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Lower bounds on localisation errors serve as a performance indicator of how close a localisation system is to providing optimal performance. In this study, localisation of mobile terminals for urban areas is performed using received signal strength (RSS)-based techniques with maximum likelihood (ML) and linear kernel (LK) estimators. Simulations are performed with and without buildings in an urban area cell to illustrate the effect of discontinuities in the RSS profiles, on radio location accuracy. Results show that a localisation error is higher when buildings are absent as compared to the scenario when buildings are present. Buildings add extra features to the RSS measurement space which, if known to the localisation system, improve radio location accuracy. A comparison is made between the root mean-square error of the ML and LK estimators with the Cramer-Rao bound (CRB), the Bayesian Cramer-Rao bound (BCRB) and the Weiss-Weinstein bound (WWB). These comparisons show that the previously used CRB and BCRB do not provide realistic lowers bounds in the presence of buildings. In such cases bounds, such as WWB, which are capable of handling RSS discontinuities provide more realistic lower bounds on the accuracy of radio location.