evidence combination rules (ECRs) [16], probability box [17], and ER rule. The ER rule does not need the prior distribution about patterns or states, as it constitute a likelihood modelling process. Therefore, this research aims to propose an intelligent approach to extract moving vessels from blips on the basis of the ER rule. A proposed approach 3.1 Step 1: The quantifications of inter-frame differences In fact, approximate dynamic information indicated by inter-frame differences is sufficient for manual identification. The problem is that when there are too many vessels, such a manual inspection becomes impractical. Hence, an intelligent approach of simulating the manual work will be helpful for navigational and maritime safety. Experienced operators are able to achieve a high identification accuracy under uncertainties, because they know the regularities of moving vessels after a long term observation. For instance, the speed of a moving vessel in a specific waterway is generally stable. Therefore, a velocity indicated by a blip is a piece of direct evidence for authenticity identification. Without any filtering algorithm, it is possible to estimate the authenticity probability of a blip based on its velocity in adjacent frames. Moreover, operators can take other attributes of a blip into consideration in order to make comprehensive and accurate identification. Therefore, it is required to quantify these attributes. In fact, many attributes of a blip can be taken as evidence for identification, such as velocity, course, size, colour, width, and length. However, there is a condition of using the ER rule that multiple pieces of evidence should be independent from each other. Hence, three types of evidence or attributes which are considered to be independent of each other in terms of their contributions to moving vessel identification are selected, namely, velocity, motion direction (i.e., course), and blip shape. The velocity and motion direction can be easily understood. Real moving vessels are more likely to move with a steady velocity and a steerable course, and noise objects are more likely to drift around a
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Ma, F., Chen, Y. W., Yan, X. P., Chu, X. M. & Wang, J: ‘A novel marine radar targets extraction approach based on sequential images and Bayesian Network.’ Ocean Engineering 2016, 120, pp. Appendices Taking the record “178,12,4,135,549.5-479.5,5,11,33,0.3” as an example, its probabilities of states can be obtained as follows. According to the definition of sentence given by Section 4.2, the attribute values of the corresponding blip can be obtained as, “serial number: 178”, “velocity: 12 pixel in 10 frames”, “direction (course): 135 degree”, “slenderness: 0.3”. Then, based on the velocity, course, and slenderness evidence modelling in Section 4.2, three pieces of evidence can be obtained and shown in Table 6. The weights and reliabilities are set to be 0.95. Table 6 Three pieces of evidence Belief degrees (probabilities) to Reliability Weight Velocity {0.4351, 0.5649, 0} 0.95 0.95 Course (Direction) {0.3654, 0.6346, 0} 0.95 0.95 Slenderness {0.0261, 0.9739, 0} 0.95 0.95 Using Eqs. (7), (8), (9) and (10), such pieces of evidence can be combined, and the result is presented as .