Underwater acoustic source localization method based on TDOA with particle filtering

Acoustic emission source localization is the basic function of the application of acoustic emission technology. For complex structures, mathematical analysis positioning algorithms cannot be obtained, and accurate acoustic emission source localization cannot be obtained, which has always been one of the problems in the actual applications of acoustic emission technology. To solve this problem, this paper proposes an acoustic emission source localization method based on deep learning, which can obtain high-precision acoustic emission source positioning without the need for mathematical analysis positioning algorithms. The AI deep learning acoustic emission source localization method adopts the method of meshing, dividing the grid in the measured structure, generating label data at the grid position, using the label data to conduct AI deep learning training to establish a positioning model, and using the trained model to analyze the actual acoustic emission data (non-labeled data) to locate the acoustic emission source. This paper uses a multi-layer perceptron model to train multi-dimensional features (arrival time and amplitude). This method effectively improves the accuracy of acoustic emission source localization. Experimental results show that the positioning accuracy of the designed deep learning model in the test set reaches 99.625%, which is significantly better than the traditional Time Difference of Arrival (TDOA) positioning algorithm. In addition, this paper further verifies the stability and reliability of the model in localization tasks through credibility metrics such as Score, Margin and Entropy. This article provides a new solution for locating acoustic emission sources in complex structures, and lays a theoretical and practical foundation for the future development of non-destructive testing technology.

In this paper, an acoustic source localization method based on 4-element cross-array is presented, and an acoustic source localization system is designed. Four acoustic sensors are employed to collect the acoustic source signal, after several steps as amplifying, processing, AD conversion, generalized cross-correlation function method is used to compute the time difference of the signal arrival to the four sensors, then the position of the acoustic source can be determined. Experiments show that our system can achieve good performance in both speed and accuracy. The acoustic source localization system can be incorporated into a motion estimation system with low cost inertial sensors to increase estimation accuracy.

For shallow underwater acoustic sources in deep-sea environments, the received signals in the direct wave zone exhibit distinct multipath arrival structures during large-depth reception, which can be exploited for passive source localization. This paper presents a method for estimating source depth and range through joint matching of arrival angles and time-delay differences between the direct wave and the first-order sea-surface reflected wave. The arrival angles are obtained from deep-sea particle velocity fields based on ray theory, while the time-delay differences are extracted from the autocorrelation function of the received signals. Simulation results show that both arrival angles and time-delay differences can be accurately estimated simultaneously at signal-to-noise ratios (SNRs) above 10 dB, allowing high-precision passive source localization with the proposed method. Under fixed source-receiver geometry, seabed topography and sea-surface roughness significantly alter acoustic propagation paths. Seabed variations affect paths of both direct and bottom-interacted waves, and sea-surface roughness specifically distorts the path of the first-order sea-surface reflected wave, thereby reducing the accuracy of time delay difference estimation in matched field processing. Validated using data from a 2020 deep-sea acoustic propagation experiment in the Western Pacific—where a single vector hydrophone captured signals from a towed source—the method effectively estimated the depth and range of shallow underwater acoustic sources within a 15 km range, with mean relative errors of 5.5% in depth and 6.2% in range. Compared to conventional methods that use only arrival angles or time-delay differences, this joint-matching strategy offers an efficient and robust solution for passive localization of underwater acoustic sources in the deep-sea direct wave zone.

The significant challenge in human computer interaction is to create tangible interfaces that will make digital world accessible through augmented physical surfaces like walls and windows. In this paper, various acoustic source localization methods are proposed which have the potential to covert a physical object into a tracking sensitive interface. The Spatial Likelihood method has been used to locate acoustic source in real time by summing the spatial likelihood from all sensors. The source location is obtained from searching the maximum in the likelihood map. The data collected from the sensors is pre-processed and filtered for improvement of the accuracy of source localization. Finally a sensor fusion algorithm based on least squared error is presented to minimize the error while positioning the source. Promising results have been achieved experimentally for the application of acoustic tangible interfaces.

