## 3 Results and Discussion
### Simulation Studies of QR-Code Tracking  
For simulation studies, the LEA of the transmitter section is developed using a QR inspired LEA. The version 5 QR code is taken as a framework, and 17 LEDs are placed inside the alignment and finder pattern. The colour of the LED status (ON/OFF) and background are chosen appropriately to eliminate mishaps. Minimal clearances between LEDs are also maintained to avoid interference, which minimizes the bit error rate. A background image of a vehicle is chosen, and the proposed LEA pattern is embedded, which can simulate images acquired by the rear vehicle in real-time as shown in Figure 12. About 1000 frames capable of transmitting 17,000 bits are used for evaluating the performance. The simulation parameters used for the performance analysis of the proposed MIMO system are given in Table 3.  
At the receiver section, the finder (\(x_{fn},y_{fn}\)) and alignment pattern (\(x_{a},y_{a}\)) are detected using the QR code detection algorithm. The detected finder and alignment patterns are used to recover translation and rotational distortions, respectively. The distance between the finder patterns are estimated and using the geometric property (square shape) of the QR code, the center of the LEA (\(x_{c},y_{c}\)) is evaluated as in Figure 13. The relative scale between the original and detected QR pattern are estimated. As these LEDs are placed in a pre-defined position, the relative scale is used to estimate the position of LEDs with respect to any finder pattern. The area of the LED is also determined using this relative scale. Next, the region corresponding to the LEDs are selected from the grayscale image. A threshold level is applied to the selected region of the LED to extract its status (ON/OFF). If the average intensity of the region is greater than the threshold then the LED status is ON and vice versa. Finally, the LEDs status is converted to the data stream, and the BER is evaluated. The tracking performance of the proposed QR code with the particle filter is evaluated over translation, rotation, and scaling distortions, as shown in Figure 14. The horizontal translation of the QR code predominately occurs during the relative positional change of the leader and trailing vehicle as in Figure 14a. The QR code is rotated along its center in both clockwise and counterclockwise directions to evaluate the detection performance as in Figure 14b. Image scaling occurs as a function of the distance between the leading and trailing vehicle, as in Figure 14c. On all these scenarios, BER is considered as a performance metric and compared with the use of the QR code detection algorithm (without particle filter) alone. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameters** & **Values** \\ \hline Image size & 1080 \(\times\) 1920 \\ QR code version & 5 \\ Size of QR-LEA & 450 \(\times\) 450 \\ Background intensity of QR code(RGB) & 127,127,127 \\ Total No. of LED & 17 \\ LED Shape & Square \\ LED size & 54 \(\times\) 54 pixels \\ Distance between LEDs & 25 pixels \\ Total number of Frame used & 1000 \\ Total number of bits transmitted & 17,000 \\ LED illumination intensity (RGB) & ON 237,27,36 \\ OFF & 0,0,0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters. Figure 14: _Cont._  
Figure 12: Sample Image Acquired from the Camera. Figure 13: Coordinates of the finder patterns and alignment pattern. Sixty frames are considered and the center point \((x_{c},y_{c})\) of LEA was tracked with and without the particle filter, and its tracking performance was analyzed as in Figure 15. A horizontal translation at a rate of one pixel/frame is used along with random distortions (five-pixel variance) on both planes to evaluate the tracking performance. The obtained results show that with the introduction of the particle filter; the tracking performance has been improved. Further, the BER performance is studied under a range of geometric noise during translation motion for 1000 frames (No of Bits Transmitted: 17,000).