*5.1. SCM-Based JTC Method*

In this simulation, the SCM-based JTC method will be evaluated under the scenario of single-target measurement without clutter. The process noise of the target motion state is characterized by *q* = 0.5 m/s2. The three ship targets have the same motion model, and the initial state is *x*<sup>0</sup> = [1.2 km 1.5 km − 7.5 m/s 5.0 m/s] T. The sampling interval is *t* = 1s and the total duration is 40 s. At a certain time, only one ship target is present, and the JTC performance of different target is analyzed separately.

The trajectory tracking (for a single run) and target classification (averaged by 20 Monte Carlo runs) results are shown in Figures 3–5. As can be seen from the figures, the proposed method can not only accurately estimate the state of the target but also correctly classify the target simultaneously. It is also seen that the classification probability curve, which matches with the true target class, increases rapidly, and can approach one (100%) within 30 s under all the tested conditions. Conversely, the classification probability curves mismatching the true target class decrease gradually and reach 0 after about 10–30 estimate cycles, meaning that a high confidence classification result is obtained. However, it does not mean that we have to wait for a period of 10–30 estimate cycles to obtain a reliable decision on target class, since the classification probability matching the true target class is obviously higher than others within only a few cycles.

**Figure 3.** Result of JTC when Target Class A is present: (**a**) estimated target trajectory; (**b**) estimated target class probability.

**Figure 4.** Result of JTC when Target Class B is present: (**a**) estimated target trajectory; (**b**) estimated target class probability.

**Figure 5.** Result of JTC when Target Class C is present: (**a**) estimated target trajectory; (**b**) estimated target class probability.

As a comparison, the simulation also considers the target classification result directly obtained from the HRRP correlation method, where the HRRP templates (training data) are generated from the CAD model with electromagnetic simulation tool, and the test data are predicted from the 3D-SCM. In the simulation, for each target, 360 HRRP training samples (which cover 0–360◦ in azimuth angle space) are generated with 1◦ interval. Accordingly, 1440 test samples are generated from each 3D-SCM at the same viewing angle with azimuth angle interval 0.2◦. The classification results are shown in

Table 1. The corresponding probabilities of correct classification (PCC) for Target Classes A, B and C are 0.8986, 0.8993 and 0.8417, respectively. The over-all PCC (OA-PCC) for all the test samples is 0.8799. The metric index PCC and OA-PCC are defined as

$$\text{PCC}(m) = N\_{\text{correct}}(m) / N\_{\text{total}}(m), m = 1, 2, 3. \tag{93}$$

$$PA - P\mathbb{C}\mathbb{C} = \sum\_{m=1}^{3} N\_{\text{correct}}(m) / \sum\_{m=1}^{3} N\_{\text{total}}(m) \tag{94}$$

where *Ntotal*(*m*) denotes the total test samples for the *m*th target class, *Ncorrect*(*m*) represents the correctly classified test samples for the *m*th target class.


**Table 1.** Classification results of the ship targets.

Compared the classification results in Table 1 (no tracking process involved) with those shown in Figures 3–5 (with JTC processing), it is seen that the SCM-based JTC method achieves a performance improvement of more than 0.1 (10%) in PCC after the tracking filter is stable, indicating the advantage of the proposed method in classification accuracy.
