Initially, the targeted data acquisition algorithm is designed to detect human presence within a vehicle cabin using a radar sensor installed on the vehicle’s roof. Therefore, this algorithm aims to collect only relevant data pertaining to the human body volume, resulting in enhanced accuracy and efficiency.

Here’s a simple implementation of the algorithm in Python:

python

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import numpy as np

# Parameters for radar settings

RADAR_FREQUENCY = 76e9  # 76 GHz

SENSING_RANGE = 2.5  # meters

HUMAN_BODY_VOLUME = 0.066  # cubic meters (average human volume)

TARGET_VOLUME_RATIO = 1/3  # targeting 1/3 of the human body volume

# Function to simulate radar data collection

def collect_radar_data(target_volume_ratio, sensing_range):

    # Simulating data collection based on targeted volume

    targeted_volume = HUMAN_BODY_VOLUME * target_volume_ratio  # Targeting 1/3 of human volume

    num_data_points = int(targeted_volume * 1000)  # 1000 data points per cubic meter

    radar_data = np.random.rand(num_data_points, 3) * sensing_range  # Simulating data in 3D space

    return radar_data

# Function to detect human presence

def detect_human_presence(radar_data):

    # Simple threshold-based detection (for demonstration purposes)

    human_threshold = 0.5  # arbitrary threshold for presence detection

    detected_humans = radar_data[radar_data[:, 0] > human_threshold]  # simplistic detection logic

    return len(detected_humans) > 0  # return True if a human is detected

# Main function to run the acquisition and detection

def main():

    # Collect radar data

    radar_data = collect_radar_data(TARGET_VOLUME_RATIO, SENSING_RANGE)

    # Detect human presence

    if detect_human_presence(radar_data):

        print(“Human detected in the vehicle cabin.”)

    else:

        print(“No human detected.”)

if __name__ == “__main__”:

    main()