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This work provides a convergence analysis for the estimate error covariance of Kalman filtering based on quantized measurement innovations (QIKF). By taking the quantization errors as random perturbations in observation system, an equivalent state-observation system is given. Accordingly, the quantitative Kalman filter for the original system is equivalent to a Kalman-like filtering for the equivalent...
We consider the problem of target tracking in a wireless sensor network (WSN) that consists of randomly distributed range-only sensors. Quantized measurements are usually adopted in such a network to attack the problem of limited power supply and communication bandwidth. Assuming that local sensor noises are mutually independent, we derive the posterior Cramer-Rao lower bound (CRLB) on the mean squared...
The problem of state estimation with quantized measurements is considered. Due to the nonlinearity of the quantizer, estimating the system state is a nonlinear and non-Gaussian estimation problem even if the system is linear and Gaussian. A novel algorithm for approximate minimum mean square error (MMSE) state estimation with quantized measurements is proposed. The algorithm is based on the information...
The quantized measurement fusion problem for target tracking in wireless sensor networks (WSNs) is investigated. Due to the limited energy and bandwidth, each activated node quantizes and then transmits the local measurements by probabilistic quantization strategy. The fusion center (FC) estimates the target state in a dimension compression way instead of merging all the quantized messages to a vector...
Maneuvering target tracking in wireless sensor network (WSN) with quantized measurements is investigated. The measurement in each local sensor is quantized by uniform quantization scheme and then transmitted to a fusion center (FC). To estimate the state of the target in the FC, the quantized messages are first fused in a weighted average way. Then interactive multiple-model (IMM) scheme using sigma-point...
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