A novel hand-motion crane control system was developed that improves performance by providing: 1) an intuitive control interface and 2) an element that reduces the complex oscillatory behavior of the payload. Hand-motion control allows operators to drive a crane by simply moving a hand-held radio-frequency tag through the desired path. Real-time location sensors track the movements of the tag and its position is used in a feedbackloop to drive the crane. However, tag measurements are corrupted by noise. It is important to understand the noise properties so that appropriate filters can be designed to mitigate the effects of noise and improve tracking accuracy. This paper quantifies the measurement noise for two possible situations during hand-motion control: when the tag is 1) located in open space inside the sensor coverage zone, and 2) is placed near large machinery. This paper also presents a Kalman filter that adapts to the noise characteristics of the workspace to minimize the tagtracking error.