## 4 Discussion
Now, a very interesting situation will be examined. This particular situation very nicely demonstrates the capabilities of the algorithm, since it includes the following challenges:  
1. MRFT data inaccuracy,
2. Multiple MRFTs within single AIS data gating radius,
3. High SAIS data latency and
4. AIS data absence, that is, only MRFT data  
Firstly, in Figure 6 only MRFTs will be presented. Figure 5: Resolving a “clear situation”—maneuvering target (yellow trace: MRFT data, dark red trace: AIS data). Figure 6: Resolving a complex situation—only MRFT data presented. From Figure 6 it could be noticed that there are two stable MRFTs, labeled with F_41297 and F_41274. Both MRFTs are integrated with their corresponding AIS data designated by 564264000 and 417222324, respectively. Then, another vessel was detected by HFSWRs and Id number F_41314 was assigned to it. MRFT labeled F_41274 will be examined more thoroughly. Based entirely on the HFSWR data it could be concluded that this vessel was conducting nearly a zig-zag manoeuver (Figure 6). It is highly unlikely that this was the case, so available AIS data will be examined as well (Figure 7). From Figure 7 it could be easily seen that there are long and stable trace produced by LAIS data. In this case there is no LAIS data latency, so the data provided by the LAIS feed may be considered highly accurate. On the other hand, SAIS data arrives with significant latency and may be discarded during further processing, since there is a stable LAIS data feed. Although stable, MRFT shows high inaccuracy and may not be considered as accurate representation of the vessel's trajectory. So, the vessel was sailing in a straight line and no maneuvers were conducted by the vessel. This is a clear case of MRFT data inaccuracy, as well as SAIS data latency. Moreover, detection of a new target by HFSWRs in a same AIS data gating radius put the integration algorithm to another test. Precisely, the algorithm needed to resolve which MRFT is the best candidate for the AIS data integration. This is done in the manner described in Section 3 of this paper and the result is presented in the Figures 6 and 7. Finally, when decisions about data integration are made, the question arises about what to do with the MRFTs which cannot be integrated with any available AIS data. As it is described in Section 3, the algorithm will forward this MRFT as it is and final result is shown in Figures 6 and 7. From Figure 8 it can be seen that the LAIS data has significant latency in comparison to the MRFTP, while SAIS data latency is even greater. In this case it is obvious that algorithm needs to make a decision based on a prior knowledge and data extrapolation. From Figure 8 it can be observed that there are two traces, the red one representing MRFT (labelled F_4071) and the yellow one which represents the track formed by the AIS data (MMSI: 240821000). It is clear that integration link between F_4071 and MMSI 240821000 already existed and this represents a prior knowledge in the current timestamp Based on this, AIS data is extrapolated and since there are no other MRFTs in the vicinity of the extrapolated AIS data, the algorithm decided that integration link shall be confirmed with a probability of 95.1%. In this way 3 different data sources about a vessel are merged into a single data feed despite their timestamp difference. The final scenario shows the proposed algorithm responses when multiple AIS data is within a single MRFT gating radius and when there is an absence of MRFT data, that is, only AIS tracking is available. In the situations where there are multiple AIS sources within single a MRFTP gate radius (Figure 9) the algorithm will try to integrate AIS data which has the best matching factor with the MRFT. Although this looks like the right approach, it is not always the case. The situation encircled in red in Figure 9 is actually an oil platform with vessels around it.