## 1 Introduction  
The visual MIMO system uses a visible light spectrum with optical spatial modulation (OSM) or color-space-based modulation (CSBM) techniques [1; 2; 3; 4] for parallel communication. The use of a visible light spectrum and optimal design of receiver using a self-powered organic photo-detector (OPD) can enhance the data rate, which makes a gigabit visual MIMO system realizable [5; 6]. This system exhibits less interference (high directionality) than the existing RF MIMO system at a lower cost [7; 8; 9]. It makes the visual MIMO system a preferred choice for vehicle to vehicle (V2V) and vehicle to everything (V2X) communication. For the vehicular network, existing LED headlight/backlight and rear-view/dashboard cameras of the vehicle are used as a transmitter and receiver antenna, respectively [10; 11; 12]. This MIMO system is used to transmit a huge volume of data conveying information about the vehicle dynamics and its location between the vehicles with lesser propagation delay [13; 14]. In a visual MIMO communication, LEA is used to transmit visible light parallel, which is captured using a camera at the receiver section. The use of visible light as a communication medium demands LoS and security. An efficient LEA detection and tracking algorithm are required to maintain the LoS and to achieve reliable communication between the two vehicles. To satisfy this requirement,the Harris corner detection and Kalman filter tracking algorithm have been proposed [15]. The Harris corner detection algorithm is used to detect the four corners of the LEA position, and the Kalman filter is used to correct the estimated corner points. These corner points are used to extract the LEA from the acquired image. Then, the detected LEA is restored by the perspective projection [15], and the individual LED status is estimated to extract the transmitted data. Perturbation factors like ambient light, irregular and blur motion, makes the detection of corner points of LEA challenging. These factors make the Harris corner detection fails to detect or inaccurately detect the minimum required corner points [16]. This makes an imperfect selection of ROI corresponding to the LEA and increases BER. Hence, there is a need for the proper design of LEA and a reliable detection technique to overcome these problems [17; 18; 19]. In this proposed work, the finder and alignment pattern of the QR has been emulated to design LEA. The robust nature of the QR pattern makes the LEA to be detected accurately. However, the dynamic varying nature of this LEA in a vehicular network demands the tracking algorithm. Existing literature used the Kalman filter [20; 21] to track the LEA position. As the Kalman filter uses linear models to predict the LEA position, the randomized/non-linear nature of the vehicular movement makes it fail in tracking the LEA. Further, the tracking performance of the Kalman filter is highly dependent on the LEA initial position. Detection of the LEA initial position is highly challenging due to discontinuous visibility of vehicles in the receivers field of view (FOV). To overcome this problem, the particle filter has been employed in the proposed work. The non-parametric nature of this filter makes it capable to track the randomly moving LEA fitted on the vehicle. Its ability to accommodate any form of noise distribution improves the tracking performance amidst ambient disturbance. A simple algorithm of the particle filter can be easily implemented in any onboard processors available in the receiver vehicle. As the LED transmitters in LEA are closely placed, they are prone to have interference. Light interference in the visual-MIMO communication is addressed with the use of the polarization technique [5], which minimizes the BER even at a high data rate. The frequency domain-based approach has also been reported to minimize interference. It employs colored LEDs in the CIE1931 color space as transmitters enabling the LEA to be differentiated from its background.