Target location and speed estimation by multistatic radar system using maximum likelihood approach

Wenlin, Yang; Chongyu, Wei

doi:10.1109/icosp.2000.893490

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…4, let the known positions from an arbitrary ith receiver Rx i be (x i , y i ) (i.e., coordinate of ith receiver), while R R i is estimation of the distance from target to ith receiver and μ s i is the azimuth angle formed by target and ith receiver. Using the governing geometries, the target localization by the ith receiver in the multistatic sonar system can be expressed by (12) and can be transformed as in (13) [7] x…”

Section: A Least Squarementioning

confidence: 99%

Performance comparison of target localization for active sonar systems

Kim

Hong

et al. 2008

IEEE Trans. Aerosp. Electron. Syst.

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The performance comparison of target localization for active sonar and effective fusion algorithms for target localization of multistatic sonar is investigated. Active sonar can be categorized into monostatic, bistatic, and multistatic, depending on the number of receiver elements. Target localization performance depends on the system configuration. The target localization performance of the monostatic, bistatic, and multistatic sonar systems is compared assuming that each element can receive both range and azimuth information of the target. In addition, we propose the weighted least square (WLS) algorithm, which incorporates judicial weighting to the conventional least square (LS) method, and an efficient sensor arrangement rule for target localization in the multistatic sonar system. The representative experimental results demonstrate that the target localization performance of multistatic sonar configuration is superior in terms of root-mean-square error (RMSE), to monostatic sonar and bistatic sonar by 35.98% and 37.45%, respectively, while the proposed WLS algorithm showed an improvement of 2.27% compared with the LS method.

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Section: A Least Squarementioning

confidence: 99%

Performance comparison of target localization for active sonar systems

Kim

Hong

et al. 2008

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…In the existing Wiener filter theory, the cost function E[ 2 ] is used [8]. However, in the multi-sensor localization scenario, the cost function should be modified as E[e T e] since the number of sensors is plural where…”

Section: Range Estimation Using Block-based Wiener Filtermentioning

confidence: 99%

“…Torrieri [1] derived a principal algorithm and analyzed for the hyperbolic location systems and direction-finding location systems, and used distance and angle information for the maximum likelihood (ML). Wenlin [2] adopted the range difference information of a T − R n multi-static radar system transmitting a new grouped waveform of FMCW signal. Foy [3] adopted an iterative least square (ILS) method, which used a combination of range and angular information, and showed that the Taylor-series method works for a variety of problems including mixed-measurement mode.…”

Section: Introductionmentioning

confidence: 99%

Source localization using block-based Wiener filter

Park

Hong

2010

IEICE Electron. Express

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A block-based Wiener filter is proposed for the source localization problem under an additive measurement error model. In this paper, we expand the Wiener filter, which is a single sensor case, to multi-sensor scenario. The proposed block-based Wiener filter method proves to be more efficient than the existing methods under various noise situations when the length of data set is more than two and as the number of data set increases, the performance is improved.

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Block LMS-based source localization using range measurement

Park

Hong

2011

Digital Signal Processing

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Target location and speed estimation by multistatic radar system using maximum likelihood approach

Cited by 4 publications

References 5 publications

Performance comparison of target localization for active sonar systems

Performance comparison of target localization for active sonar systems

Source localization using block-based Wiener filter

Block LMS-based source localization using range measurement

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