GPS Vector Tracking Loop with Fault Detection and Exclusion

In this paper,both the integrity monitoring and fault detection and exclusion(FDE)mechanisms are incorporated into the vector tracking loop(VTL)architecture of the Global Positioning System(GPS)receiver for reliability enhancement.For the VTL,the tasks of signal tracking and navigation state estimation no longer process separately and a single extended Kalman filter(EKF)is employed to simultaneously track the received signals and estimate the receiver’s position,velocity,etc.In contrast to the scalar tracking loop(STL)which utilizes the independent parallel tracking loop approach,the VTL technique is beneficial from the correlation of each satellite signal and user dynamics.The VTL approach provides several important advantages.One of the merits is that the tracking loop can be assisted for overcoming the problem of signal blockage.Although the VTL architectures provide several important advantages,they suffer some fundamental drawbacks.For example,the errors in the navigation solutions may degrade the tracking accuracy.The most significant drawback is that failure of tracking in one channel may affect the entire tracking loop and possibly lead to loss of lock.For reliability enhancement,the EKF based integrity monitoring and FDE algorithms are developed to prevent the error from spreading into the entire tracking loop.The integrity monitoring is utilized to check the possible fault in the pseudorange and the pseudorange rate,followed by the FDE mechanism employed to exclude the abnormal satellite signals.Performance assessment and evaluation for the proposed approach will be presented.

Quantitative analysis of the performance of vector tracking algorithms

Vector tracking changes the classical structure of receivers. Combining signal tracking and navigation solution,vector tracking can realize powerful processing capabilities by the fusion technique of receiving channel and feedback correction. In this paper,we try to break through the complicated details of numerical analysis,consider the overall influencing factors of the residual in observed data,and use the intrinsic link between a conventional receiver and a vector receiver. A simple method for performance analysis of the vector tracking algorithm is proposed. Kalman filter has the same steady performance with the classic digital lock loop through the analysis of the relation between gain and band width. The theoretical analysis by the least squares model shows that the reduction of range error is the basis for the superior performance realized by vector tracking. Thus,the bounds of its performance enhancement under weak signal and highly dynamic conditions can be deduced. Simulation results verify the effectiveness of the analysis presented here.

Influence of B1 code correlation loop for vector tracking structures under complicated environment

The code tracking loop is a key component for user positioning. The pseudorange information of Bei Dou B1 signals has been fused and changed for vector tracking, so a correlation output model for complex scenarios is designed to prevent the propagation of error and valuate the signal performance. The relevant software and hardware factors that affect the output are analyzed.A single channel time-division multiplexing(TDM) method for multicorrelation data extraction is proposed. Statistical characteristics of the correlation output data for both vector and scalar structures are evaluated. Simulation results show that correlation outputs for both structures follow normal or Chi-squared distributions in normal conditions, and the Gamma distribution in harsh conditions. It is shown that a tracking model based on the multi-channel fusion hardly changes the probability distribution of the correlation output in the normal case, but it reduces the ranging error of the code loop, and hence the tracking ability of the code loop for weak signals is improved. Furthermore, vector tracking changes the pseudorange characteristics of channels anytime, and affects the mutual correlation outputs of the code loops in the abnormal case. This study provides a basis for the subsequent design of autonomous integrity algorithms for vector tracking.

GPS Vector Tracking Receivers with Rate Detector for Integrity Monitoring

In this paper,the integrity monitoring algorithm based on a Kalman filter(KF)based rate detector is employed in the vector tracking loop(VTL)of the Global Positioning System(GPS)receiver.In the VTL approach,the extended Kalman filter(EKF)simultaneously tracks the received signals and estimates the receiver’s position,velocity,etc.In contrast to the scalar tracking loop(STL)that uses the independent parallel tracking loop approach,the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals.Although the VTL scheme provides several important advantages,the failure of tracking in one channel may affect the entire system and lead to loss of lock on all satellites.The integrity monitoring algorithm can be adopted for robustness enhancement.In general,the standard integrity monitoring algorithm can timely detect the step type erroneous signals.However,in the presence of ramp type slowly growing erroneous signals,detection of such type of error takes much more time since the error cannot be detected until the cumulative exceeds the specified threshold.The integrity monitoring based on the rate detector possesses good potential for resolving such problem.The test statistic based on the pseudorange residual in association with the EKF is applied for determination of whether the test statistic exceeds the allowable threshold values.The fault detection and exclusion(FDE)mechanism can then be employed to exclude the hazardous erroneous signals for the abnormal satellites to assure normal operation of GPS receivers.Feasibility of the integrity monitoring algorithm based on the EKF based rate detector will be demonstrated.Performance assessment and evaluation will be presented.

