AI Article Synopsis
- The paper discusses advancements in multistatic active sonar systems, which improve target detection and localization through spatial diversity.
- It introduces three main contributions: a hybrid dense-sparse method for creating accurate range-Doppler images with low sidelobe levels, a generalized K-Means clustering method for associating range measurements from various transmitter-receiver pairs, and an extended invariance principle-based weighted least-squares method for estimating target position and velocity.
- The effectiveness of these techniques is validated through numerical examples.
Article Abstract
Multistatic active sonar systems involve the transmission and reception of multiple probing sequences and can achieve significantly enhanced performance of target detection and localization through exploiting spatial diversity. This paper mainly focuses on two signal processing aspects of such systems, namely, enhanced range-Doppler imaging and improved target parameter estimation. The main contributions of this paper are (1) a hybrid dense-sparse method is proposed to generate range-Doppler images with both low sidelobe levels and high accuracy; (2) a generalized K-Means clustering (GKC) method for target association is developed to associate the range measurements from different transmitter-receiver pairs, which is actually a range fitting procedure; (3) the extended invariance principle-based weighted least-squares method is developed for accurate target position and velocity estimation. The effectiveness of the proposed multistatic active sonar signal processing techniques is verified using numerical examples.
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| http://dx.doi.org/10.1121/1.4809648 | DOI Listing |
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