Passive Acoustic Source Localization at a Low Sampling Rate Based on a Five-Element Cross Microphone Array

Kan, Yue; Wang, Pengfei; Zha, Fusheng; Li, Mantian; Gao, Wa; Song, Biao

doi:10.3390/s150613326

Cited by 13 publications

(12 citation statements)

References 22 publications

(29 reference statements)

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“…When multiplied by the speed of the light, c , this results in a maximum location error of 3 cm for each time measurement. In this case, if the TDoA between two antennas multiplied by c is 1 m, digitalizing the discrete TDoA, with a 3-cm error, will result in an overall location error of 0.99 m or 1.02 m. However, suitable interpolation between time sampled points can reduce the location error magnitude [ 29 ]. In the previous example, interpolating with 10 samples between time points, the effective sampling frequency may be increased to

= 100 GS/s, and

is effectively reduced to 0.01 ns, resulting in an location error of around 3 mm (see Section 4.2 ).…”

Section: Methodsmentioning

confidence: 99%

Survey on the Performance of Source Localization Algorithms

Fresno

Robles

Martínez-Tarifa

et al. 2017

Sensors

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The localization of emitters using an array of sensors or antennas is a prevalent issue approached in several applications. There exist different techniques for source localization, which can be classified into multilateration, received signal strength (RSS) and proximity methods. The performance of multilateration techniques relies on measured time variables: the time of flight (ToF) of the emission from the emitter to the sensor, the time differences of arrival (TDoA) of the emission between sensors and the pseudo-time of flight (pToF) of the emission to the sensors. The multilateration algorithms presented and compared in this paper can be classified as iterative and non-iterative methods. Both standard least squares (SLS) and hyperbolic least squares (HLS) are iterative and based on the Newton–Raphson technique to solve the non-linear equation system. The metaheuristic technique particle swarm optimization (PSO) used for source localisation is also studied. This optimization technique estimates the source position as the optimum of an objective function based on HLS and is also iterative in nature. Three non-iterative algorithms, namely the hyperbolic positioning algorithms (HPA), the maximum likelihood estimator (MLE) and Bancroft algorithm, are also presented. A non-iterative combined algorithm, MLE-HLS, based on MLE and HLS, is further proposed in this paper. The performance of all algorithms is analysed and compared in terms of accuracy in the localization of the position of the emitter and in terms of computational time. The analysis is also undertaken with three different sensor layouts since the positions of the sensors affect the localization; several source positions are also evaluated to make the comparison more robust. The analysis is carried out using theoretical time differences, as well as including errors due to the effect of digital sampling of the time variables. It is shown that the most balanced algorithm, yielding better results than the other algorithms in terms of accuracy and short computational time, is the combined MLE-HLS algorithm.

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= 100 GS/s, and

is effectively reduced to 0.01 ns, resulting in an location error of around 3 mm (see Section 4.2 ).…”

Section: Methodsmentioning

confidence: 99%

Survey on the Performance of Source Localization Algorithms

Fresno

Robles

Martínez-Tarifa

et al. 2017

Sensors

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show abstract

“…The simulation charge amount of each sensor can be obtained through Equation (19), and then the distance measurement accuracy can be calculated from Equation (17). The calculation of elevation and azimuth errors is the same.…”

Section: Error Modelmentioning

confidence: 99%

“…The technology of target localization and tracking has become a research hotspot in various fields [16][17][18][19]. Reference [20] proposed a WSN (wireless sensor network) positioning algorithm based on fuzzy decision-making.…”

Section: Introductionmentioning

confidence: 99%

Method for Localization Aerial Target in AC Electric Field Based on Sensor Circular Array

Zhang

Zhou

Suo

et al. 2020

Sensors

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The traditional method of using electric field sensors to realize early warning of electric power safety distance cannot measure the distance of dangerous sources. Therefore, aiming at the electric field with a frequency of 50 to 60 Hz (AC electric field), a new method for localization of aerial AC target by the capacitive one-dimensional spherical electric field sensor circular array is studied. This method can directly calculate the distance, elevation, and azimuth of the detector from the dangerous source. By combining the measurement principle of the spherical electric field sensor and the plane circular array theory, a mathematical model for the localization of aerial targets in an AC electric field is established. An error model was established using Gaussian noise and the effects of different layout parameters on the localization error were simulated. Based on mutual interference between sensors, minimum induced charge, and localization error, an optimal model for sensor layout was established, and it was solved by using genetic algorithms. The optimization results show that when the number of sensors is 4, the array radius is 20 cm, and the sensor radius is 1.5 cm, the ranging error is 8.4%. The detector was developed based on the layout parameters obtained from the optimization results, and the localization method was experimentally verified at 10 and 35 kV alarm distances. The experimental results show that when the detector is located at 10 kV alarm distance, the distance error is 0.18 m, the elevation error is 6.8°, and the azimuth error is 4.57°, and when it is located at 35 kV alarm distance, the distance error is 0.2 m, the elevation error is 4.8°, and the azimuth error is 5.14°, which meets the safety distance warning requirements of 10 and 35 kV voltage levels.

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“…Cross-correlation algorithms are widely used in various fields of digital signal processing. Typical applications comprise image processing [1], audio signal processing [2], impedance spectroscopy [3] and sound source localization [2]. Especially for sound source localization, approaches like time difference of arrival (TDOA) are used, a technique to calculate the position of a sound source by computing the delay between incoming sound signals of spatially divided microphones [4].…”

Section: Introductionmentioning

confidence: 99%

A highly scalable FPGA implementation for cross-correlation with up-sampling support

Schmidt¹,

Blokzyl²,

Hardt³

2018

Impedance Spectroscopy

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Cross-correlation is a fundamental algorithm which is used in many fields of digital signal processing. One important application is sound source localization. For this purpose, actual research shows a significant improvement of localization accuracy when using up-sampling-based algorithms. These state-of-the-art approaches are challenging for embedded realizations as up-sampling in combination with high sample rates and measurement window lengths leads to huge amounts of data to be processed and stored. This chapter presents hardware acceleration techniques for cross-correlation with up-sampling support. The work introduces a novel processing architecture which addresses large-scale memory consumption compared to conventional solutions. The novel design introduces a highly scalable field programmable gate array realization with linear memory complexity and an interpolation factor-independent runtime.

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Passive Acoustic Source Localization at a Low Sampling Rate Based on a Five-Element Cross Microphone Array

Cited by 13 publications

References 22 publications

Survey on the Performance of Source Localization Algorithms

Survey on the Performance of Source Localization Algorithms

Method for Localization Aerial Target in AC Electric Field Based on Sensor Circular Array

A highly scalable FPGA implementation for cross-correlation with up-sampling support

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