Position estimation of rotating sound source using Kalman filtering based on time difference of arrival measurements

2013 ◽  
Vol 134 (5) ◽  
pp. 4109-4109
Author(s):  
Jaehyung Lee ◽  
Young-Ju Go ◽  
Jong-Soo Choi
Keyword(s):  
Kalman Filtering ◽  
Sound Source ◽  
Position Estimation ◽  
Time Difference ◽  
Time Difference Of Arrival

scholarly journals Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3024 ◽  
Author(s):  
Álvarez ◽  
Díez-González ◽  
Alonso ◽  
Fernández-Robles ◽  
Castejón-Limas ◽  
...  
Keyword(s):  
Path Loss ◽  
Position Estimation ◽  
Propagation Model ◽  
Time Difference ◽  
Time Difference Of Arrival ◽  
Wide Range ◽  
Multiple Sensor ◽  
Time Determination ◽  
Log Normal ◽  
First Time

The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain.

scholarly journals A two-stage approach for passive sound source localization based on the SRP-PHAT algorithm

2020 ◽  
Vol 9 ◽  
Author(s):  
M.A. Awad-Alla ◽  
Ahmed Hamdy ◽  
Farid A. Tolbah ◽  
Moatasem A. Shahin ◽  
M.A. Abdelaziz
Keyword(s):  
Source Localization ◽  
Sound Source ◽  
Microphone Array ◽  
Search Space ◽  
Time Difference ◽  
Sound Source Localization ◽  
Time Difference Of Arrival ◽  
Error Coefficient ◽  
Viable Approach ◽  
Application Requirements

Abstract This paper presents a different approach to tackle the Sound Source Localization (SSL) problem apply on a compact microphone array that can be mounted on top of a small moving robot in an indoor environment. Sound source localization approaches can be categorized into the three main categories; Time Difference of Arrival (TDOA), high-resolution subspace-based methods, and steered beamformer-based methods. Each method has its limitations according to the search or application requirements. Steered beamformer-based method will be used in this paper because it has proven to be robust to ambient noise and reverberation to a certain extent. The most successful and used algorithm of this method is the SRP-PHAT algorithm. The main limitation of SRP-PHAT algorithm is the computational burden resulting from the search process, this limitation comes from searching among all possible candidate locations in the searching space for the location that maximizes a certain function. The aim of this paper is to develop a computationally viable approach to find the coordinate location of a sound source with acceptable accuracy. The proposed approach comprises two stages: the first stage contracts the search space by estimating the Direction of Arrival (DoA) vector from the time difference of arrival with an addition of reasonable error coefficient around the vector to make sure that the sound source locates inside the estimated region, the second stage is to apply the SRP-PHAT algorithm to search only in this contracted region for the source location. The AV16.3 corpus was used to evaluate the proposed approach, extensive experiments have been carried out to verify the reliability of the approach. The results showed that the proposed approach was successful in obtaining good results compared to the conventional SRP-PHAT algorithm.

Distributed Particle Filter With Online Model Learning for Localization Using Time-Difference-of-Arrival (TDOA) Measurements

2020 ◽  
Author(s):  
Chandler J. Panetta ◽  
Osama N. Ennasr ◽  
Xiaobo Tan
Keyword(s):  
Particle Filter ◽  
Nonlinear Filtering ◽  
Probability Distributions ◽  
Position Estimation ◽  
Propagation Model ◽  
Time Difference ◽  
Moving Target ◽  
Model Learning ◽  
Movement Model ◽  
Time Difference Of Arrival

Abstract The problem of localizing a moving target arises in various forms in wireless sensor networks. Deploying multiple sensing receivers and using the time-difference-of-arrival (TDOA) of the target’s emitted signal is widely considered an effective localization technique. Traditionally, TDOA-based algorithms adopt a centralized approach where all measurements are sent to a predefined reference node for position estimation. More recently, distributed TDOA-based localization algorithms have been shown to improve the robustness of these estimates. For target models governed by highly stochastic processes, the method of nonlinear filtering and state estimation must be carefully considered. In this work, a distributed TDOA-based particle filter algorithm is proposed for localizing a moving target modeled by a discrete-time correlated random walk (DCRW). We present a method for using data collected by the particle filter to estimate the unknown probability distributions of the target’s movement model, and then apply the distribution estimates to recursively update the particle filter’s propagation model. The performance of the distributed approach is evaluated through numerical simulation, and we show the benefit of using a particle filter with online model learning by comparing it with the non-adaptive approach.

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