• Title/Summary/Keyword: Noise localization

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A RSS-Based Localization Method Utilizing Robust Statistics for Wireless Sensor Networks under Non-Gaussian Noise (비 가우시안 잡음이 존재하는 무선 센서 네트워크에서 Robust Statistics를 활용하는 수신신호세기기반의 위치 추정 기법)

  • Ahn, Tae-Joon;Koo, In-Soo
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.11 no.3
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    • pp.23-30
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    • 2011

  • In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.

  • https://doi.org/10.7236/JIWIT.2011.11.3.023 인용 PDF KSCI

Impulsive sound localization using crest factor of the time-domain beamformer output (빔형성기 출력의 파고율을 이용한 충격음의 방향 추정)

  • Seo, Dae-Hoon;Choi, Jung-Woo;Kim, Yang-Hann
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2014.10a
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    • pp.713-717
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    • 2014

  • This paper presents a beamforming technique for locating impulsive sound source. The conventional frequency-domain beamformer is advantageous for localizing noise sources for a certain frequency band of concern, but the existence of many frequency components in the wide-band spectrum of impulsive noise makes the beamforming image less clear. In contrast to a frequency-domain beamformer, it has been reported that a time-domain beamformer can be better suited for transient signals. Although both frequency- and time-domain beamformers produce the same result for the beamforming power, which is defined as the RMS value of its output, we can use alternative directional estimators such as the peak value and crest factor to enhance the performance of a time-domain beamformer. In this study, the performance of three different directional estimators, the peak, crest factor and RMS output values, are investigated and compared with the incoherent interfering noise embedded in multiple microphone signals. The proposed formula is verified via experiments in an anechoic chamber using a uniformly spaced linear array. The results show that the peak estimation of beamformer output determines the location with better spatial resolution and a lower side lobe level than crest factor and RMS estimation in noise free condition, but it is possible to accurately estimate the direction of the impulsive sound source using crest factor estimation in noisy environment with stationary interfering noise.

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Indoor Mobile Localization System and Stabilization of Localization Performance using Pre-filtering

  • Ko, Sang-Il;Choi, Jong-Suk;Kim, Byoung-Hoon
    • International Journal of Control, Automation, and Systems
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    • v.6 no.2
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    • pp.204-213
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    • 2008

  • In this paper, we present the practical application of an Unscented Kalman Filter (UKF) for an Indoor Mobile Localization System using ultrasonic sensors. It is true that many kinds of localization techniques have been researched for several years in order to contribute to the realization of a ubiquitous system; particularly, such a ubiquitous system needs a high degree of accuracy to be practical and efficient. Unfortunately, a number of localization systems for indoor space do not have sufficient accuracy to establish any special task such as precise position control of a moving target even though they require comparatively high developmental cost. Therefore, we developed an Indoor Mobile Localization System having high localization performance; specifically, the Unscented Kalman Filter is applied for improving the localization accuracy. In addition, we also present the additive filter named 'Pre-filtering' to compensate the performance of the estimation algorithm. Pre-filtering has been developed to overcome negative effects from unexpected external noise so that localization through the Unscented Kalman Filter has come to be stable. Moreover, we tried to demonstrate the performance comparison of the Unscented Kalman Filter and another estimation algorithm, such as the Unscented Particle Filter (UPF), through simulation for our system.

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Noise source localization using comparison between candidate signal and beamformer output in time domain (시간 영역의 빔출력과 후보 신호 사이의 비교를 통한 소음원의 위치 추정)

  • Kim, Koo-Hwan;Kim, Yang-Hann
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 2010.10a
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    • pp.543-543
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    • 2010

  • The objective of this research is estimating the location of interested sound source by using the similarity between a beamformer output in time domain and the candidate signal. The waveform of beamformer output at the location of sound source is similar with the waveform emitted by that source. To estimate the location of sound source by using this feature, we define quantified similarity between candidate signal and beamformer output. The candidate signal describes the signal which is generated by interested source. In this paper, similarity is defined by four methods. The two methods use time vector comparison, and the other two methods use time-frequency map or linear prediction coefficients. To figure out the results and performance of localization by using similarities, we demonstrate two conditions. The one is when two pure tone sources exist and the other condition is when several bird sounds exist. As a consequence, inner product with two time-vectors and structural similarity with spectrograms can estimate the locations of interest sound source.

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