Using an A-priori learnt motion model with particle filters for tracking a moving person by a linear infrared array network

摘要: An infrared sensor has been primarily used as a proximity sensor, its use being mostly limited because of imprecise measurements attributing to the non-linearity device well dependence on reflectivity surrounding objects. However, one cannot overlook fact that these sensors are quite low-cost, can be easily mounted small robotic systems and computationally very efficient. In this paper, we try an array network detect person in environment also track person. A traditional particle filter algorithm using given motion model poses challenges for tracking sensors, might fail keep up with complex dynamic changes directions coupled presence noisy readings or missed detections from data, errors position estimation could add over time making completely lose instead fixed model, propose learn statistically initial target data subsequently filtering approach order addition, learnt is regularly updated so support establishing more accurate