MULTITARGET STATE AND TRACK ESTIMATION FOR THE PROBABILITY HYPOTHESES DENSITY FILTER

The particle Probability Hypotheses Density (particle-PHD) filter is a tractable approach for Random Finite Set (RFS) Bayes estimation, but the particle-PHD filter can not directly derive the target track. Most existing approaches combine the data association step to solve this problem. This paper proposes an algorithm which does not need the association step. Our basic ideal is based on the clustering algorithm of Finite Mixture Models (FMM). The intensity distribution is first derived by the particle-PHD filter, and then the clustering algorithm is applied to estimate the multitarget states and tracks jointly. The clustering process includes two steps: the prediction and update. The key to the proposed algorithm is to use the prediction as the initial points and the convergent points as the es- timates. Besides, Expectation-Maximization (EM) and Markov Chain Monte Carlo (MCMC) ap- proaches are used for the FMM parameter estimation.

Unpower aerocraft augmented state feedback tracking guaranteed cost control

Aimed at designing the unpower aerocraft attitude control system in a simple and practical way, the guaranteed cost control is adopted. To eliminate the steady-error, a novel tracking control approach--augmented state feedback tracking guaranteed cost control is proposed. Firstly, the unpower aerocraft is modeled as a linear system with norm bounded parameter uncertain, then the linear matrix inequality based state feedback guaranteed cost control law is combined with the augmented state feedback tracking control from a new point of view. The sufficient condition of the existence of the augmented state feedback tracking guaranteed cost control is derived and converted to the feasible problem of the linear matrix inequality. Finally, the proposed approach is applied to a specified unpower aerocraft. The six dimensions of freedom simulation results show that the proposed approach is effective and feasible.

STOCHASTIC NOISE TOLERANCE:ENHANCED FULL STATE OBSERVER VS. KALMAN FILTER FROM VIDEO TRACKING PERSPECTIVE

A control-based full state observer scheme is explored for video target tracking application, and is enhanced with a lowpass filter for improving the tracking precision, thus forming an Enhanced Full State Observer (EFSO). The whole design is based on the given lab-generated video sequence with motion of an articulate target. To evaluate the EFSO’s stochastic noise tolerance, a Kalman Filter (KF) is intentionally employed in tracking the same target with the given Gaussian white noises. The comparison results indicate that, for system noises of certain statistics, the proposed EFSO has its own noise resistance capacity that is superior to that of KF and is more advantageous for implementation.

Analysis of Maximum PowerPoint Tracking (MPPT) Adaptability in Inverters of the Three-Phase Photovoltaic Systems Integrated into the Electrical Grid of Congo-Brazzaville

This paper investigates the adaptability of Maximum Power Point Tracking (MPPT) algorithms in single-stage three-phase photovoltaic (PV) systems connected to the grid of Congo-Brazzaville and compares the attributes of various conventional, significance and novelty of controller system of the proposed of method and improved Incremental Conductance algorithms, Perturbation and Observation Techniques, and other Maximum Power Point Tracking (MPPT) algorithms in normal and partial shading conditions. Performance evaluation techniques are discussed on the basis of the dynamic parameters of the PV system although the control of this structure is relatively advanced technology but the conversion efficiency is difficult to improve due to increase in transformation series. The single stage topology has a simple topology with high reliability and efficiency because of high power consumption, but control algorithm is more complex because of its power convert main circuit a new strategy is being developed. This paper describes a method for maximum power point tracking (MPPT) in the single-stage and three single-phase PV grid-connected system. In the paper, the nonlinear output characteristics of the PV including I-V & P-V are obtained in changed solar insulations or temperature based on MATLAB, and the MPPT algorithm which is based on the P & O algorithm method, compared with Incremental Conductance, is also described, a dimensioning of the impedance adapter for better stabilization. A comparison SPWM and SVPWM control methods in the case of a grid connection applied to the electrical grid of Republic of Congo and their influences on the dynamic performance of the system and their impact in reducing the harmonic rate for better injection into the grid. The simulation model of three single-phase PV grid-connected system is built, and simulation results show the MPPT algorithm has excellent dynamic and static performances, which verifies the Incremental Conductance is effective for MPPT in the single-stage and three single-phase PV grid-connected system.

