# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 4 Real Experimental Results and Discussion  
The measured data are entirely sourced from the data collection of a one-way, three-lane section of a road in Xi'an (the scene dataset is the same as in Reference [2]). The baseband signal processor is the ZYNQ-7020, produced by Xilinx, San Jose, CA, USA. The millimeter-wave radar RF chip is the CAL60S244 from Caltech, Pasadena, CA, USA. The hardware architecture of the millimeter-wave radar system is shown in Figure 4. The system is primarily composed of the RF board and the baseband board. The RF board includes the RF chip and the array transceiver antenna module, while the baseband board contains the power module, signal processing module, and storage module. The storage module is used to temporarily store data received by the radar or intermediate processing results, helping to avoid processing bottlenecks caused by data flow rates that exceed processing capacity. Figure 3: Filter tracking error plot. **(a)** RMSE of position; **(b)** RMSE of velocity. Before the radar installation, angle calibration and testing are required to ensure the accuracy of the millimeter-wave radar (MMW) measurements. An angle controller, lane markings, and corner reflectors are used for calibration. The standard measurement error of the radar is \(\pm\)0.03 m/s. As shown in Figure 5a, the traffic millimeter-wave radar detection system uses angular calibration to ensure the radar antenna is parallel to the lane direction. After the radar antenna calibration is completed, the radar equipment is installed on an overpass beside the road as a fixed observation point. Figure 5b illustrates the actual deployment of the radar system. Data collection is conducted through a host computer that is connected to the radar system in real time. The host computer receives, processes, and stores the measurement data, such as vehicle speed and position, collected by the millimeter-wave radar. Figure 4: Radar hardware system architecture. Figure 5: Traffic MMW radar detection system implementation. (**a**) Calibration of traffic MMW radar; (**b**) measurement data acquisition. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Radar System Parameters** & **Parameter Value** & **Unit** \\ \hline Operating Frequency Range & 60–61 & GHz \\ Number of TX/RX Antenna Array & 4T/4R & - \\ Signal Bandwidth & 200 & MHz \\ Signal Pulse Period & 30 & us \\ Tuning Frequency Rate & 6.7 & MHz/ μs \\ Distance Resolution & 0.75 & m \\ Speed Resolution & 0.1 & m/s \\ Angle Resolution & 0.1 & o \\ Data Rate & 20 & Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Radar system parameters and performance indicators. Figure 6a shows the relationship between the traffic millimeter-wave radar, the camera, and the target. During the data collection process, synchronized data acquisition was performed using both the motion camera and the millimeter-wave radar. The camera and radar were installed on two separate mounts and placed close to each other to ensure they covered the same observation area. Figure 6b illustrates a complex traffic scenario with heavy traffic flow and closely matched vehicle speeds. During the experiment, to evaluate the effectiveness of the MAST-UKF algorithm, data from two vehicles were compared: Vehicle A changes lanes during its movement, while Vehicle B simultaneously continues to drive straight. Vehicle A starts being tracked at the 27 th second, with continuous tracking from frame 632 to frame 918. Vehicle B starts being tracked at the 24 th second, with the tracking interval from frame 573 to frame 929. Figure 6c,d show the images of the two target vehicles. Figure 6: Schematic of the experimental scenario and objectives: (**a**) geometric relationship diagram between the traffic MMW radar and the target object; (**b**) traffic scenario during a complex traffic peak period; (**c**) image of the tracked target vehicle at frame 798; and (**d**) image of the tracked target vehicle at frame 916. The tracking results of the MAST-UKF algorithm are shown in Figure 7a,b.