A novel time difference of arrival localization algorithm using a neural network ensemble model

Abstract

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

Improving TDOA Radar Performance in Jammed Areas through Neural Network-Based Signal Processing

“…Many papers, such as Refs. [ 6 , 13 , 30 ], have presented the use of neural networks as a complement to standard methods to pinpoint the location of an object. However, the authors believe that so far no work has considered the use of jammed signals and the usage of neural networks as an autoencoder for radars, which reduces the data flow from individual nodes to the center node.…”

“…In the early 21st century, the Internet of Things (IoT) and the increasing demand for location-based services led to further research and development in TDOA location estimation techniques. Some works already combined neural networks with TDOA systems, combining the outputs of all the individual NN to improve position estimate accuracy [ 6 ].…”

Artificial Intelligence Techniques for Mobile Station Location Estimation

“…Positioning methods using artificial intelligence techniques are based on measuring signal levels from all nearby access points or base stations, and their coordinates are known [5]. Neural networks are employed to optimize the RSSI method [6][7]. Also, for this purpose, a fuzzy-controller software or hardware solution may be created [8].…”

A Noise Robust Automatic Radiolocation Animal Tracking System

“…They tested the model on simulated TDoAs within a 2D 500 × 500 m space with seven receivers, the Root Mean Square Error (RMSE) of the localization was 17 m while the Chan algorithm leads to an ≈ 30 m RMSE. Zhang et al [29] presented a novel localization algorithm based upon a neural network ensemble to estimate the positions of objects in indoor multipathing environments. The ensemble method was tested on the simulated TDoA in a 2D 60×60 cm space.…”