Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs
Abstract
:1. Introduction
2. System Model and Related Work
2.1. System Model
- A1.
- There is only a single sensor node with unknown position (target).
- A2.
- All anchor nodes are equipped with a device that can measure the AOA (directional antenna or antenna arrays) and RSS.
- A3.
- All anchor nodes are fixed at known positions and can transfer RSS/AOA measurements to a central node (processor).
- A4.
- All measurement errors are i.i.d. zero-mean Gaussian noise with unknown variances which are the same for all anchor nodes.
- A5.
- All nodes are in line-of-sight with respect to the target.
- A6.
- All nodes are located at a homogeneous environment so that the path-loss exponent (PLE) is the same for all nodes.
- (i)
- From the relation between Cartesian and spherical coordinates, a direct estimation of the target position () can be obtained from the distance estimate () and the observed angles ():
- (ii)
- From the maximum likelihood Function (6), the following linear equation can be derived via several non-linear transforms ([33]):The WLS solution based on range-based weights, referred to as the target-range WLS (TR-WLS) solution, is presented in [33], while the WLS solution based on the approximated error covariance matrix, referred to as the EC-WLS, is presented in [24]. The EC-WLS achieves state-of-the-art target estimation accuracy performance in terms of mean squared error (MSE) [24].
2.2. Problem Formation
2.3. Related Work
3. Proposed Method
3.1. Estimation of Initial Target Position Using EC-WLS Based on AOA’s
3.2. Kalman Filter-Based Estimation of Transmit Power and Path-Loss Exponent
3.3. Example of KF Based TP and PLE Estimation
3.4. KF-Based Estimation of TP and PLE for a Moving Target
3.5. Refined Estimation of Target Position Using Hybrid Measurements
Algorithm 1 Proposed hybrid target localization algorithm under unknown TP and PLE. |
1. Estimate the target position from AOA measurements. Solve (24) 2. Estimate the TP and PLE using the KF. i. Initialize and . ii. Predict and update , and . (30) for Predict: Update: end 3. Estimate the target position using EC-WLS with the estimated parameters (TP and PLE). Solve (35) |
4. Complexity Comparison
5. Performance Results
6. Practical Considerations and Limitations
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A. Approximated Error Covariance Matrix for AOA Measurements
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Algorithm | Description | Complexity |
---|---|---|
SOCP | The SOCP method in case for unknown TP in [30] | |
WLLS | The WLLS method in case for unknown PLE in [31] | |
SR-WLS | The SR-WLS method in case for unknown TP in [32] | |
TP-WLS | The TP-WLS method in case for unknown TP in [33] | |
KF-ECWLS | The proposed method in case for unknown TP and PLE |
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Kang, S.; Kim, T.; Chung, W. Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs. Sensors 2020, 20, 6582. https://doi.org/10.3390/s20226582
Kang S, Kim T, Chung W. Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs. Sensors. 2020; 20(22):6582. https://doi.org/10.3390/s20226582
Chicago/Turabian StyleKang, SeYoung, TaeHyun Kim, and WonZoo Chung. 2020. "Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs" Sensors 20, no. 22: 6582. https://doi.org/10.3390/s20226582
APA StyleKang, S., Kim, T., & Chung, W. (2020). Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs. Sensors, 20(22), 6582. https://doi.org/10.3390/s20226582