Chaff material is a classic passive jamming, widely applying in radar countermeasure operations to generate a large radar cross section (RCS) for protecting aircraft from air defense systems. Electromagnetic scattering measurements is the most direct and accurate method to obtain RCS of chaff, study its diffusion effects, and evaluate its jamming performance. In outdoor environment, an unmanned aerial vehicle (UAV) equipped with a chaff dispensing system is used to release chaff at high altitudes, simulating the actual environment of chaff. Simultaneously, ground-based electromagnetic scattering measurement systems begin tests. This method offers advantages such as rapid deployment and low measurement costs, allowing chaff jamming performance evaluation to be conducted in relatively small areas. However, the wide-beam antenna used in near-field measurement adds clutter. Therefore, to improve measurement accuracy and obtain high-confidence results, issues such as clutter suppression and the identification of targets and clutter need to be urgently addressed. This paper focuses on the high-precision outdoor measurement based on UAV carried chaff dispensing systems. First, a sliding-window zero-Doppler clutter filter technique is applied to suppress stationary environmental clutter. Then, to address the classification and identification of targets and clutter in measured data processing, a region identification technique using statistical distribution features is proposed. This technique identifies the type of echoes in different regions and completes RCS data processing, thereby enhancing data processing accuracy and supporting research on the electromagnetic scattering characteristics of chaff jamming.