Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE 5.1’s constant tone extension feature with mmWave radar measurements to provide real-time 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few metres. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags in real-time with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further evaluate a variant of BmmW, named BmmW-LITE, that is specifically designed for single-antenna BLE devices (i.e., that avoids the need of bulky and costly multi-antenna arrays). Our results show that BmmW-LITE achieves a mean 3D localization accuracy of 36 cm, thus enabling accurate tracking of objects in indoor environments despite the use of inexpensive single-antenna BLE devices