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Summary: This article deals with state estimation of complex nonlinear discrete fractional‐order systems with unknown noise statistics by means of an adaptive fractional‐order Unscented Kalman filter (AFUKF). Firstly, in order to alleviate the communication burden of fractional‐order Unscented Kalman filter, short‐term memory effect is utilized to decide an appropriate memory length. Then aiming at the problem of filtering divergence and accuracy degradation caused by unknown statistical characteristics of noise, based on the maximum a posterior (MAP) principle, a noise statistical estimator is introduced to estimate and correct the statistical characteristics of noise in real‐time. Finally, the unbiasedness of the proposed algorithm is analyzed to verify that the estimated mean and covariance of noise are unbiased. The effectiveness and accuracy of AFUKF are demonstrated via simulation experiments. [ABSTRACT FROM AUTHOR]

Using the sensor nodes to achieve target tracking is a challenging problem in resource-limited wireless sensor networks. The tracking nodes are usually required to consume much energy to improve the tracking performance. In this paper, an energy-efficient node scheduling method is proposed to minimize energy consumption while ensuring the tracking accuracy. Firstly, the Kalman-consensus filter is constructed to improve the tracking accuracy and predict the target position. Based on the predicted position, an adaptive node scheduling mechanism is utilized to adjust the sample interval and the number of active nodes dynamically. Rather than using traditional search algorithm, the scheduling problem is decomposed to decouple the sample interval and number of nodes. And the node index is mapped into real domain to get closed-form solution to decide the active nodes. Thus, the NP-complete nature is avoided in the proposed method. The proposed scheduling method can keep the tracking accuracy while minimizing energy consumption. Simulation results validate its effective performance for target tracking in wireless sensor networks. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries have been employed extensively in many important applications in the electronics industry. For safety and reliability, it is extremely critical to get an accurate and early-stage remaining useful life prognostic of lithium-ion batteries. However, battery lifetime predictions are challenging due to the nonlinear battery degradation and the operational diversity among batteries. To increase the prediction accuracy, this paper proposes a hybrid framework combining the model-based method and data-driven method. In this framework, after estimating the battery capacity using online operating data, battery lifetime is predicted by the model-based empirical model as well as the data-driven support vector regression model. For the empirical model, its adaptability is improved by updating the parameters dynamically with particle filters. For the support vector regression model, its performance is optimized by an artificial bee colony algorithm. Finally, a fusion method with cascaded structure is proposed to integrate predictions from these two models, which boosts the prediction accuracy by iteratively exerting two concatenated Kalman filters. The generality and effectiveness of the proposed method are verified on battery data sets provided by NASA and our testing bench, respectively. The experimental results illustrate that the proposed method can improve the prediction accuracy of battery remaining lifetime, especially at the early stage. RMSE and MAE of the proposed hybrid framework are within 4 and 3.5. Compared with two existed hybrid methods, RMSE of prediction can be reduced by at least 7.6%. A reduction of not less than 5.9% in MAE of prediction is achieved. • A hybrid prediction method is proposed to predict RUL at the early stage of batteries. • The proposed method integrates the empirical model and data-driven model through cascaded Kalman filters. • Empirical model is updated by particle filters online to adapt to battery dynamically. • Data-driven model is optimized by artificial bee colony algorithm. [ABSTRACT FROM AUTHOR]