This paper presents a joint sparsity aided joint manifold learning (JSAJML) approach for heterogenous sensor fusion of image and radio frequency (RF) data. A typical scenario includes several objects (with RF emitters), which are observed by a network of platforms with Medium Wavelength Infrared (MWIR) cameras and/or RF Doppler sensors. In the past, we developed two heterogeneous approaches for mixed and/or mixed sensor modalities. The joint sparsity (JS) approach for cases without ground truth and joint manifold learning (JML) for cases with ground truth. In this paper, we unify the abovementioned methods. We propose an interactive learning/training of JML based on joint sparsity results. We use the joint sparsity support recovery to generate estimated ground truth and train the JML framework. On the other hand, the joint sparsity can use the JML results to improve the data fusion results and speed up the processing as well. The JSAJML approach is tested and verified on AFRL datasets.


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    Title :

    Joint Sparsity Aided Joint Manifold Learning for Sensor Fusion


    Contributors:
    Shen, Dan (author) / Lu, Jingyang (author) / Zulch, Peter (author) / Disasio, Marcello (author) / Chen, Genshe (author) / Wang, Zhonghai (author) / Niu, Ruixin (author)


    Publication date :

    2020-03-01


    Size :

    1701799 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Joint Manifold Learning Based Distributed Sensor Fusion of Image and Radio-Frequency Data

    Shen, Dan / Lu, Jingyang / Zulch, Peter et al. | IEEE | 2019


    Joint sparsity based heterogeneous data-level fusion for multi-target discovery

    Niu, Ruixin / Zulch, Peter / Distasio, Marcello et al. | IEEE | 2018


    Manifold joint seal

    SCHARPF ROBERT N / EVATT S CLAY | European Patent Office | 2015

    Free access

    MANIFOLD JOINT SEAL

    SCHARPF ROBERT N / EVATT S CLAY | European Patent Office | 2017

    Free access

    Manifold joint seal

    SCHARPF ROBERT N / EVATT S CLAY | European Patent Office | 2015

    Free access