Application of Road Information in Ground Moving Target Tracking

A new algorithm is developed to achieve accurate state estimation in ground moving target tracking by means of using road information. It is an adaptive variable structure interacting multiple model estimator with dynamic models modification （DMM VS-IMM for short）. Firstly, road information is employed to modify the target dynamic models used by filter, including modification of state transition matrix and process noise. Secondly, road information is applied to update the model set of a VS-IMM estimator. Predicted state estimation and road information are used to locate the target in the road network on which the model set is updated and finally IMM filtering is implemented. As compared with traditional methods, the accuracy of state estimation is improved for target moving not only on a single road, but also through an intersection. Monte Carlo simulation demonstrates the efficiency and robustness of the proposed algorithm with moderate computational loads.

Trajectory Tracking of Vertical Take-off and Landing Unmanned Aerial Vehicles Based on Disturbance Rejection Control

We investigate the trajectory tracking problem of vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV), and propose a practical disturbance rejection control strategy. Firstly, the nonlinear error model is established completely by the modified Rodrigues parameters, while considering dynamics of the servo actuators. Then, a hierarchical control scheme is applied to design the translational and rotational controllers based on the time-scale property of each subsystem, respectively. And the linear extended state observer and auxiliary observer are used to deal with the uncertainties and saturation. At last, global stability of the closed-loop system is analyzed based on the singular perturbation theory. Simulation results show the effectiveness of the proposed control strategy. © 2014 Chinese Association of Automation.

Airborne Target State Estimator

A Target State Estimator (TSE) for airborne radar system is proposed in this paper. It is very important for fire control system to obtain accurate estimation of the maneuvering target and the TSE becomes a key link in the integrated Flight/Fire Control (IFFC) system. By adopting the Cartesian coordinates and pseudomeasurements ,the result ed TSE has it s advantages in computation.In addition, by employing accurate range and range-rate redundant filter, the range direction estimations obtained in Cartesian filter are greatly improved. The TSE shows its satisfaCtory performance in the Monte Carlo simulation of the IFFC system.

Asymptotic Tracking Control for Wind Turbines in Variable Speed Mode

This paper presents a variable speed control strategy for wind turbines in order to capture maximum wind power.Wind turbines are modeled as a two-mass drive-train system with generator torque control.Based on the obtained wind turbine model,variable speed control schemes are developed.Nonlinear tracking controllers are designed to achieve asymptotic tracking for a prescribed rotor speed reference signal so as to yield maximum wind power capture.Due to the difficulty of torsional angle measurement,an observer-based control scheme that uses only rotor speed information is further developed for global asymptotic output tracking.The effectiveness of the proposed control methods is illustrated by simulation results.

Nonlinear suboptimal tracking control of spacecraft approaching a tumbling target

We investigate the close-range relative motion and control of a spacecraft approaching a tumbling target. Unlike the traditional rigid-body dynamics with translation and rotation about the center of mass（CM）, the kinematic coupling between translation and rotation is taken into consideration to directly describe the motion of the spacecraft＇s sensors or devices which are not coincident with the CM. Thus, a kinematically coupled 6 degrees-of-freedom（DOF） relative motion model for the instrument（feature point） is set up. To make the chaser spacecraft＇s feature point track the target＇s, an optimal tracking problem is defined and a control law with a feedback-feedforward structure is designed. With quasi-linearization of the nonlinear dynamical system, the feedforward term is computed from a specified constraint about the dynamical system and the reference model, and the feedback action is derived starting from the state-dependent Ricca equation（SDRE）. The proposed controller is compared with an existing suboptimal tracking controller, and numerical simulations are presented to illustrate the effectiveness and superiority of the proposed method.

