A computer-implemented method, comprising: - obtaining a radar measurement dataset indicative of depth positions of data points of a scene observed by a radar unit, the scene comprising a target, the target being selected from the group consisting of a hand, a part of a hand, and a handheld object, and - processing the radar measurement dataset using at least one neural network algorithm (500, 500-1, 500-2, 500-3) to obtain an output dataset, the output dataset comprising one or more position estimates of the target defined with respect to a predefined reference coordinate system associated with the scene, wherein the radar measurement dataset comprises a time series of frames, each frame of the time series of frames being indicative of the depth positions of the respective data points of the scene at a respective point in time, wherein the at least one neural network algorithm (500, 500-1, 500-2, 500-3) comprises multiple neural network algorithms included in multiple cells (581, 582, 583) of a super-network with recurrent structure, the method being characterised in that the output dataset comprises a time series of multiple position estimates of the target defined with respect to the predefined reference coordinate system, the time series of the multiple position estimates being associated with the time series of frames, wherein the super-network augments the output dataset with inferred velocity estimates of the target. - obtaining a radar measurement dataset indicative of depth positions of data points of a scene observed by a radar unit, the scene comprising a target, the target being selected from the group consisting of a hand, a part of a hand, and a handheld object, and - processing the radar measurement dataset using at least one neural network algorithm (500, 500-1, 500-2, 500-3) to obtain an output dataset, the output dataset comprising one or more position estimates of the target defined with respect to a predefined reference coordinate system associated with the scene, wherein the radar measurement dataset comprises a time series of frames, each frame of the time series of frames being indicative of the depth positions of the respective data points of the scene at a respective point in time, wherein the at least one neural network algorithm (500, 500-1, 500-2, 500-3) comprises multiple neural network algorithms included in multiple cells (581, 582, 583) of a super-network with recurrent structure, the method being characterised in that the output dataset comprises a time series of multiple position estimates of the target defined with respect to the predefined reference coordinate system, the time series of the multiple position estimates being associated with the time series of frames, wherein the super-network augments the output dataset with inferred velocity estimates of the target. wherein the radar measurement dataset comprises a time series of frames, each frame of the time series of frames being indicative of the depth positions of the respective data points of the scene at a respective point in time, wherein the at least one neural network algorithm (500, 500-1, 500-2, 500-3) comprises multiple neural network algorithms included in multiple cells (581, 582, 583) of a super-network with recurrent structure, the method being characterised in that the output dataset comprises a time series of multiple position estimates of the target defined with respect to the predefined reference coordinate system, the time series of the multiple position estimates being associated with the time series of frames, wherein the super-network augments the output dataset with inferred velocity estimates of the target.