Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight
Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the auth...
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| Veröffentlicht in: | IEEE photonics journal 2020-06, Vol.12 (3), p.1-15 |
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| creator | Ren, Yu Xie, Zongliang Luo, Yihan Xu, Shaoxiong Ma, Haotong Tan, Yi |
| description | Recently, non-line-of-sight (NLOS) detection based on time of flight (TOF) has been investigated. In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. The simplicity, high efficiency, and automation of this algorithm make it applicable for tracking a hidden moving object. |
| doi_str_mv | 10.1109/JPHOT.2020.2994784 |
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In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. 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In order to simulate the NLOS location of a hidden object, we derive the signal scattered by the object and build a model of photon flight based on photon scattering and propagation. To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. The Gauss filter is proposed to extract the TOF of the scattering sources of interest without a priori information or manual judgment of the useful scattered signal by filtering the disturbance out of the histogram. The hidden object can then be located by TOF processing. Compared with previous work using a fitting algorithm, the Gauss filtering approach preserves more waveform information and presents improved positioning accuracy and robustness under the influence of noise, which is demonstrated in both simulation and experiment. It is possible to locate a NLOS object automatically through filtering identification of the object signal. The simplicity, high efficiency, and automation of this algorithm make it applicable for tracking a hidden moving object.</description><subject>Algorithms</subject><subject>Bidirectional reflectance</subject><subject>bidirectional reflectance distribution function</subject><subject>Computer simulation</subject><subject>Detectors</subject><subject>Distribution functions</subject><subject>Filtering</subject><subject>Filtering algorithms</subject><subject>Filtration</subject><subject>Gauss filter</subject><subject>Gaussian process</subject><subject>Histograms</subject><subject>Line of sight</subject><subject>Moving object recognition</subject><subject>Non-line-of-sight detection</subject><subject>Nonlinear optics</subject><subject>Photon scatter</subject><subject>Photonics</subject><subject>Photons</subject><subject>Scattering</subject><subject>Surface treatment</subject><subject>Waveforms</subject><issn>1943-0655</issn><issn>1943-0647</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNo9UctOwzAQjBBIQOEH4GKJc4rXcRz7CIgWUHlIgDhwsBw_WlchBjs98Pe4tOppV7Mzs7uaojgDPAbA4vLh5e75bUwwwWMiBG043SuOQNCqxIw2-7u-rg-L45SWGDMBtTgqPp9CX858b8vgylc_XwxoFrQafOjRhx8WaKpWKaGJ7wYbfT9HV908xDz4QtcqWYMyT6HHYGyHgkMvizBkZNKtnU6KA6e6ZE-3dVS8T27fbu7K2fP0_uZqVmqK66EU3DgLrmo5sU6BZkwZ4yrMLVZGgIaqBQotYdRoRqpWgVAmI8Dyby1l1ai43_iaoJbyO_ovFX9lUF7-AyHOpYqD152VtWZOG3DAuaEN5i2tWyCNANY2NeFN9rrYeH3H8LOyaZDLsIp9Pl8SCg0lHCqSWWTD0jGkFK3bbQUs14HI_0DkOhC5DSSLzjcib63dCQQWFFNa_QFZeYU9</recordid><startdate>20200601</startdate><enddate>20200601</enddate><creator>Ren, Yu</creator><creator>Xie, Zongliang</creator><creator>Luo, Yihan</creator><creator>Xu, Shaoxiong</creator><creator>Ma, Haotong</creator><creator>Tan, Yi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Bidirectional reflectance bidirectional reflectance distribution function Computer simulation Detectors Distribution functions Filtering Filtering algorithms Filtration Gauss filter Gaussian process Histograms Line of sight Moving object recognition Non-line-of-sight detection Nonlinear optics Photon scatter Photonics Photons Scattering Surface treatment Waveforms |
| title | Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight |
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