Track-before-detect for Infrared Maneuvering Dim Multi-target via MM-PHD

This paper proposes a generalized method for tracking of multiple objects from moving, cooperative vehicles -- bringing together an Unscented Kalman Filter for vehicle localization and extending a Sequential Monte Carlo Probability Hypothesis Density filter with a novel cooperative fusion algorithm for tracking. The latter ensures that the fusion of information from cooperating vehicles is not limited to a fully overlapping Field Of View (FOV), as usually assumed in popular distributed fusion literature, but also allows for a perceptual extension corresponding to the union of the vehicles' FOV. Our method hence allows for an overall extended perception range for all cooperative vehicles involved, while preserving same or improving the accuracy in the overlapping FOV. This method also successfully mitigates noisy sensor measurement and clutter, as well as localization inaccuracies of tracking vehicles using Global Navigation Satellite Systems (GNSS). Finally, we extensively evaluate our method using a high-fidelity simulator for vehicles of varying speed and trajectories.

Parallel robot possesses the characteristics of large rigidity, strong load bearing capacity and small error. Directed against the new-type parallel robot mechanism with step motor drive, the closed-formed solutions were developed for both inverse and direct kinematics, a track planning in its workspace has been carried out, and a model of control system was establish, after that a kind of sliding mode control algorithm was designed, and a simulative experiment was made on the Matlab/Simulink. It is shown that the parallel robot system using the proposed control algorithm does not have the chattering problem. The system has good robustness, and is robust to the uncertainties and disturbance, and has good performance in tracking, and high precision real-time control on this parallel robot is achieved.

This paper presents a new family of fuzzy phase locked loop (FPLL)-fuzzy double decouple synchronous reference frame phase locked loop (FDDSRF-PLL)-that incorporated DDSRF-PLL into FPLL. FDDSRF-PLL enjoys both advantageous of DDSRF-PLL and fuzzy system to detect the fundamental-frequency positive-sequence component of the utility voltage under unbalanced and distorted conditions as well as fast and smooth tracking of phase jump. Furthermore, to achieve the best possible performance, a fuzzy adaptive particle swarm optimization (FAPSO) algorithm is considered to optimize parameters of the fuzzy system and DDSRF-PLL's filters. The results show that, in comparison with DDSRF-PLL and FPLL, FDDSRF-PLL has better performance in tracking of the positive-sequence voltages of the three-phase system under unbalanced condition, phase and jump simultaneously.

In this paper, we present an easy-hardware-implementation multiple model particle filter (MMPF) for maneuvering target tracking. In the proposed filter, the sampling importance resampling (SIR) filter typically used for nonlinear and/or non-Gaussian application is extended to incorporating multiple models that are composed of a constant velocity (CV) model and a "current" statistical (CS) model, and the Independent Metropolis Hasting (IMH) sampler is utilized for the resampling unit in each model. Compared with the bootstrap MMPF, the proposed MMPF requires no knowledge of models and model transition probabilities for different maneuvering motions, and keeps a constant number of particles per model at all times. This allows a regular pipelined hardware structure and can be implemented in hardware easily. Furthermore, using the IMH sampler for the resampling unit avoids the bottleneck introduced by the traditional systematic resampler and reduces the latency of the whole implementation. Simulation results indicate that the proposed filter has approximately equal tracking performance with the bootstrap MMPF. Hardware architecture of the IMH sampler and its corresponding sample unit are presented, and a parallel architecture consisting of CV model processing element (PE), CS model PE and a central unit (CU) is described. The proposed architecture is evaluated on a Xilinx Virtex-II Pro FPGA platform for a maneuvering target tracking application and the results show many advantages of the proposed MMPF over existing approaches in terms of efficiency, lower latency, and easy hardware implementation.

Acoustic source tracking based on adaptive distributed particle filter in distributed microphone networks

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human–robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human–robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism.

