Target Trajectory Tracking Research Articles

Second-Order Fault Tolerant Extended Kalman Filter for Discrete Time Nonlinear Systems

As missing sensor data may severely degrade the overall system performance and stability, reliable state estimation is of great importance in modern data-intensive control, computing, and power systems applications. Aiming at providing a more robust and resilient state estimation technique, this paper presents a novel second-order fault-tolerant extended Kalman filter estimation framework for discrete-time stochastic nonlinear systems under sensor failures, bounded observer-gain perturbation, extraneous noise, and external disturbances condition. The failure mechanism of multiple sensors is assumed to be independent of each other with various malfunction rates. The proposed approach is a locally unbiased, minimum estimation error covariance based nonlinear observer designed for dynamic state estimation under these conditions. It has been successfully applied to a benchmark target-trajectory tracking application. Computer simulation studies have demonstrated that the proposed second-order fault-tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter. Experimental results have demonstrated that the proposed second-order fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing nonlinear estimation approaches.

Thermal Multisensor Fusion for Collaborative Robotics

Collaborative robotic configurations for monitoring and tracking human targets have attracted interest in the fourth industrial revolution. The fusion of different types of sensors embedded in collaborative robotic systems achieves high-quality information and contributes to significantly improve robotic perception. However, current methods have not deeply explored the capabilities of thermal multisensory configurations in human-oriented tasks. In this paper, we propose thermal multisensor fusion (TMF) for collaborative robots to overcome the limitations of stand-alone robots. Thermal vision helps to utilize the heat signature of the human body for human-oriented tracking. An omnidirectional (O-D) infrared (IR) sensor provides a wide field of view (FOV) to detect human targets, and Stereo IR helps determine the distance of the human target in the oriented direction. The fusion of O-D IR and Stereo IR also creates a multisensor stereo for an additional determination of the distance to the target. The fusion of thermal and O-D sensors brings their limited prediction accuracy with their advantages. The maximum a posteriori method is used to predict the distance of the target with high accuracy by using the distance results of TMF stereo from multiple platforms according to the reliability of the sensors rather than its usage of visible-band-based tracking methods. The proposed method tracks the distance calculation of each sensor instead of target trajectory tracking as in visible-band methods. We proved that TMF increases the perception of robots by offering a wide FOV and provides precise target localization for collaborative robots.

Measurement of spindle radial error based on target trajectory tracking

High-speed spindle is the core device of dicing saws, grinders and other equipment, which are widely used in semiconductor manufacturing industry. The rotational performance of spindle is critical to the overall accuracy of a complex mechanical system. A new method based on TTT (Target Trajectory Tracking, TTT) is proposed to measure the rotational characteristic of a spindle used in the tools. In the proposed method, a commercial CCD camera and a designed pinhole were applied to measure the rotation track of spindle. The exposure time of CCD is governed by the shutter, and the shutter time is smaller than 1 ms. Correspondingly, the high-speed spindle rotation cycle is approximately millisecond. We set a light-emitting point (the target) on the rotating end of the spindle and record the movement track of this point with the CCD. The experimental results confirmed that the device can be used to measure the spindle radial error with a maximal speed of 6000 r/min.

Monocular Vision-based Sense and Avoid of UAV Using Nonlinear Model Predictive Control

SummaryThe potential use of onboard vision sensors (e.g., cameras) has long been recognized for the Sense and Avoid (SAA) of unmanned aerial vehicles (UAVs), especially for micro UAVs with limited payload capacity. However, vision-based SAA for UAVs is extremely challenging because vision sensors usually have limitations on accurate distance information measuring. In this paper, we propose a monocular vision-based UAV SAA approach. Within the approach, the host UAV can accurately and efficiently avoid a noncooperative intruder only through angle measurements and perform maneuvers for optimal tradeoff among target motion estimation, intruder avoidance, and trajectory tracking. We realize this feature by explicitly integrating a target tracking filter into a nonlinear model predictive controller. The effectiveness of the proposed approach is verified through extensive simulations.

Omnidirectional Nonprehensile Manipulation Using Only One Actuator

This paper presents a novel nonprehensile manipulation method that uses the vibration of a plate, where the two degrees of freedom of a part on the plate are controlled by only one actuator. First, a manipulator whose end effector is a flat plate is introduced. By employing an underactuated joint mechanism, the shape and orientation of the vibrational orbit of the plate vary according to frequency and offset angle of the sinusoidal displacement input to an actuator. Then, simulation analyses reveal that the manipulator can omnidirectionally induce translational velocity to the part on the plate. There exists an orthogonality between the effects of the frequency and offset angle on the velocity map of the part. Based on this characteristic, a visual feedback control for manipulating the part is designed. Finally, the proposed method is validated via experiments using a prototype manipulator. A target-trajectory tracking task and a four-way part-feeding task are demonstrated.

Target Enclosing and Trajectory Tracking for a Mobile Robot With Input Disturbances

This letter investigates two control problems of a unicycle-type mobile robot: 1) target enclosing and 2) trajectory tracking. The existence of disturbance in velocities, i.e., the input channels of a mobile robot, is considered in both problems. The input disturbance is assumed to be generated by a linear exogenous system, which can be used to describe linear combinations of a finite number of sinusoidal signals and step signals. Two dynamic control laws are proposed respectively, such that the disturbance rejection is achieved and global asymptotic stability of the closed-loop system is guaranteed. Finally, simulation results of an example verify effectiveness of the proposed control laws.

