This work describes a heterogeneous sensor platform for elderly people useful in Ambient Assisted Living context for sleep disorder evaluation. The platform integrates hardware (ambient and wearable sensors), as well as software components (data simulation tool, reasoning). Three sensors with different sensing principles are considered: a Time-Of-Flight camera, a MEMS wearable wireless accelerometer and an Ultra-Wideband radar. The inputs of the platform are the postural information, even simulated, in common to all involved sensors (ie, Standing, Bending, Sitting, Lying down). Since they are extensively used both for analysis of Activities of Daily Living and human behaviour understanding. A posture simulator, calibrated on real experiments performed by each sensor involved in the platform, has been implemented in order to compensate the lack of wide datasets containing long-term data. Moreover, the platform integrates a reasoning layer for automatic sleep disorder evaluation by using an unsupervised learning technique. The effectiveness of the platform was demonstrated by preliminary results, exhibiting high accuracy in sleep disorder evaluation using the three aforementioned sensors.