uncertainty of different state variable is different. Only one single fading factor can not get each state variable optimal estimation. Aiming at this problem, multiple fading factors strong tracking SCKF algorithm is proposed. Simulation results show that the multiple fading factors strong tracking SCKF algorithm has higher estimation accuracy than the CKF algorithm, SCKF algorithm and STSCKF algorithm when the underwater target system model is uncertain.