The Time Difference of Arrival (TDOA) of an acoustic source with respect to a given pair of microphones is typically estimated as the maximizer of a Measure of Synchrony (MoS) between the pair of microphone signals, within the interval [−D/v, D/v], where D is the inter-microphone distance and v is the speed of sound. In practical enclosures, phantom sources are created due to reverberation, which can cause false peaks in the MoS, leading to erroneous TDOA estimates. In several practical enclosures such as meeting rooms, conference halls, lecture halls, etc., the regions which can admit an acoustic source are restricted, because of furniture and other fixtures. Consequently, it is possible to acquire prior knowledge of the region which can accommodate an acoustic source. In this paper, we propose an approach to utilize this prior knowledge of source region for accurate TDOA estimation. We transform the prior knowledge of source region into an interval of possible TDOAs using a newly developed concept of family of hyperboloids. This interval, referred to as Region Constrained TDOA Interval (RCTI) is shown to be smaller than [−D/v, D/v]. TDOA is then estimated as the maximizer of a suitable MoS within the RCTI. We demonstrate that estimating TDOA within RCTI, with generalized cross correlation (GCC) class of functions as MoS, is more robust to reverberation than estimating TDOA within [−D/v, D/v].

Acoustic source localization in an anisotropic plate without knowing its material properties – A new approach

A microphone clustering and back propagation (BP) neural network based acoustic source localization method using distributed microphone arrays in an intelligent meeting room is proposed. In the proposed method, a novel clustering algorithm is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to select clusters which are small and close to the source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each selected cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization approaches confirm the validity of the proposed method.

An intelligent system is proposed to locate and classify audio source signals in large spaces. The system is composed of a sparsely distributed microphone array and an artificial intelligence (AI) system. A sparse array aimed for acoustic source localization and signal extraction is configured. The localization method is based on time difference of arrival (TDOA). This method begins with estimation of the TDOAs among at least 4 sensors, with the aid of a subspace-based time delay estimation algorithm. Next, a constrained least squares (CLS) algorithm is applied to locate the source in accord with the estimated TDOAs. Once the source is located, the source signal is extracted by using the minimum variance distortionless response (MVDR) beamformer and a postfilter. The extracted audio signals are further classified in light of machine learning. Convolutional Long Short-Term Memory (ConvLSTM) plays a central role in the AI-based classifier. Mel-Frequency Spectral Coefficients (MFSC) serves as the input layer in the ConvLSTM. The performance of the proposed system is quantified by using localization error, audio quality, and F 1 scores. Simulations are undertaken to validate the proposed TDOA-based localization and separation technique.

Agriculture is becoming increasingly reliant upon accurate data from sensor arrays, with localization an emerging application in the livestock industry. Ground-based time difference of arrival (TDoA) radio location methods have the advantage of being lightweight and exhibit higher energy efficiency than methods reliant upon Global Navigation Satellite Systems (GNSS). Such methods can employ small primary battery cells, rather than rechargeable cells, and still deliver a multi-year deployment. In this paper, we present a novel deep learning algorithm adapted from a one-dimensional U-Net implementing a convolutional neural network (CNN) model, originally developed for the task of semantic segmentation. The presented model (ResUnet-1d) both converts TDoA sequences directly to positions and reduces positional errors introduced by sources such as multipathing. We have evaluated the model using simulated animal movements in the form of TDoA position sequences in combination with real-world distributions of TDoA error. These animal tracks were simulated at various step intervals to mimic potential TDoA transmission intervals. We compare ResUnet-1d to a Kalman filter to evaluate the performance of our algorithm to a more traditional noise reduction approach. On average, for simulated tracks having added noise with a standard deviation of 50 m, the described approach was able to reduce localization error by between 66.3% and 73.6%. The Kalman filter only achieved a reduction of between 8.0% and 22.5%. For a scenario with larger added noise having a standard deviation of 100 m, the described approach was able to reduce average localization error by between 76.2% and 81.9%. The Kalman filter only achieved a reduction of between 31.0% and 39.1%. Results indicate that this novel 1D CNN U-Net like encoder/decoder for TDoA location error correction outperforms the Kalman filter. It is able to reduce average localization errors to between 16 and 34 m across all simulated experimental treatments while the uncorrected average TDoA error ranged from 55 to 188 m.