Solving the path-tracking problem of vehicles is a key part of realizing intelligent transportation. The four-wheel- independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) has the advantages of good controllability, low speed maneuverability and high speed stability, which is beneficial to improving the path tracking performance. In this paper, a novel 4WIS electric vehicle is proposed and the path tracking control research was carried out on it by using the adaptive fuzzy PID algorithm. Firstly, the four-wheel steering strategy was analyzed, and the geometric relationship between the front and rear steering angle when the vehicle's centroid angle is zero was derived. Furthermore, based on the principle of adaptive fuzzy PID control and the single-step heading prediction algorithm, a path-tracking controller was designed for the self-built 4WIS EV. A Simulink/Carsim co-simulation platform was built, on which the simulation was carried out at different condition, and the simulation results show that the designed adaptive fuzzy PID controller has good path-tracking performance and strong robustness. Finally, the serpentine trajectory and the double lane change (DLC) trajectory are taken as the reference trajectory, and the reliability and robustness of the designed adaptive fuzzy PID control algorithm were verified on the open road. The results show that the adaptive fuzzy PID control algorithm has good tracking performance and is far superior to pure PID control.

This paper proposes a MIMO robust servo controller design method for a three wheeled Omnidirectional Automated Guided Vehicles (OAGVs) with a disturbance to track desired references using a polynomial differential operator. The process for designing the proposed controller can be described as follows: Firstly, modeling of the MIMO three-wheeled OAGV are presented. Secondly, a new extended system is obtained by applying the polynomial differential operator to the state space model and the output velocity error vector. Thirdly, the proposed controller for the given plant is designed by using the pole assignment method. By applying an inverse polynomial differential operator, a servo compensator is obtained. Finally, in order to verify the effectiveness of the proposed controller, the numerical simulation results are shown. The simulation results show that the proposed controller has good tracking performance under a step type of disturbance and the complicated higher order reference signals such as ramp and parabola. These simulation results are compared with those of the adaptive controller proposed by Bui, T. L. in 2013. The proposed controller shows the better tracking performance than the adaptive controller.

In this article, the novel adaptive neural networks (NNs) tracking control scheme is presented for nonlinear partial differential equation (PDE)-ordinary differential equation (ODE) coupled systems subject to deception attacks. Because of the special infinite-dimensional characteristics of PDE subsystem and the strong coupling of PDE-ODE systems, it is more difficult to achieve the tracking control for coupled systems than single ODE system under the circumstance of deception attacks, which result in the states and outputs of both PDE and ODE subsystems unavailable by injecting false information into sensors and actuators. For efficient design of the controllers to realize the tracking performance, a new coordinate transformation is developed under the backstepping method, and the PDE subsystem is transformed into a new form. In addition, the effect of the unknown control gains and the uncertain nonlinearities caused by attacks are alleviated by introducing the Nussbaum technology and NNs. The proposed tracking control scheme can guarantee that all signals in the coupled systems are bounded and the good tracking performance can be achieved, despite both sensors and actuators of the studied systems suffering from attacks. Finally, a simulation example is given to verify the effectiveness of the proposed control method.

In this paper, a robust controller for a three degree of freedom helicopter control is proposed in presence of modeling error and disturbance. In this frame, interval type-2 fuzzy logic control approach (IT2FLC) and sliding mode control (SMC) techniques are used to design a controller named interval type-2 fuzzy sliding mode controller (IT2FSMC). Elevation, pitch and travel angles are considered measurable, whereas unmeasured states are estimated by using a nonlinear observer. The proposed control scheme can not only attenuate the chattering effect of the SMC, but also it reduces the rules number of the fuzzy controller. As for stability, it is exponentially guaranteed by using the Lyapunov criteria. The simulation results show that the IT2FSMC significantly alleviates the chattering effect and provides a good tracking performance, in the presence of modeling error and disturbance. They also illustrate that the IT2FSMC achieves the best tracking performance in comparison with the type-1 fuzzy logic controller, the IT2FLC and the type-1 fuzzy sliding mode controller.

