带宽与能量有限的无线传感网络卡尔曼滤波OA

To address the issues of limited bandwidth and battery energy in wireless sensor networks,an event-triggered distributed quantization and dimensionality reduction fused Kalman filtering algorithm is proposed.At the sensor nodes,the algorithm obtains the optimal transmission segments of local estimates via dimensionality reduction and quantization strategies.At the fusion center,a novel compensation scheme is adopted to compensate for the partial components transmitted by the sensors,yielding a recursive distributed fused Kalman filtering algorithm in the sense of linear minimum variance.Simulation results verify that,after 100 iterative steps,the total communication traffic of the proposed distributed Kalman filtering algorithm is reduced by 726 bits compared with the single quantization-based data compression algorithm,and by 528 bits compared with the single dimensionality reduction-based data compression algorithm.By employing the proposed algorithm,communication traffic is reduced,energy consumption and communication burden between sensor nodes and the fusion center are decreased,and the problems of bandwidth and energy constraints are effectively solved.