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 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 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 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 . <EOS>