Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation
Abstract
:1. Introduction
- The RSS model is transformed into a pseudo-linear system with new noise;
- Based on LS criterion, a new non-convex objective function is derived to solve the target positioning problem;
- The non-convex objective function is transformed into a convex objective function by semi-definite programming.
2. System Model and Problem Formulation
3. The Proposed Algorithm
3.1. Known Positioning Algorithm
3.2. Unknown Positioning Algorithm
4. Simulation Results
4.1. Is Known
4.2. Is Unknown
5. Experiment
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
WSNs | Wireless sensor networks |
AOA | Angle of arrival |
TOA | Time of arrival |
TDOA | Time difference of arrival |
RSS | Received signal strength |
SDP | Semi-definite programming |
SOCP | Second-order cone programming |
RMSE | Root mean square error |
CDF | Cumulative distribution function |
CM | Mean error |
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Symbol | Explanation |
---|---|
The RSS measurements | |
The reference distance | |
The RSS measurements when | |
The pass-loss exponent | |
The position of the target node | |
The position of the i-th anchor node | |
N | The number of anchor nodes |
The range between the target node and the i-th anchor node | |
The measurement noise between the target node and the i-th anchor node | |
The standard deviation of measurement noises |
Symbol | Describe | Value |
---|---|---|
reference distance | 1 m | |
path loss | 4 | |
reference measured value | −10 dBm | |
simulation times | 5000 |
Method | Describe | Running Times (s) |
---|---|---|
MLE-SDP | The “ML-SDP” algorithm in this paper | 0.58 |
LS-SDP | The “LS-SDP” algorithm in [30] | 0.85 |
LS-SOCP | The “LS-SOCP” algorithm in [31] | 1.16 |
LSRE-SOCP | The “LSRE-SOCP” algorithm in [32] | 2.53 |
optimal-ML | The “optimal ML range-free” algorithm in [37] | 3.58 |
SOCP1 | The “SOCP1” algorithm in [35] | 2.87 |
DEOR1 | The “DEOR” algorithm in [38] | 0.45 |
DEOR-fast1 | The “DEOR-fast” algorithm in [38] | 0.23 |
Method | Describe | Running Times (s) |
---|---|---|
MLE-SDP2 | The “ML-SDP2” algorithm in this paper | 1.13 |
LS-SDP2 | The “LS-SDP2” algorithm in [30] | 1.65 |
LS-SOCP2 | The “LS-SOCP2” algorithm in [31] | 2.28 |
SOCP2 | The “SOCP2” algorithm in [35] | 5.43 |
DEOR2 | The “DEOR” algorithm in [38] | 0.63 |
DEOR-fast2 | The “DEOR-fast” algorithm in [38] | 0.32 |
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Share and Cite
Ding, W.; Zhong, Q.; Wang, Y.; Guan, C.; Fang, B. Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation. Sensors 2022, 22, 733. https://doi.org/10.3390/s22030733
Ding W, Zhong Q, Wang Y, Guan C, Fang B. Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation. Sensors. 2022; 22(3):733. https://doi.org/10.3390/s22030733
Chicago/Turabian StyleDing, Weizhong, Qiubo Zhong, Yan Wang, Chao Guan, and Baofu Fang. 2022. "Target Localization in Wireless Sensor Networks Based on Received Signal Strength and Convex Relaxation" Sensors 22, no. 3: 733. https://doi.org/10.3390/s22030733