Emerging applications require the location information of clients to enable human-environment interactions or personalized services. With an increasing number of antennas equipped in today's wireless devices, recent research has shown possibility of sub-meter level localization based only on the angle of arrival (AoA) of WiFi sig- nals. While most existing work provides promising median accu- racy, their tail performance however is usually far worse. We ob- serve from measurements that the root cause is due to unequal AoA estimation reliability. In some critical areas, a small variation in the channel state information of signals could introduce an extremely large AoA estimation error. With this observation, we propose UAT (Unequal Angle Tracking), a confidence-aware AoA-based localiza- tion system. We show that unequal reliability of AoA measures can be mathematically quantified, allowing a system to weigh the de- cisions of different APs according to their confidence. Our testbed evaluation shows that UAT's confidence-aware design provides reli- able decimeter level localization for around 90% of locations. UAT is especially effective for risky areas and can reduce their localiza- tion errors by 27.5%, as compared to reliability-oblivious designs.