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Based on the assumption that the received signal intensity is inversely proportional to a polynomial function of the distance from the target, a maximum likelihood (ML) target location estimator that uses multi-frame binary data is proposed for wireless sensor networks (WSNs). Simulation results show that this estimator is much more accurate than a heuristic weighted average method, and it can reach the Cramér-Rao lower bound (CRLB) even with relatively small amount of data. As a natural extension, the estimator that uses multi-frame multi-bit discrete data is developed, and its corresponding CRLB is derived. Its theoretical performance bound (CRLB) is compared with the CRLB of the estimator using analog data, which is also derived in this paper. Under a communication constraint, guidelines on an optimal tradeoff between number of bits and number of frames are provided for various situations.