Target Tracking in 3-D Using Estimation Based Nonlinear Control Laws for UAVs

This paper presents an estimation based backstepping like control law design for an Unmanned Aerial Vehicle (UAV) to track a moving target in 3-D space. A ground-based sensor or an onboard seeker antenna provides range, azimuth angle, and elevation angle measurements to a chaser UAV that implements an extended Kalman filter (EKF) to estimate the full state of the target. A nonlinear controller then utilizes this estimated target state and the chaser’s state to provide speed, flight path, and course/heading angle commands to the chaser UAV. Tracking performance with respect to measurement uncertainty is evaluated for three cases: (1) stationary white noise; (2) stationary colored noise and (3) non-stationary (range correlated) white noise. Furthermore, in an effort to improve tracking performance, the measurement model is made more realistic by taking into consideration range-dependent uncertainties in the measurements, i.e., as the chaser closes in on the target, measurement uncertainties are reduced in the EKF, thus providing the UAV with more accurate control commands. Simulation results for these cases are shown to illustrate target state estimation and trajectory tracking performance.

Sequential detection of target trajectory tracking loss using the decision statistics of pips

A sequential algorithm of detection of the tracking loss of target trajectory based on the cumulative sums method has been obtained using the values of decision statistics of pips obtained during the primary processing of radar data. In this case the probabilistic data combining method is used for the estimation of moving target parameters at small values of the signal-to-noise ratio. The obtained algorithm was analyzed by using the statistical simulation of a model case of the target tracking with the data of surveillance radar measuring the range and radial velocity of target. The description of decision statistics of target and noise pips involved the use of the Rice and exponential distribution laws, respectively.

A Research on Moving Target Trajectory Simulation and Tracking in Wide Area Surveillance Ground Moving Target Indication Mode

由于可以在较短时间内对大范围区域进行动目标监视，广域监视动目标检测(Wide Area Surveillance, WAS-GMTI)模式已经成为机载监视雷达的一种重要工作模式。目前具备WAS-GMTI工作模式的系统较少，实际数据较难获得，严重影响了对WAS-GMTI中相关处理算法的验证。该文提出一种基于电子地图路网信息的WAS-GMTI动目标点迹仿真方法。该方法根据载机和电子地图路网信息直接产生动目标信息，避免了仿真场景回波所需的大量运算，而且产生的动目标信息可以合理地融合到真实地理环境中。此外，联合对同一动目标的多次观测信息，该文还给出了一种动目标的轨迹关联方法。仿真结果验证了该文所提方法的有效性。

First-principles-driven model-based current profile control for the DIII-D tokamak via LQI optimal control

In tokamak fusion plasmas, control of the spatial distribution profile of the toroidal plasma current plays an important role in realizing certain advanced operating scenarios. These scenarios, characterized by improved confinement, magnetohydrodynamic stability, and a high fraction of non-inductively driven plasma current, could enable steady-state reactor operation with high fusion gain. Current profile control experiments at the DIII-D tokamak focus on using a combination of feedforward and feedback control to achieve a targeted current profile during the ramp-up and early flat-top phases of the shot and then to actively maintain this profile during the rest of the discharge. The dynamic evolution of the current profile is nonlinearly coupled with several plasma parameters, motivating the design of model-based control algorithms that can exploit knowledge of the system to achieve desired performance. In this work, we use a first-principles-driven, control-oriented model of the current profile evolution in low confinement mode (L-mode) discharges in DIII-D to design a feedback control law for regulating the profile around a desired trajectory. The model combines the magnetic diffusion equations with empirical correlations for the electron temperature, resistivity, and non-inductive current drive. To improve tracking performance of the system, a nonlinear input transformation is combined with a linear-quadratic-integral (LQI) optimal controller designed to minimize a weighted combination of the tracking error and controller effort. The resulting control law utilizes the total plasma current, total external heating power, and line averaged plasma density as actuators. A simulation study was used to test the controller's performance and ensure correct implementation in the DIII-D plasma control system prior to experimental testing. Experimental results are presented that show the first-principles-driven model-based control scheme's successful rejection of input disturbances and perturbed initial conditions, as well as target trajectory tracking.

Second law of thermo-dynamics

After a shipwreck (ship collision, explosion, disappearance, etc.), people rely on the marine search and rescue wireless sensor networks (MSR-WSNs) as one key weapon to locate and track drowning targets to save lives and assets. However, due to the complex and dynamic ocean environment, marine sensors could be captured by malicious attackers and then become Byzantine sensors. Byzantine sensors may tamper with the actual sensor data at a fixed or time-varying probability and transmit it to the data fusion center (DFC), which renders the normal localization and tracking function of the search and rescue system downgrade or even fail. To address this issue, we develop a New robust marine mobile Multi-Target LocAlization and Tracking scheme called NMTLAT by eliminating abnormal measurement data from the initial measurement data. NMTLAT works in several steps. By analyzing and mining sensors data and behavior, we firstly employ the information entropy of the system composed of a single sensor and their neighbor sensors to develop an efficient dynamic threshold based Byzantine node identification method. After migrating Byzantine sensors, the DFC utilizes sensor-aware and preprocessed marine data of the beacon or honest sensors to complete target localization and trajectory tracking. Specifically, NMTLAT consists of a novel distributed and cooperative multi-target localization and tracking algorithm using both received signal strength indication (RSSI) and priori information of sensors location. NMTLAT employs the importance sampling method to approximate the posterior probability distribution of the sensors and targets location. Furthermore, when the marine target is outside MSR-WSNs coverage, a piecewise function is utilized to characterize the likelihood of finding a drowning target in the search and rescue sea area. Finally, the Lyapunov’s second stability theorem is adopted to measure the stability of the NMTLAT. Our extensive simulation experiments validate that the NMTLAT performance is superior to existing solutions in various marine search and rescue scenarios.