Event-Triggered State Filter Estimation for INS/DVL Integrated Navigation with Correlated Noise and Outliers
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an Event-Triggered Correlation Noise Filter (ETCNF) for fusing Inertial Navigation System (INS) and Doppler Velocity Log (DVL) data in autonomous underwater vehicles, enhancing navigation accuracy and robustness.
Area Of Science
- Robotics and Autonomous Systems
- Navigation and Control Systems
- Signal Processing
Background
- Inertial Navigation Systems (INS) and Doppler Velocity Logs (DVL) are crucial for Autonomous Underwater Vehicle (AUV) navigation.
- Challenges include correlated noise and DVL measurement anomalies in complex underwater environments.
- Existing fusion methods may not adequately address these issues.
Purpose Of The Study
- To develop a novel filtering method for INS/DVL data fusion.
- To improve the accuracy and robustness of AUV navigation systems.
- To address correlated noise and abnormal DVL measurements.
Main Methods
- Designed an Event-Triggered Correlation Noise Filter (ETCNF).
- Introduced an auxiliary matrix to decouple correlated noise for state estimation.
- Implemented an event-triggered mechanism to detect and eliminate abnormal DVL measurements.
Main Results
- The ETCNF method effectively fused INS and DVL data.
- Decoupling correlated noise improved state estimation.
- The event-triggered mechanism successfully handled DVL anomalies.
- Enhanced positioning accuracy and system robustness were demonstrated.
Conclusions
- The proposed ETCNF algorithm offers a superior approach for INS/DVL data fusion in AUVs.
- The method effectively mitigates correlated noise and DVL measurement errors.
- Simulation results validate the algorithm's effectiveness and robustness.
These videos have been matched automatically. Contact us if they are not relevant.
These videos have been matched automatically. Contact us if they are not relevant.
Related Concept Videos
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
Where x(t) is the...
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...