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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.

We address the problem of license plate detection in video surveillance systems. The Adaboost based approach, known for relative ease of implementation, makes use of discriminative features such as edges or Haar-like features. In this paper, we propose a novel detection algorithm based on local structure patterns for license plate detection. The proposed algorithm includes post-processing methods to reduce false positive rate using positional and color information of license plates. Experimental results demonstrate effectiveness of the proposed method compared to both the edge and Haar-like feature based methods.

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