Metin Aktas

Acoustic direction finding in highly reverberant environment with single acoustic vector sensor

We propose a novel wideband acoustic direction finding method for highly reverberant environments using measurements from a single Acoustic Vector Sensor (AVS). Since an AVS is small in size and can be effectively used within the full acoustic frequency bands, the proposed solution is suitable for wideband acoustic source localization. In particular, we introduce a novel approach to extract the signal portions that are not distorted with multipath signals and noise. We do not make any stochastic and sparseness assumptions regarding the underlying signal source. Hence, our approach can be applied to a wide range of wideband acoustic signals. We present experiments with acoustic signals that are specially exposed to long reverberations, where the Signal-to-Noise Ratio is as low as 0 dB. In these experiments, the proposed method reliably estimates the source direction with less than 5 degrees of error even under the introduced significantly high reverberation conditions.

A model-based analysis of extinction ratio effects on phase-OTDR distributed acoustic sensing system performance

Extinction ratio is an inherent limiting factor that has a direct effect on the detection performance of phase-OTDR based distributed acoustics sensing systems. In this work we present a model based analysis of Rayleigh scattering to simulate the effects of extinction ratio on the received signal under varying signal acquisition scenarios and system parameters. These signal acquisition scenarios are constructed to represent typically observed cases such as multiple vibration sources cluttered around the target vibration source to be detected, continuous wave light sources with center frequency drift, varying fiber optic cable lengths and varying ADC bit resolutions. Results show that an insufficient ER can result in high optical noise floor and effectively hide the effects of elaborate system improvement efforts.

Acoustic direction finding under high reverberation

One of the major challanges for acoustic source localization is to eliminate the multipath distortions due to the reverberation. In this paper, we propose a novel direction finding method that is robust to multipath distortions. Since we do not make any stochastic and sparseness assumptions regarding the underlying signal source, our method can be applied to a wide range of wideband acoustic signals. In particular, we introduce a novel approach to extract the signal portions that are not distorted with multipath signals and noise. Hence, the DOA estimation can be performed without being affected from the reverberation. Simulation results show that the proposed method reliably estimates the source direction even under the significantly high reverberation conditions.

D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).

Analysis of optical fading in phase-OTDR distributed acoustic sensing systems: the effects of fading in threat detection

Signal fading is a widely observed phenomenon in communication and sensing applications that results in spatially and temporally varying degradations in the received signal power. Specifically, for distributed acoustic sensing (DAS) applications based on phase sensitive Optical Time Domain Reflectometry (phase-OTDR), it is reported that optical signal fading is observed as random dramatic signal power fluctuations, which in turn cause substantial variations in threat detection sensitivity. In this paper, we study optical signal fading in the context of phase-OTDR based DAS from a signal processing perspective and analyze the undesired effects of fading on threat detection performance. Using a detailed phase-OTDR signal model, we analyze the effects of internal system parameters and external vibration source characteristics on optical fading. Based on these analyses, we define the conditions under which optical fading can manifest itself as a dramatic variation in threat detection performance.

Acoustic direction finding using single acoustic vector sensor under high reverberation

Abstract We propose a novel and robust method for acoustic direction finding, which is solely based on acoustic pressure and pressure gradient measurements from single Acoustic Vector Sensor (AVS). We do not make any stochastic and sparseness assumptions regarding the signal source and the environmental characteristics. Hence, our method can be applied to a wide range of wideband acoustic signals including the speech and noise-like signals in various environments. Our method identifies the “clean” time frequency bins that are not distorted by multipath signals and noise, and estimates the 2D-DOA angles at only those identified bins. Moreover, the identification of the clean bins and the corresponding DOA estimation are performed jointly in one framework in a computationally highly efficient manner. We mathematically and experimentally show that the false detection rate of the proposed method is zero, i.e., none of the time-frequency bins with multiple sources are wrongly labeled as single-source, when the source directions do not coincide. Therefore, our method is significantly more reliable and robust compared to the competing state-of-the-art methods that perform the time-frequency bin selection and the DOA estimation separately. The proposed method, for performed simulations, estimates the source direction with high accuracy (less than 1 degree error) even under significantly high reverberation conditions.

Deep learning based threat classification in distributed acoustic sensing systems

This paper presents a distributed acoustic sensing system based on direct detection phase-OTDR (optical time domain reflectometry) technique along with a deep learning based threat classification approach. Signal needs to be processed with denosing and signal conditioning algorithms prior to threat classification. For threat detection, power thresholding approach is taken. The developed system and algorithms are tested experimentally using a buried fiber optic cable for distances up to 40 kilometers. The results show that by using appropriate signal conditioning and threat detection algorithms, six different activities such as manual digging and walking/running can be classified at 40 kilometers distance and up to 10 meters away from the fiber optic cable.

Deep learning based multi-threat classification for phase-OTDR fiber optic distributed acoustic sensing applications

This paper presents a distributed acoustic sensing based linear asset protection system along with novel signal processing and threat classification techniques. The sensing system is realized by direct detection phase-OTDR (optical time domain reflectometry). An effective signal preprocessing approach for noise reduction that aims to improve the threat detection capability of the system is proposed. The proposed method is not limited to direct detection based systems and is applicable to any phase-OTDR system. A novel deep learning based threat clas- sification method is presented to identify various types of threats. The method uses a deep convolutional neural network trained with real sensor data. Experiments are conducted with an ITU-T G.652 fiber optic cable buried at one meter depth. The effects of applied preprocessing methods on both threat detection and threat classification performance are analyzed. The proposed preprocessing method is compared with the methods commonly used in the literature such as time differencing and wavelet denoising. The results show that by applying the proposed signal conditioning, event detection and classification methods, threat classification accuracies above 93% can be achieved with six typically observed activities, namely, walking, digging with pickaxe, digging with shovel, digging with harrow, strong wind and facility noise caused by water pipes, generators or air conditioning, at ranges of up to 40 km. The proposed classification strategy can easily be generalized for identifying different types of threats that are of interest in various security applications.

Directional phased based 3D acoustic enhancement

In order to exploit the phase information for a better 3D acoustic source enhancement, we design a novel array configuration based on three orthogonally placed Acoustic Vector Sensors (AVS), which effectively uses the directional array response in phase in addition to the one in amplitude. The proposed design is called “Phased AVS Array” (PAVSA). We compare the signal enhancement performance of our design with the well-known configurations, e.g., ULA and Spherical arrays, in terms of the noise and interference suppression. The proposed design PAVSA is experimentally shown to perform comparably to the conventional omni-directional microphone arrays, however, with significantly smaller array size and smaller number of microphones while being, moreover, superior to the enhancement performance of the 1D and 2D AVS arrangement strategies.