A tracking loop based on tightly coupled range and velocity filter equations

A joint range-velocity closed tracking loop,which is based on tightly coupled range and velocity filter is proposed.When the measured velocity value is adopted in the range tracking loop to modify the velocity and acceleration equations from traditional α-β-γ filter,the tracking loop based on tightly coupled range and velocity filter can not only track the range and the velocity simultaneously,but also improve the range tracking accuracy.The experimental results show that the tracking errors about range thermal noise in the proposed loop is lower than those in the traditional loop over 2.2 dB,when filter parameters satisfy least mean-square error criterion.Moreover,with the increase of the filter parameter,the tracking performance of our schemes are improved accordingly.     

Learning-based tracking of complex non-rigid motion

This paper describes a novel method for tracking complex non-rigid motions by learning the intrinsic object structure. The approach builds on and extends the studies on non-linear dimensionality reduction for object representation, object dynamics modeling and particle filter style tracking. First, the dimensionality reduction and density estimation algorithm is derived for unsupervised learning of object intrinsic representation, and the obtained non-rigid part of object state reduces even to 2-3 dimensions. Secondly the dynamical model is derived and trained based on this intrinsic representation. Thirdly the learned intrinsic object structure is integrated into a particle filter style tracker. It is shown that this intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle filter style tracker more robust and reliable.Extensive experiments are done on the tracking of challenging non-rigid motions such as fish twisting with selfocclusion, large inter-frame lip motion and facial expressions with global head rotation. Quantitative results are given to make comparisons between the newly proposed tracker and the existing tracker. The proposed method also has the potential to solve other type of tracking problems.     

Novel sensor scheduling and energy-efficient quantization for tracking target in wireless sensor networks

This paper focuses on sensor scheduling and information quantization issues for target tracking in wireless sensor networks (WSNs). To reduce the energy consumption of WSNs, it is essential and effective to select the next tasking sensor and quantize the WSNs data. In existing works, sensor scheduling’ goals include maximizing tracking accuracy and minimizing energy cost. In this paper, the integration of sensor scheduling and quantization technology is used to balance the tradeoff between tracking accuracy and energy consumption. The main characteristic of the proposed schemes includes a novel filtering process of scheduling scheme, and a compressed quantized algorithm for extended Kalman filter (EKF). To make the algorithms more efficient, the proposed platform employs a method of decreasing the threshold of sampling intervals to reduce the execution time of all operations. A real tracking system platform for testing the novel sensor scheduling and the quantization scheme is developed. Energy consumption and tracking accuracy of the platform under different schemes are compared finally.     

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Cooperative fusion particle filter tracker

Robust visual tracking has become an important topic in the field of computer vision. Integrating multiple cues has proved to be a promising approach to visual tracking in situations where no single cue is suitable. In this work, a new particle filter based visual tracking algorithm is proposed. By introducing a new cooperative fusion strategy, the proposed tracker has better fault tolerance ability than the traditional methods. Experiments are performed in various tracking scenes to evaluate the proposed algorithm, and the results show improved tracking accuracy.     

Multipath effects on vector tracking algorithm for GNSS signal

The vector tracking algorithm uses a single extended Kalman filter （EKF） to predict the time-delays and the Doppler deviations of the GNSS signal, while also estimating the user＇s position, velocity, and clock state. In this paper, the effects of multipath on the tracking performance of the vector delay / frequency lock loop （VDFLL） is studied for better application in the multipath environment. The error expressions of the measurements are given in theory. The tracking error caused by multipath is reduced by VDFLL, which is proved by the tracking error of VDFLL through a new iterating method. The theoretical analysis is verified by the Monte Carlo simulation.     

Shipborne radar maneuvering target tracking based on the variable structure adaptive grid interacting multiple model

The trajectory of a shipbome radar target has a certain complexity, randomness, and diversity. Tracking a strong maneuvering target timely, accurately, and effectively is a key technology for a shipbome radar tracking system. Combining a variable structure interacting multiple model with an adaptive grid algorithm, we present a variable structure adaptive grid inter- acting multiple model maneuvering target tracking method. Tracking experiments are performed using the proposed method for five maneuvering targets, including a uniform motion - uniform acceleration motion target, a uniform acceleration motion - uni- form motion target, a serpentine locomotion target, and two variable acceleration motion targets. Experimental results show that the target position, velocity, and acceleration tracking errors for the five typical target trajectories are small. The method has high tracking precision, good stability, and flexible adaptability.     

Object tracking based on particle filter with discriminative features

This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.     

New optimal algorithm of data association for multi-passive-sensor location system

In dense target and false detection scenario of four time difference of arrival (TDOA) for multi-passive-sensor location system, the global optimal data association algo- rithm has to be adopted. In view of the heavy calculation burden of the traditional optimal assignment algorithm, this paper proposes a new global optimal assign- ment algorithm and a 2-stage association algorithm based on a statistic test. Compared with the traditional optimal algorithm, the new optimal algorithm avoids the complicated operations for finding the target position before we calculate as- sociation cost; hence, much of the procedure time is saved. In the 2-stage asso- ciation algorithm, a large number of false location points are eliminated from can- didate associations in advance. Therefore, the operation is further decreased, and the correct data association probability is improved in varying degrees. Both the complexity analyses and simulation results can verify the effectiveness of the new algorithms.     

