Target track extraction in high false density environments using multiple hypothetical frame selection MLPDA

Masanori Mori; Takashi Matsuzaki; Hiroshi Kameda; Toru Umezawa

doi:10.1117/12.2022613

30 September 2013 Target track extraction in high false density environments using multiple hypothetical frame selection MLPDA

Masanori Mori, Takashi Matsuzaki, Hiroshi Kameda, Toru Umezawa

Author Affiliations +

Proceedings Volume 8857, Signal and Data Processing of Small Targets 2013; 88570I (2013) https://doi.org/10.1117/12.2022613
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

Abstract

MLPDA (Maximum Likelihood Probabilistic Data Association) has drawn attention as an effective target track extraction algorithm in high false density environments. In this algorithm, the target track is estimated as the maximum likelihood state vector, by using multiple observation frames that include the target signal and many false signals. The track is confirmed whether it is the true target or not, by comparing its likelihood with a given track confirmation threshold. However, when the target signals are lost at several frames, the conventional MLPDA deteriorates the track estimation accuracy due to false signals in frames without the target signal. In this paper, we propose multiple hypothetical frame selection MLPDA, which can extract the target track under the situation where the target signals are lost in several frames. Specifically, a batch of stored frames is first selected for track extraction. If the track is not confirmed, our algorithm offers multiple frame selection hypotheses where some frames are assumed to be the frames without the target signal and the other frames include the target signal. The track is extracted under these hypotheses, respectively, and the most likely hypothesis is accepted. If all hypotheses are rejected, our proposed method generates hypotheses that increase the number of frames without the target signal, and verifies them again. Furthermore, the hypotheses that have likelihoods above a given threshold are retained in order to modify the wrong frame selection later. Simulation results show the validity of our proposed method.

Citation Download Citation

Masanori Mori, Takashi Matsuzaki, Hiroshi Kameda, and Toru Umezawa "Target track extraction in high false density environments using multiple hypothetical frame selection MLPDA", Proc. SPIE 8857, Signal and Data Processing of Small Targets 2013, 88570I (30 September 2013); https://doi.org/10.1117/12.2022613

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KEYWORDS

Detection and tracking algorithms

Target detection

Monte Carlo methods

Computer simulations

Image sensors

Sensors

Signal detection

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