Robust perception algorithm for indoor and outdoor scenes based on signal of opportunity

Considering that the development of navigation application service requirements for the combination of indoor and outdoor scenes and the problems of low recognition stability and the low recognition accuracy of existing indoor and outdoor scenes perception methods, this paper proposes a robust sensing algorithm for indoor and outdoor scenes based on signal of opportunity. The signal of opportunity is used to reduce the single signal recognition error. In order to improve the classification accuracy of the traditional AdaBoost algorithm for imbalanced data sets, the Probabilistic Neural Network(PNN) is used as the training weak classifier, and the entropy weight method is introduced to modify the weight of the weak classifier generated by iteration to improve the classification accuracy of the strong classifier. Experimental verification in real scenarios shows that the algorithm in this paper performs best in indoor and outdoor scene perception using signal of opportunity: WiFi signal, available GNSS stars, and light intensity in indoor and outdoor environments. For scenes switching in different angle directions, the recognition accuracy of 98％ can be achieved with a false alarm rate of only 1.7％, which verifies the accuracy and robustness of the algorithm proposed in this paper.