Humanpresence detection is essential for a various number of applications including defense and healthcare. Accurate measurements of distances, relative velocities of humans, and other objects can be made with radars. They are largely impervious to external factors like the impact of smoke, dust, or rain. They are also capable of working in varied intensity of light in indoor environments. This report explores the analyzing of real data captured and the application of different detection algorithms. Adaptive thresholding suppresses stationary backgrounds while maintaining detection thresholds to keep false alarm rates low. Using different approaches of Constant False Alarm Rate (CFAR) namely Cell averaging, Smallest of Cell averaging,Greatest of Cell Averaging and Order Statistic, this report aims to show its performance in detecting humans in an indoor environment using real time data collected. The objective of this project is to explain the signal processing chain of presence detection using a small scale RADAR