Adaptive beaconing for collision avoidance and tracking accuracy in vehicular networks

In vehicular networks,the exchange of beacons among neighboring vehicles is a promising solution to guarantee a vehicle's safety.However,frequent beaconing under high vehicle density conditions will cause beacon collisions,which are harmful to a vehicle's driving safety and the location tracking accuracy.We propose an ABIwRC(Adaptive Beaconing Interval with Resource Coordination)method for a highway scenario.Each vehicle broadcasts beacon interval requests,including the intervals needed for both the vehicle's driving safety and location tracking accuracy.The RSU(Road Side Unit)allocates resources for a vehicle's beaconing according to the requests from all vehicles and the interference relationship between the vehicles in adjacent RSUs.We formulate a resource allocation problem for maximizing the sum utility,which measures the satisfaction of vehicles'requests.We then transform the optimization problem into a maximum weighted independent set problem,and propose an algorithm to solve this effciently.Simulation results show that the proposed method outperforms the benchmark in terms of beacon reception ratio,vehicle driving safety,and location tracking accuracy.

Energy efficient target tracking algorithm using cooperative sensors

Target tracking is one of the applications of wireless sensor networks（WSNs）.It is assumed that each sensor has a limited range for detecting the presence of the object,and the network is sufficiently dense so that the sensors can cover the area of interest.Due to the limited battery resources of sensors,there is a tradeoff between the energy consumption and tracking accuracy.To solve this problem,this paper proposes an energy efficient tracking algorithm.Based on the cooperation of dispatchers,sensors in the area are scheduled to switch their working mode to track the target.Since energy consumed in active mode is higher than that in monitoring or sleeping mode,for each sampling interval,a minimum set of sensors is woken up based on the select mechanism.Meanwhile,other sensors keep in sleeping mode.Performance analysis and simulation results show that the proposed algorithm provides a better performance than other existing approaches.

High-accuracy target tracking for multistatic passive radar based on a deep feedforward neural network

In radar systems,target tracking errors are mainly from motion models and nonlinear measurements.When we evaluate a tracking algorithm,its tracking accuracy is the main criterion.To improve the tracking accuracy,in this paper we formulate the tracking problem into a regression model from measurements to target states.A tracking algorithm based on a modified deep feedforward neural network(MDFNN)is then proposed.In MDFNN,a filter layer is introduced to describe the temporal sequence relationship of the input measurement sequence,and the optimal measurement sequence size is analyzed.Simulations and field experimental data of the passive radar show that the accuracy of the proposed algorithm is better than those of extended Kalman filter(EKF),unscented Kalman filter(UKF),and recurrent neural network(RNN)based tracking methods under the considered scenarios.

Adaptive Backstepping Slide Mode Control of Pneumatic Position Servo System

With the price decreasing of the pneumatic proportional valve and the high performance micro controller, the simple structure and high tracking performance pneumatic servo system demonstrates more application potential in many fields. However, most existing control methods with high tracking performance need to know the model information and to use pressure sensor. This limits the application of the pneumatic servo system. An adaptive backstepping slide mode control method is proposed for pneumatic position servo system. The proposed method designs adaptive slide mode controller using backstepping design technique. The controller parameter adaptive law is derived from Lyapunov analysis to guarantee the stability of the system. A theorem is testified to show that the state of closed-loop system is uniformly bounded, and the closed-loop system is stable. The advantages of the proposed method include that system dynamic model parameters are not required for the controller design, uncertain parameters bounds are not need, and the bulk and expensive pressure sensor is not needed as well. Experimental performance, as compared with some existing methods. results show that the designed controller can achieve better tracking

Development of underwater electric manipulator based on interventional autonomous underwater vehicle (AUV)

In applications such as marine rescue,marine science,archaeology,and offshore industries,autonomous underwater vehicles(AUVs)are frequently used for survey missions and monitoring tasks,with most operations being performed by manned submersibles or remotely operated vehicles(ROVs)equipped with robotic arms,as they can be operated remotely for days without problems.However,they require expensive marine vessels and specialist pilots to operate them.Scientists exploring oceans are no longer satisfied with the use of manned submersibles and ROVs.There is a growing desire for seabed exploration to be performed using smarter,more flexible,and automated equipment.By improving the field operation and intervention capability of AUVs,large-scale and long-range seafloor exploration and sampling can be performed without the support of a mother ship,making it a more effective,economical,convenient,and rapid means of seafloor exploration and sampling operations,and playing a critical role in marine resource exploration.In this study,we explored the integration technology of underwater electric robotic arms and AUVs and designed a new set of electric manipulators suitable for water depths greater than 500 m.The reliability of the key components was analyzed by finite element analysis and,based on the theory of robot kinematics and dynamics,simulations were performed to verify the reliability of the key components.Experiments were conducted on land and underwater,trajectory tracking experiments were completed,and the experimental data in air and water were compared and analyzed.Finally,the objectives for further research on the autonomous control of the manipulator underwater were proposed.

Weighted adaptive filtering algorithm for carrier tracking of deep space signal

Carrier tracking is laid great emphasis and is the difficulty of signal processing in deep space communication system.For the autonomous radio receiving system in deep space, the tracking of the received signal is automatic when the signal to noise ratio（SNR） is unknown.If the frequency-locked loop（FLL） or the phase-locked loop（PLL） with fixed loop bandwidth, or Kalman filter with fixed noise variance is adopted, the accretion of estimation error and filter divergence may be caused.Therefore, the Kalman filter algorithm with adaptive capability is adopted to suppress filter divergence.Through analyzing the inadequacies of Sage–Husa adaptive filtering algorithm, this paper introduces a weighted adaptive filtering algorithm for autonomous radio.The introduced algorithm may resolve the defect of Sage–Husa adaptive filtering algorithm that the noise covariance matrix is negative definite in filtering process.In addition, the upper diagonal（UD） factorization and innovation adaptive control are used to reduce model estimation errors,suppress filter divergence and improve filtering accuracy.The simulation results indicate that compared with the Sage–Husa adaptive filtering algorithm, this algorithm has better capability to adapt to the loop, convergence performance and tracking accuracy, which contributes to the effective and accurate carrier tracking in low SNR environment, showing a better application prospect.