Compensated robust least‐squares estimator for target localisation in sensor network using time difference of arrival measurements

2013 ◽  
Vol 7 (8) ◽  
pp. 664-673 ◽  
Author(s):  
Ka Hyung Choi ◽  
Won‐Sang Ra ◽  
Jin Bae Park ◽  
Tae Sung Yoon
Keyword(s):  
Least Squares ◽  
Sensor Network ◽  
Time Difference ◽  
Least Squares Estimator ◽  
Time Difference Of Arrival ◽  
Target Localisation ◽  
Robust Least Squares

scholarly journals Time Difference of Arrival (TDoA) Localization Combining Weighted Least Squares and Firefly Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2554 ◽  
Author(s):  
Peng Wu ◽  
Shaojing Su ◽  
Zhen Zuo ◽  
Xiaojun Guo ◽  
Bei Sun ◽  
...  
Keyword(s):  
Least Squares ◽  
Firefly Algorithm ◽  
Weighted Least Squares ◽  
Initial Position ◽  
Time Difference ◽  
Sensor Nodes ◽  
Passive Location ◽  
Search Region ◽  
Time Difference Of Arrival ◽  
Location Methods

Time difference of arrival (TDoA) based on a group of sensor nodes with known locations has been widely used to locate targets. Two-step weighted least squares (TSWLS), constrained weighted least squares (CWLS), and Newton–Raphson (NR) iteration are commonly used passive location methods, among which the initial position is needed and the complexity is high. This paper proposes a hybrid firefly algorithm (hybrid-FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search region for the FA method. Simulations showed that the hybrid-FA method required far fewer iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments were conducted to compare the results of hybrid-FA with other methods. The findings indicated that the root-mean-square error (RMSE) and mean distance error of the hybrid-FA method were lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid-FA outperformed the NR, TSWLS, and GA for TDoA measurement.

scholarly journals 3D Tdoa Problem Solution with Four Receiving Nodes

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2892 ◽  
Author(s):  
Javier Díez-González ◽  
Rubén Álvarez ◽  
Lidia Sánchez-González ◽  
Laura Fernández-Robles ◽  
Hilde Pérez ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Confidence Interval ◽  
Sensor Network ◽  
Three Dimensional ◽  
Time Difference ◽  
Problem Solution ◽  
Time Difference Of Arrival ◽  
Geometric Properties ◽  
Positioning Systems

Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s.

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