A robust vector tracking loop structure based on potential bias analysis

This paper proposes a robust vector tracking loop structure based on potential bias analysis. The influence of four kinds of biases on the existing two implementations of Vector Tracking Loops(VTLs) is illustrated by theoretical analysis and numerical simulations, and the following findings are obtained. Firstly, the initial user state bias leads to steady navigation solution bias in the relative VTL, while new measurements can eliminate it in the absolute VTL. Secondly, the initial code phase bias is transferred to the following navigation solutions in the relative VTL, while new measurements can eliminate it in the absolute VTL. Thirdly, the user state bias induced by erroneous navigation solution of VTLs can be eliminated by both of the two VTLs. Fourthly,the multipath/NLOS likely affects the two VTLs, and the induced tracking bias in the duration of the multipath/NLOS would decrease the performance of VTLs. Based on the above analysis,a robust VTL structure is proposed, where the absolute VTL is selected for its robustness to the two kinds of initialization biases;meanwhile, the instant bias detection and correction method is used to improve the performance of VTLs in the duration of the multipath/NLOS. Numerical simulations and experimental results verify the effectiveness of the proposed robust VTL structure.

Evaluation of a laying-hen tracking algorithm based on a hybrid support vector machine

Background： Behavior is an important indicator reflecting the welfare of animals. Manual analysis of video is the most commonly used method to study animal behavior. However, this approach is tedious and depends on a subjective judgment of the analysts. There is an urgent need for automatic identification of individual animals and automatic tracking is a fundamental part of the solution to this problem.Results： In this study, an algorithm based on a Hybrid Support Vector Machine（HSVM） was developed for the automated tracking of individual laying hens in a layer group. More than 500 h of video was conducted with laying hens raised under a floor system by using an experimental platform. The experimental results demonstrated that the HSVM tracker outperformed the Frag（fragment-based tracking method）, the TLD（Tracking-Learning-Detection）,the PLS（object tracking via partial least squares analysis）, the Mean Shift Algorithm, and the Particle Filter Algorithm based on their overlap rate and the average overlap rate.Conclusions： The experimental results indicate that the HSVM tracker achieved better robustness and state-of-theart performance in its ability to track individual laying hens than the other algorithms tested. It has potential for use in monitoring animal behavior under practical rearing conditions.

Support Vector Machine Assisted GPS Navigation in Limited Satellite Visibility

This paper investigates performance improvement via the incorporation of the support vector machine(SVM)into the vector tracking loop(VTL)for the Global Positioning System(GPS)in limited satellite visibility.Unlike the traditional scalar tracking loop(STL),the tracking and navigation modules in the VTL are not independent anymore since the user’s position can be determined by using the information from other satellites and can be predicted on the basis of the states of the user.The method proposed in this paper makes use of the SVM to bridge the GPS signal and prevent the error growth due to signal outage.Similar to the neural network,the SVM is motivated by its ability to approximate an unknown nonlinear input-output mapping through supervised training.The SVM is employed for predicting adequate numerical control oscillator(NCO)inputs,i.e.,providing better prediction of residuals for the Doppler frequency and code phase in order to maintain regular operation of the navigation system.When the navigation processing is in good condition,the SVM is at the training stage,and the output information from the discriminator and navigation filter is adopted as the inputs.Other machine learning(ML)algorithms such as the radial basis function neural network(RBFNN)and the Adaptive Network-Based Fuzzy Inference System(ANFIS)are employed for comparison.Performance evaluation for the SVM assisted architecture as compared to the RBFNNand ANFIS-assisted methods and the un-assisted VTL will be carried out and the performance evaluation during GPS signal outage will be presented.The proposed design is very useful for navigation during the environment of limited satellite visibility to effectively overcome the problem in the environment of GPS outage.

