Remote sensing radar satellites cover wide areas and provide spatially dense measurements, with millions of scatterers. Knowledge of the precise position of each radar scatterer is essential to identify the corresponding object and interpret the estimated deformation. The absolute position accuracy of synthetic aperture radar (SAR) scatterers in a 2D radar coordinate system, after compensating for atmosphere and tidal effects, is in the order of centimeters for TerraSAR-X (TSX) spotlight images. However, the absolute positioning in 3D and its quality description are not well known. Here, we exploit time-series interferometric SAR to enhance the positioning capability in three dimensions. The 3D positioning precision is parameterized by a variance–covariance matrix and visualized as an error ellipsoid centered at the estimated position. The intersection of the error ellipsoid with objects in the field is exploited to link radar scatterers to real-world objects. We demonstrate the estimation of scatterer position and its quality using 20 months of TSX stripmap acquisitions over Delft, the Netherlands. Using trihedral corner reflectors (CR) for validation, the accuracy of absolute positioning in 2D is about 7 cm. In 3D, an absolute accuracy of up to \(\sim \)66 cm is realized, with a cigar-shaped error ellipsoid having centimeter precision in azimuth and range dimensions, and elongated in cross-range dimension with a precision in the order of meters (the ratio of the ellipsoid axis lengths is 1/3/213, respectively). The CR absolute 3D position, along with the associated error ellipsoid, is found to be accurate and agree with the ground truth position at a \(99\,\%\) confidence level. For other non-CR coherent scatterers, the error ellipsoid concept is validated using 3D building models. In both cases, the error ellipsoid not only serves as a quality descriptor, but can also help to associate radar scatterers to real-world objects.

During emergency response to mass casualty disasters, one of the main logistic impediments faced by the on-site organization chief is to track the patients at the disaster site. We had proposed a new system based on a location aware wireless sensor network (WSN) to overcome these impediments and assist the responders in providing efficient emergency response. In this paper we have implemented a new ranging algorithm called ranging using environment and mobility adaptive RSSI (REMA) filter which will provide the distance estimates and yield the real time localization of patients at the disaster site. We have conducted ranging experiments both in indoor and outdoor environments and have built an offline database which is given as input for the REMA filter simulation. The output of REMA filter is given as input to the new position estimation algorithm currently being developed by us and the position estimation error is calculated using simulation. The REMA filter simulation results show the suitability of the algorithm for ranging during tracking of patients at the disaster site.