
$$C={\left[\begin{array}{l l}{0.9}&{0.1}\\ {0.1}&{0.9}\end{array}\right]}$$
$$0.9\quad0.1$$

kT )( d Here we assume there are two closely spaced targets: Cargo and Fighter. Due to circumstances, attribute measurements received are predominately from one or another and both targets generates actually one single (unresolved kinematics) track. To simulate such scenario, a true Target Type sequence over 100 scans was generated according figure 1 below. The sequence starts with the observation of a Cargo type and then the observation of the target type switches two times onto Fighter type during different time duration. At each time step k the decision is randomly generated according to the corresponding row of the confusion matrix of the classifier given the true target type (known in simulations). Then the algorithm presented in the previous section is applied. 

The simulation consists of 10000 Monte-Carlo runs. The computed averaged performances are shown on the figures 2 and 3 on the base of estimated belief masses obtained by the trackers based on Demspter's rule, DSmT based PCR5 rule, and TCN fusion rule with the following t-conorm and tnorm functions: *(1) TCN with t-corm=max and t-norm=bounded product; (2)TCN with t-corm=max* and t-norm=min; (3) TCN with t-corm=sum and t-norm=min. It is evident that the target tracker based on Dempster's rule ([10]) is unable to track properly and quickly the changes of target type. Such a very long latency delay is due to the too long integration time necessary to the Demspter's rule for recovering the true belief estimation. PCR5 rule can quickly detect the type changes ([10]) and properly re-estimates the belief masses contrariwise to Dempster's rule. Our goal here is to evaluate the performance of TCN fusion rule with the analyzed above combinations between applied t-conorm and t-norm functions. In general it is obvious that TCN fusion rule shows a stable, quite proper and effective behavior, following the performance of PCR5 rule. TCN fusion rule with *t-corm=max* and *tnorm=bounded product* reacts and adopts better than TCN with t*-corm=sum* and *t-norm=min*, 
followed by TCN with t-corm=max *and t-norm=min*. As a whole, TCN rule is more cautious in the process of re-estimating the belief masses in comparison to PCR5. It provides symmetric type estimation contrariwise to Dempster's. 

![0_image_0.png](0_image_0.png)

