In this paper, we derive the classical Cramér-Rao Lower Bound (CRLB) for RSS-based direction-of-arrival (DOA). The drawbacks of the classical CRLB and its influence on the optimal network topology are discussed. The CRLB indicates that the minimum variance unbiased estimator (MVUE) does not exist for the problem of RSS-based DOA due to the nature of its measurement function. Hence, beyond the CRLB, we derive performance metrics for the maximum likelihood estimator (MLE) and compare position estimation errors for the MVUE and the MLE for different network topologies. Since both approaches, the CRLB and the maximum likelihood (ML) limits, are not capable of handling ambiguities, we introduce another measure for the variance of a measurement and its corresponding position estimate based on information theory. This way, the amount of information for a set of RSS measurements can be quantified exactly, even in the case of ambiguous probability densities. Thus, the proposed technique gives a holistic view on the information obtained from sensor measurements which can be utilized for network topology optimizatio}, author = {Nowak, Thorsten and Hartmann, Markus and Thielecke, Jörn}, doi = {10.1186/s13634-018-0570-8}, faupublication = {yes}, journal = {Eurasip Journal on Advances in Signal Processing}, keywords = {Information theory, Estimation theory, Cramér-Rao lower bound (CRLB), Localization, Wireless sensor network (WSN), Network localization, Location sensors}, pages = {1-17}, peerreviewed = {Yes}, title = {{Unified} performance measures in network localization}, volume = {48}, year = {2018} }