Localization, a process of determining the position of a blind node, can be used in various applications. Signal-strength localization provides a low-cost and lowpower solution to positioning. Signal-strength positioning approaches using fingerprinting or calibrated approaches require a time-consuming calibration phase. Existing self-calibrating approaches, which do not require a priori calibration, use a least-squares fitting model to determine both the position of the blind node as well as the optimal environmental parameters. In this paper, we propose an approach using the Product-Moment correlation between the measured signal strength and the estimated signal strengths. Such approach does not require estimation of the environmental parameters or prior calibration and outperforms existing self-calibrating least-squares approaches. We compare our approach to existing least-squares calibration-free positioning approaches. Moreover, we look at the Cramer-Rao Bound (CRB) of signal-strength localization and using simulations we show that the product-moment correlation outperforms least-squares approaches and follows the CRB closely. Simulation and evaluation using a real-world experiment dataset show the product-moment approach significantly outperforms least-squares approaches. The product-moment approach follows the CRB much more closely and achieves up to twice more accurate positions in certain scenarios. When the error ratio increases and the number of reference positions stays fixed at 6, the product-moment approach scores 20% more accurate positions. In the cooperative localization scenario, the product-moment correlation performs 40% better.