Consecutive tracking for ballistic missile based on bearings-only during boost phase

This paper proposes a modified centralized shifted Rayleigh filter（MCSRF） algorithm for tracking boost phase of ballistic missile（BM） trajectory with a highly nonlinear dynamical model based on bearings-only.This paper contributes three folds.Firstly,the mathematical model of an MCSRF for multiple passive sensors is derived.Then,minimum entropy based onedimensional optimization search to adaptively adjust the probability of the different filters for real time state estimation is deployed.Finally,the unscented transform（UT） is introduced to resolve the asymmetric state estimation problem.Simulation results show that the proposed algorithm can consecutively track the BM precisely during the boost phase.In comparison with the unscented Kalman filter（UKF） algorithm,the proposed algorithm effectively reduces the tracking position and velocity root mean square（RMS） errors,which will make more sense for early precision interception.

Measurement of Radon Gas Concentrations in Tap Water Samples for Thi-Qar Governorate Using Nuclear Track Detector (CR-39)

In the present work, we have measured the radon gas concentrations in tap water samples are taken directly from drinking tap water in sites houses being carried in Thi-Qar governorate by using nuclear track detector (CR-39). The results of measurements have shown that the highest average radon concentration in water samples is found in AL-Refai region which is equal to (0.223 ± 0.03 Bq/L), while the lowest average radon gas concentration is found in AL-Fajr region which is equal to (0.108 ± 0.01 Bq/L), with an average value of (0.175 ± 0.03 Bq/L). The highest value of annual effective dose (AED) in tap water samples is found in AL-Refai region, which is equal to (0.814 μSv/y), while the lowest value of (AED) is found in AL-Fajr region which is equal to (0.394 μSv/y), with an average value of (0.640 ± 0.1 μSv/y). The present results have shown that radon gas concentrations in tap water samples are less than the recommended international value (11.1 Bq/L). There for tap water in all the studied sites in Thi-Qar governorate is safe as for as radon concentration being concerned.

Posterior Cramer-Rao lower bounds for bearing-only tracking

In the state estimation of passive tracking systems, the traditional approximate expression for the Cramero-Rao lower bound （CRLB） does not take two factors into consideration, that is, measurement origin uncertainty aad state noise. Such treatment is only valid in ideal situation but it is not feasible in actual situation. In this article, considering the two factors, the posterior Cramer-Rao lower bound （PCRLB） recursion expression for the error of bearing-only tracking is derived. Then, further analysis is carried out on the PCRLB. According to the final result, there are four main parameters that play a role in the performance of the PCRLB, that is, measurement noise, detection probability, state noise and clutter density, amongst which the first two have greater impact on the performance of the PCRLB than the others.

基于状态可观测性和多模态数据PF的移动目标跟踪

为了实现对移动目标的跟踪,提出了一种新颖的基于目标状态可观测性和多模态数据粒子滤波(Particle Filter,PF)的跟踪方案。通过部署在目标移动区域中的传感器获得跟踪目标的距离和到达方向测量值,对接收到的数据进行预处理来计算PF的观测值,以形成一个临时距离图像。通过利用状态更新函数和形成的候选图像模板确定目标状态向量;在PF器中加入额外的加权阶段,使得PF器可自适应地同步多模态数据流,以实现鲁棒的目标跟踪。仿真实验结果验证了所提方案能够有效地跟踪移动目标。

Adaptive practical output tracking of a class of nonlinear systems

Focus is laid on the adaptive practical output-tracking problem of a class of nonlinear systems with high-order lower-triangular structure and uncontrollable unstable linearization. Using the modified adaptive addition of a power integrator technique as a basic tool, a new smooth adaptive state feedback controller is designed. This controller can ensure all signals of the closed-loop systems are globally bounded and output tracking error is arbitrary small.

Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

Uncertain IMM Estimator for Multi-Sensor Target Tracking

Interacting Multiple Model (IMM) estimator can provide better performance of target tracking than mono model Kalman filter. In multi-sensor system ordinarily, availability of measurement from different sensors is stochastic, and it is difficult to construct uniform global observation vector and observation matrix appropri-ately in existing method. An IMM estimator for uncertain measurement is presented. By the method invalid measurement is regarded as outlier, and approximation is reconstructed by feedback of system state estima-tion of fusion center. Then nominally generalized certain measurement can be obtained by substituting re-constructed one for invalid one. The generalized certain measurement can be centralized to construct global measurement and provided to IMM estimator, and existing multi-sensor IMM estimation method is general-ized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.

Adaptive unscented Kalman filter for parameter and state estimation of nonlinear high-speed objects

An adaptive unscented Kalman filter （AUKF） and an augmented state method are employed to estimate the timevarying parameters and states of a kind of nonlinear high-speed objects. A strong tracking filter is employed to improve the tracking ability and robustness of unscented Kalman filter （UKF） when the process noise is inaccuracy, and wavelet transform is used to improve the estimate accuracy by the variance of measurement noise. An augmented square-root framework is utilized to improve the numerical stability and accuracy of UKF. Monte Carlo simulations and applications in the rapid trajectory estimation of hypersonic artillery shells confirm the effectiveness of the proposed method.

带有状态/输入量化的无人船自适应模糊跟踪控制

针对海上通讯带宽受限情况下无人船的航迹跟踪控制问题,设计了一种带有状态量化和输入量化的自适应反馈跟踪控制方案。在保证有效跟踪的同时,减少执行器执行频次,降低控制幅度。首先,在不考虑量化情况下基于自适应反步法设计了系统跟踪控制律,并结合动态面技术有效降低了虚拟控制律的计算量膨胀问题。对于控制系统中存在的不确定项,利用模糊逻辑系统进行逼近。其次,采用均匀量化器分别对控制系统中的状态变量和输入变量进行量化,且量化后的状态反馈信息被用于无人船航迹跟踪控制器的设计。根据所得到的量化信息,给出了同时考虑状态量化和输入量化的无人船航迹跟踪控制律。在稳定性分析中,通过Lyapunov稳定性理论证明了在不考虑量化的情况下闭环控制系统的稳定性,并根据递归的方法证明了闭环控制系统中量化变量和非量化变量之间误差的有界性。基于给定的引理,最终证明了在同时考虑状态量化和输入量化的情况下,所设计的带有状态量化和输入量化的模糊自适应反馈跟踪控制系统的稳定性。最后,通过两组仿真实验验证了所提方案的实用性。即在同时考虑状态量化和输入量化的情况下,无人船仍能保持对理想轨迹良好的跟踪性能,并有效减轻了执行器的执行频次,更符合航海工程实践。

线性自抗扰控制系统的稳定性分析方法

针对二阶线性系统和二阶线性自抗扰控制,提出了基于误差的稳定性分析方法。该方法通过观测误差、跟踪误差状态量建立闭环系统状态空间模型,借助状态空间模型的特征值分析闭环系统的收敛性,并运用李雅普诺夫法判断系统的稳定性。与基于劳斯判据的稳定性分析方法相比较,二者的分析结论一致,算例和仿真结果验证了所提方法的简单性和有效性。此外,通过仿真和蒙特卡洛实验验证了自抗扰控制相比于PID控制在跟踪性能、抗扰能力和鲁棒性等方面的优势。

超声焊接全状态频率跟踪算法

超声金属焊接负载重且变化剧烈,易导致换能器出现无阻性点和电源频率误跟踪。基于梅森等效模型,提出一种能同时计算出谐振频率与反谐振频率,避免误跟踪的全状态频率跟踪算法。存在阻性点时,利用3个不同发波频率及其对应的相位差,算出跟踪目标频率;当换能器处于无阻性点状态时,把跟踪目标变为相位差最小点,从而实现全状态频率跟踪。通过仿真软件对算法进行仿真,算法能有效避免误跟踪。焊接试验结果表明,能较易实现谐振频率和反谐振频率的频率跟踪,在无阻性点的工况下焊接过程平稳。