Quantization/compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth.We consider the problem of target tracking in a WSN with quantized measurements in this paper.Attention is focused on the design of measurement quantizer with adaptive thresholds.Based on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy,an adaptive design method for quantization thresholds is proposed.Due to the nonlinear measuring and quantization models,particle filtering (PF) is adopted in the fusion center (FC) to estimate the target state.Posterior Cram’er-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also derived.Finally,a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach. Quantization / compression is usually adopted in wireless sensor networks (WSNs) since each sensor node typically has very limited power supply and communication bandwidth. We consider the problem of target tracking in a WSN with quantized measurements in this paper. Attention is focused on the design of measurement quantizer with adaptive thresholds. Based on the probability density function (PDF) of the signal amplitude measured at a random location and by maximizing the entropy, an adaptive design method for quantization thresholds is proposed. Due to the nonlinear measuring and quantization models, particle filtering (PF) is adopted in the fusion center (FC) to estimate the target state. Preterior Cram’er-Rao lower bounds (CRLBs) for tracking accuracy using quantized measurements are also derived .Finally, a simulation example on tracking single target with noisy circular trajectories is provided to illustrate the effectiveness of the proposed approach.