Probability hypothesis density (PHD) filter has been demonstrated a promising algorithm for tracking an unknown number of targets in real time. However, this method can only be used in the multi‐target tracking systems with known measurement noise variances; otherwise, the tracking performance will decline greatly. To solve this problem, an adaptive PHD filter algorithm is proposed based on the variational Bayesian approximation technique to recursively estimate the joint PHDs of the multi‐target states and the time‐varying measurement noise variances. First, the variational calculus method is employed to derive the multi‐target estimate recursions, and then the Gaussian and the inverse Gamma distributions are introduced to approximate the joint posterior PHD, and achieve a closed‐form solution. Simulation results show that the proposed algorithm can effectively estimate the unknown measurement noise variances and has a good performance of multi‐target tracking with a strong robustness.

Track of weak targets with unknown and time varying number is an important and difficult issue. In the paper, an improved multitarget track-before-detect (TBD) method for image measurements is proposed to tackle this problem. The method is developed in the framework of probability hypothesis density (PHD) filtering by utilizing the independent measurements from multiple homogeneous sensors. To fast implement the PHD based method using particle filtering technique, an efficient implementation approach is also presented by partitioning the particles into multiple subsets. Simulation results show the improved performance of the proposed method for multitarget TBD at low signal-to-noise ratio (SNR) condition.

Dual-linear-motor-driven gantry systems have been widely used in high-speed or heavy payload precision motion systems. Due to the unique physical properties such as mechanical coupling, the precise synchronization control of such kinds of systems is crucial to achieve good tracking and smooth operation performance. However, the existing synchronization schemes usually focus on the pure motion synchronization only and, thus, have certain inherent performance limitations. In this paper, an accurate multi-input multi-output mathematical model of a dual-driven gantry is presented first, where the complete planar motions of the crossbeam include the traditional linear motion along the guide rails and the previously ignored rotational motion around the mass center. With the better understanding of the mechanical coupling and the internal forces caused by the rotational dynamics, an advanced synchronization control scheme by directly taking into account the additive rotational dynamics is presented, which not only synchronizes the motions of two parallel motors but also regulates the internal forces. In the proposed scheme, the adaptive robust control algorithm is also applied to obtain a guaranteed robust performance in the presence of both parametric uncertainties and uncertain nonlinearities. Comparative experiments with previous control schemes are carried out to show the better synchronization performance and verify the effectiveness of the proposed method.

A number of vision-based algorithms designed to detect and track unmanned aerial vehicles (UAVs) from on board a second UAV have been researched, implemented, and experimentally validated over the last decade. However, the successful methods have tended to rely on characteristics such as color or shape, meaning they require the target UAV to have particular markings or geometries. This paper uses the Viola–Jones cascade classifier, a computer vision algorithm originally designed to detect human faces in video streams, and demonstrates its capability for detecting and tracking an arbitrary type of UAV with excellent performance in either indoor or outdoor environments and with a variety of backgrounds. The Viola–Jones algorithm is applied to two specific quadrotor UAV models, the Solo from 3D Robotics and the AR.Drone 2.0 from Parrot. Experimental testing demonstrates that the resulting system achieves very good detection and tracking performance in real time on each UAV type for both indoor and outdoor flight tests.

Human body detection and tracking is an important research field of computer vision in monitoring applications of the healthcare, surveillance and etc. The performance of detection and tracking is closely associated with each other. In this paper, a novel approach for detection and tracking a moving body in field of sensor fusion with respect to CCD, thermal image sensor is proposed. We calibrated CCD and thermal image by using the chess board which can display thermal and texture data for heterogeneous sensor fusion. The Segmented Blob Tree of body was developed to track the moving bodies. Experiments showed that the method yields good detection and tracking performance in dynamic environments, such as a lighting change, various electric heating appliances or complicated backgrounds.