To meet the real time, automation, anti-concept drift and other processing requirements of pico-satellite high dimensionality telemetry data, a new cluster-based telemetry data anomaly detection method is proposed.The method contains correlative subspace search and two-phase density-based clustering processing for telemetry data.Subspace search calculates the entropy and entropy gain value to achieve low-dimensional subspace partitions of all telemetry data, to reduce the computational complexity and to avoid the occurrence of "dimension disasters".Two-phase telemetry data clustering algorithm meets the requirements of rapid anomaly detection and complex anomaly detection.In the online phase, the grid indexes are used to find single anomalies in real time, in the off-line phase, their specific features of the selected mining data are clustered to find collective anomalies.It also supports the anti-concept drift by iterative updating of normal data features and adaptive modification of the algorithm.The analysis for telemetry data of ZDPS-1A shows that in the on-line phase 10 kHz flowing data can be processed in real time and 95% of single-point anomalies can be found to meet the general needs of real-time telemetry data anomaly detection of Pico Satellite.This method adapts itself to change satellite conditions.The single cluster stays stable when the satellite rotates faster.This approach also detects an anomaly caused by attitude determination and control system's breakdown one month earlier than the current limit-checking system.The proposed algorithm solves the anomaly detection problem of telemetry data with high dimension and concept drift.It is suitable for the ground monitoring system of pico-satellite constellations.