Automotive radars have been proven to be the most reliable technology for target detection and estimation of range, Doppler and angle, in almost every condition. Through the investigation of key performance issues on different applications of indoor monitoring, we found that most of the existing solutions are based on a single sensor for data acquisition. It is well-known that single aspect angle limits feature extraction performance and can even prevent detection. Besides, providing sufficient angular resolution is still a challenge, especially when considering dynamic multi-target applications. The aforementioned scenario and limitations of the current technology motivate us to design a distributed, collaborative and connected MIMO (Multiple-Input Multiple-Output) radar system for indoor applications. Besides the already expected improvement of MIMO configuration, multi-sensor data acquisition and dedicated processing can significantly improve the existing technologies. From a more technical perspective, in a multi-sensor scenario, the additional information available generates new degrees of freedom allowing the exploitation of RCS diversity. That may improve the detector performance by increasing the Probability of Detection and even making possible the detection of slow-moving and low SCNR targets. Additionally, more accuracy is also expected. In other words, the goal of this project is to investigate different architectures of distributed radar for indoor monitoring and how to efficiently combine and processing the available data to improve detection and parameter estimation. The possible results of this study have a wider reach with different applications (including outdoor and even non-automotive) spanning multiple domains.