Recently, indoor positioning has been one of the hot topics in the field of navigation and positioning. Among different solutions on indoor positioning, positioning with acoustic signals has its promise due to its relatively high accuracy in the line of sight scenarios, low cost, and ease of being implemented in smartphones. In this work, a novel acoustic positioning method, called RATBILS, is proposed, in which encoded chirp acoustic signals are modulated and transmitted by different acoustic base stations. The smartphones receive the signals and perform the following three steps: (1) preprocessing; (2) time of arrival (TOA) estimation; and (3) time difference of arrival (TDOA) calculation and location estimation. In the preprocessing stage, we use band pass filters to filter out low-frequency noise from the environment. At the same time, we perform a signal decoding function in order to lock onto the positioning source. In the TOA estimation stage, we conduct both coarse and fine detection to enhance the accuracy and robustness of TOA estimation. The primary goal of coarse detection is to establish a noise range for fine detection. The main objective of fine detection is to emphasize the intensity of the first arrival diameter and resistance with multipath and non-line-of-sight (NLOS) caused by human body obstruction. In the TDOA calculation and location estimation stage, we estimate the TDOA based on the TOA estimation and then use the TDOA results for position estimation. In order to evaluate the performance of the proposed RATBILS system, two indoor field tests are carried out. The test results show that the RATBILS system achieves a positioning error of 0.23 m at 92% in region 1 of scene 1 and is superior to the traditional threshold method. The RATBILS system achieves a positioning error of 0.56 m at 92% in region 2 of scene 1 and is superior to the traditional threshold method. In scene 2, the maximum average positioning error was 1.26 m, which is better than the 3.33 m and 3.87 m of the two traditional threshold methods.

Time–distance domain transformation for Acoustic Emission source localization in thin metallic plates

Acoustic source localization has been an hot research topic with widespread applications in many fields. In the noisy environment or when the reverberation is considerable, the source localization problem becomes challenging and many existing algorithms deteriorate. The paper proposes a robust algorithm which combines the RANdom SAmple Consensus (RANSAC) algorithm, and the Generalized Cross-Correlation (GCC) based Time Difference of Arrival (TDOA). Experiments in real world data show that the proposed algorithm has significantly better performance than the traditional algorithm.

Miniaturized multi-topology acoustic source localization network based on intelligent microsystem

Composite materials have become increasingly vital in modern industry due to their exceptional corrosion resistance and high strength-to-weight ratio. However, under varying environmental conditions and mechanical loading, these materials are prone to progressive damage and failure, making early detection and maintenance critical. Traditional acoustic emission (AE) analysis methods face significant limitations in damage monitoring due to the anisotropic nature of composites, which leads to severe modal dispersion in acquired signals and complicates mode identification. These challenges undermine the accuracy and reliability of conventional source localization techniques.To address these issues, this study proposes a novel AE source localization method that combines modal arrival time and wave velocity analysis. The approach leverages time difference of arrival (TDOA) and modal wave velocity data captured by a three-sensor array to determine both the angular direction and precise planar coordinates of AE sources. Experimental validation demonstrates that the proposed method achieves high localization accuracy within a 350 mm radius from the sensor array center. These findings advance the capability for real-time structural health monitoring in composite structures, offering improved diagnostic precision for industrial applications.

In this paper we consider the problem of tracking multiple acoustic sources in reverberant environments. The solution that we propose is based on the combination of two techniques. A blind source separation (BSS) method known as TRINICON [5] is applied to the signals acquired by the microphone arrays. The TRINICON de-mixing filters are used to obtain the Time Differences of Arrival (TDOAs), which are related to the source location through a nonlinear function. A particle filter is then applied in order to localize the sources. Particles move according to a swarm-like dynamics, which significatively reduces the number of particles involved with respect to traditional particle filter. We discuss results for the case of two sources and four microphone pairs. In addition, we propose a method, based on detecting source inactivity, which overcomes the ambiguities that intrinsically arise when only two microphone pairs are used. Experimental results demonstrate that the average localization error on a variety of pseudo-random trajectories is around 40 cm when the T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">60</sub> reverberation time is 0.6s.