Abstract
Wireless sensor networks (WSNs) fusion systems are usually faced with the communication bandwidth constraints, so it is necessary to adopt quantization. The existing quantization is usually under the assumption of known distribution function of data. However, obtaining the accurate distribution function of data is impossible. Therefore, in this paper, based on the obtained measurements, a kind of non-uniform quantization strategy under the principle of least square sum of quantization error is proposed firstly. Then, the solving method of non-uniform quantizing points based on adaptive quantization interval is presented. Next, we further provide a recursive updating method of quantizing points which include semi real-time update and real-time update. Finally, on the basis of the new method, we establish the Kalman Filter based on the new idea of Non-uniform Quantization and give some analysis of performance, which includes the theoretical part and experiment simulation.
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Notes
- 1.
This work was supported by the National Natural Science Foundation of China under grant numbers 61371064, 61304258, 61304186 and 61271144.
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Wen, C., Zhu, C., Xu, D., Quan, L. (2017). A Non-uniform Quantization Filter Based on Adaptive Quantization Interval in WSNs. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_58
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DOI: https://doi.org/10.1007/978-981-10-5230-9_58
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