The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the different aspects of ML have been analyzed. In the general ML domain, poisoning and adversarial attacks and the related mitigation techniques are an active area of research. Such attacks aim to hamper the ML classification process by poisoning the data set. Mitigation techniques are designed to counter this threat using different approaches. Poisoning attacks in LOS/NLOS classification have not received significant attention by the wireless communication community and this paper aims to address this gap by proposing the application of a specific mitigation technique based on outlier detection algorithms. The rationale is that poisoned samples can be identified as outliers from legitimate samples. In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model.