**Author details**

14 Will-be-set-by-IN-TECH

Under the use of this covariance matrix *Pk*|*k*−<sup>1</sup> and the innovation covariance *Sk*, the gain *Kk*

*Kk* <sup>=</sup> *Pk*|*k*−1*S*−<sup>1</sup>

Both, matrix *Qk* and gain *Kk* are used as additional features in the classification process. The separation of the feature space is depicted in Figure 10. These histograms show that a vehicle (Figure 10(c), 10(d)) has lower process noise *Qv* and thus results in a smaller gain *K* compared to a pedestrian (Figure 10(a), 10(b)). This is due to a mostly linear trajectory of a vehicle with one main reflection point. A pedestrian echo fluctuates, which is reflected in a greater process

(a) *Qv*, Pedestrian (b) *K*, Pedestrian (c) *Qv*, Vehicle (d) *K*, Vehicle

The additional features *Q* and *K* are available for a created and active track. For each detected object a track is created, which is however not activated until several measurements can be associated with the track. A track must be confirmed by subsequent measurements, otherwise the track remains semi-active. In any case, additional features are available for classification.

> *x*<sup>17</sup> *Qv* Velocity component of the process noise matrix Q *x*<sup>18</sup> *Kv* Veleocity component of the Kalman gain matrix K

An additional feature extraction based on the process noise and Kalman gain has been described. These features are added to the prior feature set. Table 7 shows the results of the classifier using all proposed features, single measurement, multiple measurements and tracker feedback. Even in an urban area with a high density of static targets pedestrians could be detected, tracked and classified with a true positive rate of 61.22% in the test data set. These results outperform prior outcomes using single- and the multiple measurements as a feature

The classification results show an increasing performance in terms of correct classification and

**Table 6.** Additional Feature Set extracted from the tracker using an adaptive process noise.

**Figure 10.** Feature Histogram of Process Noise *Qv* and Gain *K*.

Feature Annotation Description

*<sup>k</sup>* (13)

can be calculated.

*3.3.2. Classification*

misclassification of pedestrians.

basis.

noise and thus larger gain *K*.

Hermann Rohling and Steffen Heuel *Department of Telecommunications, Hamburg University of Technology, Hamburg, Germany*