Continuous Tracking of GPS Signals with Data Wipe-Off Method

The decentralized pre-filter based vector tracking loop(VTL)configuration with data wipe-off(DWO)method of the Global Positioning System(GPS)receiver is proposed for performance enhancement.It is a challenging task to continuously track the satellites’signals in weak signal environment for the GPS receiver.VTL is a very attractive technique as it can provide tracking capability in signal-challenged environments.In the VTL,each channel will not form a loop independently.On the contrary,the signals in the channels of VTL are shared with each other;the navigation processor in turn predicts the code phases.Thus,the receiver can successfully track signals even the signal strength from individual satellite is weak.The tracking loop based on the pre-filter provides more flexible adjustment to specific environments to reduce noise interference.Therefore,even if the signals from some satellites are very weak the receiver can track them from the navigation results based on the other satellites.The navigation data,which contains information necessary to perform navigation computations,are binary phase-shift keying(BPSK)modulated onto the GPS carrier phase with the bit duration of 20 ms(i.e.,50 bits per second)for the GPS L1 C/A signals.The coherent integration interval can be extended for improved tracking performance in signal-challenged environment.However,tracking accuracy is decreased by possible data bit sign reversal.The DWO algorithm can be employed to remove the data bit in I and Q correlation values so as to avoid energy loss due to bit transitions when the integration interval of the correlator is extended over 20 ms under the low carrier-to-noise ratio(C/No)environments.The proposed method has an advantage to provide continuous tracking of signals and obtain improved navigation performance.Performance evaluation of the tracking capability as well as positioning accuracy will be presented.

Vector modeling and track simulation in axial turn-milling motion

Through vector analysis the kinetic vector model is built in a machining cylinder surface through axial turn-milling. When building a kinetic vector model in the machining field, machining through axial turn-milling and using equilateral triangles and square prism surfaces, the kinetic vector model is given any equilateral polygon prismic surface. Kinetic tracks are simulated through these kinetic models respectively, thus it can be seen that the axial turn-milling is a very effective method in manufacturing any equilateral, polygon, prismic surface.

Multi-channel signal parameters joint optimization for GNSS terminals

Traditional global navigation satellite system(GNSS)terminals for satellite navigation adopt independent channels to track the signals from different satellites, which results in a lack of information interaction between the channels. Inspired by the vector tracking idea, and drawing lessons from the principle that in the position domain the Taylor expanded pseudorange observations can be used for positioning via the least squares method, this paper proposes a novel least squares-based multi-channel parameter joint estimation(MPJE) method in the signal domain, which not only retains the advantages of channel fusion, but also maintains the flexibility and diversity of the localization algorithm. With achieving optimal carrier to noise ratio as the goal, the proposed method obtains the required code loop and carrier loop parameters for signal tracking in the domain of whole channels. Experimental results indicate that this method fully achieves the assistant fusion advantages of frequency lock loop(FLL), phase lock loop(PLL)and delay lock loop(DLL), making good use of the robustness and dynamic properties of the FLL and the measurement accuracy of the DLL, and is helpful for achieving stable and accurate signal tracking under weak signals and high dynamic stress environments.

HYBRID INTERFERENCE CANCELLATION SCHEME FOR BLIND MULTIUSER DETECTION IN MULTIPATH FADING CHANNELS

This paper introduces and analyzes a detection scheme for adaptive suppression of Multiuser Access Interference (MAI) and MultiPath Distortion (MPD) for mobile station of DS/CDMA system. The proposed detection scheme may amount to a RAKE receiver structure,wherein each branch is considered as a linear multiuser filter designed under a Linear Constrained Minimum Variance (LCMV) optimization strategy to suppress MAI, followed by a proper combining rule to suppress MPD. The adaptive blind multiuser detecting and optimum combining of the proposed receiver are realized, based on the Least-Mean-Square (LMS) algorithm and an adaptive vector tracking algorithm respectively. Finally, the feasibility of the above two algorithms is proved by the numerical results provided by computer simulation.

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function,gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.