Abstract: Techniques are provided for sparse array RF imaging for surveillance applications. Objects in a three dimensional (3-D) image-data-set obtained from multi-static radio frequency detection data are classified, for example, as human or non-human. One or more geometric image features are extracted from the image-data-set that support a target classification process; and the one or more objects are classified as a threat based on a parametric evaluation of the extracted geometric image features.

Abstract: Techniques are provided for sparse array RF imaging for surveillance applications. The present invention enables object image identification using a sparsely populated array of distributed radio nodes deployed and operated as a radar detection, tracking and identification application. Three-dimensional object images are formed, from which estimates of extent and shape can be extracted for objects that are detected in the vicinity of the deployed radar array. In addition, techniques are provided for the identification and/or classification of vehicles, humans and fauna through feature extraction from the formed three-dimensional images.

Abstract: Techniques are provided for sparse array RF imaging for surveillance applications. The present invention enables object image identification using a sparsely populated array of distributed radio nodes deployed and operated as a radar detection, tracking and identification application. Three-dimensional object images are formed, from which estimates of extent and shape can be extracted for objects that are detected in the vicinity of the deployed radar array. In addition, techniques are provided for the identification and/or classification of vehicles, humans and fauna through feature extraction from the formed three-dimensional images.