In this paper, two approaches for obstacle detection and position estimation are presented. One is an algorithm based on M out of N detection logic and the other is an algorithm based on the probabilistic Interacting Multiple Model (IMM) method. The M out of N threshold-based algorithm declares that there is an obstacle present if it gets M validated measurements out of N consecutive measurements. IMM algorithm runs two different models in parallel, each based on a different hypothesis. One model assumes that there is an obstacle present while the other model assumes that there is no obstacle present in the sensor field of view. The performances of the two algorithms are compared based on their false alarm rate and detection speed. At first, Monte Carlo simulations are performed using only the false measurements to determine the thresholds for each method that generate a similar number of false detections. Using these thresholds, the detection speed of each method is compared and it is shown that the IMM-based algorithm is superior to the M out of N logic-based algorithm.

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