The growth of applications relying on the Automatic Identification System (AIS) brings to light the need of verification and trustworthiness of AIS data. AIS data are vulnerable to false or missing declarations requiring the implementation of measures increasing the trustworthiness of AIS messages. One of the possible solution is the comparison between the reported vessel position and the position estimated using for example radiolocalization techniques. The usage of AIS signals for radi-olocalization has been already demonstrated, however the main limitation of this technique is the limited coverage. In order to extend the coverage area, data from different networks should be used. In this case an inter-network time bias has to be considered. This offset is due to the lack of standardization about the specific synchronization technique, hence each AIS receiver manufacturer can implement time extraction using different algorithms. If devices from the same manufacturer are employed, the offset between the receivers is negligible, whereas if dissimilar receivers are used, the offsets could prevent the correct outcome of the classical radiolocalization techniques. In order to fill in this gap, a “Multi-Network (MN) Time Difference Of Arrival (TDOA)” algorithm has been designed. The approach extends the traditional TDOA technique, by working out any synchronization problem. The algorithm has been tested in simulated scenarios. From the results, the effects of the inter-network bias have been investigated and the benefits of the inclusion of heterogeneous stations, either non-synchronized or with different accuracies, have been shown. Moreover, the maximum tolerable anachronism between the receivers included in the network has been evaluated.


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    Title :

    Inter-network bias: Impact on multi-network TDOA


    Contributors:


    Publication date :

    2017-06-01


    Size :

    349318 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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