In order to extend fuel cell lifespan and improve fuel economy of electrical hybrid vehicle with fuel cell/battery/ultracapacitor (FCHEV), a frequency decoupling-based energy management strategy (EMS) for FCHEV using fuzzy control method is proposed. In detail, firstly, according to different characteristics of energy sources, required power of FCHEV is decomposed into three frequency ranges based on Harr wavelet transform and an adaptive-fuzzy filter. Secondly, based on the proposed frequency decoupling, the obtained three frequency required power is supplied by fuel cell-battery and ultracapacitor, respectively, which can guarantee power performance of vehicle and reduce pressure and power fluctuation on fuel cell and battery. Thirdly, for improving fuel economy, one fuzzy controller is proposed to split the power between fuel cell and battery. Finally, the proposed strategy in this paper is verified by advisor-simulink and experimental bench. Simulation and experimental results show that the proposed EMS can effectively reduce impact of power fluctuations on fuel cell, extend its lifespan and reduce fuel consumption on 7.94% compared to equivalent consumption minimization strategy.

The key to successful positioning of autonomous mobile robots in complicated indoor environments lies in the strong anti-interference of the positioning system and accurate measurements from sensors. Inertial navigation systems (INS) are widely used for indoor mobile robots because they are not susceptible to external interferences and work properly, but the positioning errors may be accumulated over time. Thus ultra wideband (UWB) is usually adopted to compensate the accumulated errors due to its high ranging precision. Unfortunately, UWB is easily affected by the multipath effects and non-line-of-sight (NLOS) factor in complex indoor environments, which may degrade the positioning performance. To solve above problems, this paper proposes an effective system framework of INS/UWB integrated positioning for autonomous indoor mobile robots, in which our modeling approach is simple to implement and a Sage–Husa fuzzy adaptive filter (SHFAF) is proposed. Due to the favorable property (i.e., self-adaptive adjustment) of SHFAF, the difficult problem of time-varying noise in complex indoor environments is considered and solved explicitly. Moreover, outliers can be detected and corrected by the proposed sliding window estimation with fading coefficients. This facilitates the positioning performance improvement for indoor mobile robots. The benefits of what we propose are illustrated by not only simulations but more importantly experimental results.

Extended object tracking, an important and emerging research field, has attracted attention for applications in civilian and military fields. However, the modeling and tracking of maneuvering extended objects still face challenges that need to be solved explicitly. Specifically, it is usually difficult to describe the irregular or complex extension dynamics (e.g., the object extension changes in shape and orientation over time) of a maneuvering extended object with random hypersurface. Further, it is tightly coupled to the centroid kinematics during the object maneuvers. Hence, this paper proposes a general approach for modeling a maneuvering extended object with random hypersurface. In this approach, both the object centroid and the extension dynamics for describing the turn maneuvers with different turn rates can be characterized accurately, in a concise linear form. This facilitates the design of an effective tracking algorithm to deal with different turn maneuvers of a maneuvering extended object with random hypersurface. The merits of this proposed model are illustrated by simulation results, where the kinematic state and object extension are estimated jointly and effectively.

Visual object tracking for unmanned aerial vehicles (UAVs) is extensively used in civil and military applications such as intelligent transportation, disaster rescue, and military reconnaissance. However, when a UAV encounters haze weather, its tracking performance deteriorates greatly because of problems such as blurring and quality degradation of the collected images. Most trackers are designed to operate in good weather conditions, this greatly limits the robustness of UAV tracking in haze weather. To solve this problem, this paper proposes an adaptive dehazing Siamese network, which consists of an adaptive dehazing module, a dynamic template update branch, and feature extraction and bounding box prediction subnetworks. The dehazing module yields haze-free images and preserves the edge information of the object, so that the tracker can extract the object features with better characterization. Subsequently, to mitigate the risk of tracking drift caused by the changing appearance of the object, the dynamic template update branch dynamically selects more reliable template images during tracking. In addition, to solve the problem of insufficient UAV object tracking testing datasets in haze scenes, a haze testing dataset for UAV video object tracking is obtained through artificial synthesis and data collection in real-world haze scenes. Furthermore, experiments are conducte on haze testing datasets, namely UAV123-AH, UAV20L-AH, UAVDT-AH, DTB70-AH, and UAVhaze; the results verified that the proposed tracker yields more competitive performance and more robustness in haze scenes compared with the existing algorithms.

Maneuvering extended target tracking, an important but challenging research field, has attracted increasing attention in the field of radar signal processing. Variable structure multiple-model (VSMM) estimation is the current mainstream tracking algorithm, it possesses high tracking performance but depends largely on the model set used. However, the existing model set design methods still cannot offer higher tracking performance due to the complex maneuverability of the target. The current best model augmentation (BMA) algorithm is an efficient and universal model set design method, but it cannot adapt well to complex maneuvering situations because of its fixed basic and candidate model set. Hence, this paper proposes a modified BMA algorithm for VSMM based on adaptive grid, it can make full use of the previous information to realize an adaptation and time variation of the model set. Overall, two different scenarios were considered, wherein the digital simulation shows that the proposed method can provide a more reliable estimation of the kinematic state and shape of the extended target.