An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks
<p>Conventional distributed fusion architecture for target tracking.</p> "> Figure 2
<p>Measurement residual indicator based distributed fusion architecture.</p> "> Figure 3
<p>Artificial measurement-based distributed fusion architecture.</p> "> Figure 4
<p>Flow chart of artificial measurements-based adaptive filter.</p> "> Figure 5
<p>Target tracking performance: <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p> "> Figure 6
<p>Performance comparison: <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>. (<b>a</b>) Target tracking error: <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>. (<b>b</b>) Energy consumptions: <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics> </math> versus <math display="inline"> <semantics> <mrow> <mi>δ</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics> </math>.</p> "> Figure 7
<p>Impacts of normalized threshold <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>. (<b>a</b>) Target tracking error with different <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>. (<b>b</b>) Energy consumptions with different <math display="inline"> <semantics> <mi>δ</mi> </semantics> </math>.</p> "> Figure 8
<p>Impacts of pre-given reference value <math display="inline"> <semantics> <msub> <mi mathvariant="normal">Θ</mi> <mi>r</mi> </msub> </semantics> </math>. (<b>a</b>) Target tracking error with different <math display="inline"> <semantics> <msub> <mi mathvariant="normal">Θ</mi> <mi>r</mi> </msub> </semantics> </math>. (<b>b</b>) Energy consumptions with different <math display="inline"> <semantics> <msub> <mi mathvariant="normal">Θ</mi> <mi>r</mi> </msub> </semantics> </math>.</p> "> Figure 9
<p>Performances of different sensor groups. (<b>a</b>) Target tracking error with different sensor groups. (<b>b</b>) Energy consumptions with different sensor groups.</p> "> Figure 10
<p>Performances of different number of selected sensors. (<b>a</b>) Target tracking error with different number of selected sensors. (<b>b</b>) Energy consumptions with different number of selected sensors.</p> "> Figure 11
<p>Number of cases needed to try of different search algorithms.</p> ">
Abstract
:1. Introduction
2. Related Work
3. Problem Formulation
3.1. System Model
3.2. Distributed Fusion Architectures
3.3. Measurement Residual-Based Sensor Scheduling
4. Artificial Measurement Based Adaptive Filter
4.1. Artificial Measurement Model
4.2. Artificial Measurement Based Filter
4.3. Adaptive Determination
4.4. Optimal Sensor Group Selection
5. Simulation and Results
5.1. Simulation Scenario
5.2. Performance Verification
5.2.1. Performance Comparison
5.2.2. Impacts of
5.2.3. Performance of Adaptive Filter
5.2.4. Performance of Sensor Group Selection
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Notations | Explanations |
---|---|
Target state at time k | |
Estimate of target state at time k | |
Predicted estimate of target state at time k | |
State transition matrix at time k | |
Process noise at time | |
Covariance of process noise at time k | |
Measurement of sensor i at time k | |
, , | Measurement noise of sensor i at time k |
Covariance of measurement noise of sensor i at time k | |
Predicted measurement at time k | |
Measurement residual of sensor i at time k | |
Artificial measurement of sensor i at time k | |
Measurement function of sensor i at time k | |
Jacobian matrix of sensor i at time k | |
Target location at time k | |
Location of Sensor i | |
Normalized threshold | |
Indicator value of sensor i at time k | |
Estimate error covariance at time k | |
Predicted estimate error covariance at time k | |
Distribution of random variable | |
Expectation of random variable | |
Covariance of random variable | |
Probability of random variable | |
Covariance of measurement residual of sensor i at time k | |
Covariance of measurement residual of sensor i at time k with artificial measurement | |
Kalman gain of sensor i at time k | |
Kalman gain of sensor i at time k with artificial measurement | |
Trace of | |
Pre-given reference value |
Exhaustive Search | 15 | 70 | 210 | 1365 | 4845 |
GBFOS | 11 | 26 | 45 | 110 | 200 |
Worst Sensor Group | Random Sensor Group | Best Sensor Group | |
---|---|---|---|
Target tracking error | 10.6308 | 5.3976 | 4.3389 |
Number of packets | 292.65 | 210.34 | 192.16 |
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Chen, H.; Zhang, S.; Liu, M.; Zhang, Q. An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks . Sensors 2017, 17, 971. https://doi.org/10.3390/s17050971
Chen H, Zhang S, Liu M, Zhang Q. An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks . Sensors. 2017; 17(5):971. https://doi.org/10.3390/s17050971
Chicago/Turabian StyleChen, Huayan, Senlin Zhang, Meiqin Liu, and Qunfei Zhang. 2017. "An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks " Sensors 17, no. 5: 971. https://doi.org/10.3390/s17050971
APA StyleChen, H., Zhang, S., Liu, M., & Zhang, Q. (2017). An Artificial Measurements-Based Adaptive Filter for Energy-Efficient Target Tracking via Underwater Wireless Sensor Networks . Sensors, 17(5), 971. https://doi.org/10.3390/s17050971