To solve the problem of offset failure of flexible single-link manipulator actuator due to actuator aging and wear,an adaptive boundary fault-tolerant control combining hysteresis quantizer and event trigger mechanism is proposed.Firstly,a hysteresis quantizer with unknown parameters is used to quantize the control input signal.Secondly,a static event trigger mechanism is constructed to reduce the consumption of input signal resources.Furthermore,the adaptive boundary fault-tolerant control law and parameter updation law are designed based on the improved Lyapunov direct method.Finally,the simulation results show that the proposed control algorithm is feasible.With communication constraints and disturbances,the proposed control method can achieve better posture tracking effect compared with traditional adaptive methods,while achieving system stability with lower computational load.
Radar direction finding is an essential cooperative monitoring method for aircraft,and the directional parameters of the direction finding antenna determine the accuracy of direction finding.A modified amplitude comparison direction finding model is proposed based on the traditional principle of adjacent amplitude comparison direction finding and the error of amplitude comparison direction finding.Based on the correction of amplitude comparison direction finding model,an error correction genetic simulated annealing(COR-GSA)algorithm is proposed to identify unknown directional antenna directionality parameters by determining the antenna directionality parameters that need to be identified.Identification experiments are conducted using a self-designed dual channel direction finding receiver,and 1 000 sets of aircraft real azimuth data are used for verification.The results show that using the COR-GSA algorithm to identify antenna directional parameters has the highest direction finding accuracy.Finally,the identified antenna directionality parameters are used to track the aircraft's azimuth,and the tracking error is reduced by 18.3％compared to the tracking error without azimuth correction.
With the expansion of user scale in LEO satellite networks, unbalanced regional load and bursty network traffic lead to the problem of load disequilibrium. A distributed hops-based back-pressure (DHBP) routing is proposed. DHBP theoretically derives a fast solution for the minimum end-to-end propagation hops between satellite nodes in inclined-orbit LEO satellite networks; hence, link weights are determined based on remaining hops between the next hop and destination satellites. In order to control the number of available retransmission paths, the permitted propagation region is restricted to a rectangular region consisting of source-destination nodes to reduce the propagation cost. Finally, DHBP is designed distributedly, to realize a dynamic selection of the shortest link with low congestion and balanced traffic distribution without obtaining the whole network topology. Network simulation results demonstrate that DHBP has higher throughput and lower delay under high load conditions compared with state-of-the-art routing protocols.
Compact high-frequency surface wave radar suffers from a high false alarm rate in target detection due to its low transmit power and wide beam, thus a large number of false plots are produced, which increases the computational burden of subsequent target tracking algorithm and easily leads to producing false tracks. In this paper, a two-stage false plot identification method is proposed. Firstly, a multi-frame plot clustering algorithm is proposed to cluster the potential plots of the same target in several consecutive frames, the plots outside the clusters are removed as false plots. Then, the differences in terms of range and Doppler velocity between the plot in the center frame and those in its neighbor frames in each cluster are used as features. Finally, a trained extreme learning machine is applied to the obtained features to recognize the remaining false plots. Experimental results with both simulated and field data demonstrate the effectiveness of the proposed method for false plot identification.
Sparse arrays are flexible and can reduce inter-array coupling while increasing the array aperture, but traditional DOA estimation based on sparse arrays can lead to angular ambiguity and confusion, which brings problems of poor estimation accuracy and insufficient robustness. In this paper, we propose a robust matrix filling algorithm with weighted truncated singular value projection (WT-SVP) for DOA estimation of sparse arrays, in which weights are assigned according to the size of singular values during the filling iteration to highlight the array information contained in large singular values and reduce the unnecessary weights in small singular values. The traditional singular value projection algorithm is optimized by reducing unnecessary noise information in small singular values. The algorithm can achieve hole information recovery of sparse arrays and make full use of discontinuous array elements, while the WT-SVP filling algorithm achieves high accuracy and high resolution of sparse array DOA estimation and high robustness at low signal-to-noise ratio and low snapshot.