Maximum correntropy-based pseudolinear Kalman filter for passive bearings-only target tracking

This paper proposes a new approach for solving the bearings-only target tracking (BoT) problem by introducing a maximum correntropy criterion to the pseudolinear Kalman filter (PLKF). PLKF has been a popular choice for solving BoT problems owing to the reduced computational complexity. However, the coupling between the measurement vector and pseudolinear noise causes bias in PLKF. To address this issue, a bias-compensated PLKF (BC-PLKF) under the assumption of Gaussian noisewas formulated. However, this assumptionmay not be valid in most practical cases. Therefore, a bias-compensated PLKF with maximum correntropy criterion is introduced, resulting in two new filters: maximum correntropy pseudolinear Kalman filter (MC-PLKF) and maximum correntropy bias-compensated pseudolinear Kalman filter (MC-BC-PLKF). To demonstrate the performance of the proposed estimators, a comparative analysis assuming large outliers in the process and measurement model of 2D BoT is conducted. These large outliers are modeled as non-Gaussian noises with diverse noise distributions that combine Gaussian and Laplacian noises. The simulation results are validated using root mean square error (RMSE), average RMSE (ARMSE), percentage of track loss and bias norm. Compared to PLKF and BC-PLKF, all the proposed maximum correntropy-based filters (MC-PLKF and MC-BC-PLKF) performed with superior estimation accuracy.     

Fast parallel algorithms for discrete Gabor expansion and transform based on multirate filtering

The Gabor transform has long been recognized as a very useful tool for the joint time and frequency analysis in signal processing.Its real time applications,however,were limited due to the high computational complexity of the Gabor transform algorithms.In this paper,some novel and fast parallel algorithms for the finite discrete Gabor expansion and transform are presented based on multirate filtering.An analysis filter bank is designed for the finite discrete Gabor transform(DGT)and a synthesis filter bank is designed for the finite discrete Gabor expansion(DGE).Each of the parallel channels in the two filter banks has a unified structure and can apply the FFT and the IFFT to reduce its computational load.The computational complexity of each parallel channel does not change as the oversampling rate increases.In fact,it is very low and depends only on the length of the input discrete signal and the number of the Gabor frequency sampling points.The computational complexity of the proposed parallel algorithms is analyzed and compared with that of the major existing parallel algorithms for the finite DGT and DGE.The results indicate that the proposed parallel algorithms for the finite DGT and DGE based on multirate filtering are very attractive for real time signal processing.     

A Stable Analytical Solution Method for Car-Like Robot Trajectory Tracking and Optimization

In this paper, the car-like robot kinematic model trajectory tracking and control problem is revisited by exploring an optimal analytical solution which guarantees the global exponential stability of the tracking error. The problem is formulated in the form of tracking error optimization in which the quadratic errors of the position, velocity, and acceleration are minimized subject to the rear-wheel car-like robot kinematic model. The input-output linearization technique is employed to transform the nonlinear problem into a linear formulation. By using the variational approach, the analytical solution is obtained, which is guaranteed to be globally exponentially stable and is also appropriate for real-time applications. The simulation results demonstrate the validity of the proposed mechanism in generating an optimal trajectory and control inputs by evaluating the proposed method in an eight-shape tracking scenario.     

机器人轨线跟踪的视觉与控制集成方法1)

Aiming at high speed visual tracking,this paper presents a new control scheme from the point of integration of vision and control.To track a curve,the control is decomposed into two subfunctions.One is the velocity control which controls the center of a CCD camera to move along the tangential direction of the curve.The other is the position control which controls the cneter of the CCD camera to move in the normal direction approaching to the curve.In order to improve the tracking performances,the proposed control scheme was realized in servo rate by the aid of a transputer based parallel controller.The tracking speed reached 250mm/s with a small field of view(26.4mm×26.4mm).     

Optimal Fusion Reduced-Order Kalman Filters Weighted by Scalars for Stochastic Singular Systems

Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion reduced-order Kalman filter with scalar weights is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. It has higher accuracy than any local filter does. Compared with the distributed fusion filter weighted by matrices, it has lower accuracy but has reduced computational burden. Computation formula of cross-covariance matrix of the filtering errors between any two sensors is given. An example with three sensors shows the effectiveness.     

An optimal adaptive H-infinity tracking control design via wavelet network

In this paper, an optimal adaptive H-infinity tracking control design method via wavelet network for a class of uncertain nonlinear systems with external disturbances is proposed to achieve H-infinity tracking performance. First, an alternate tracking error and a performance index with respect to the tracking error and the control effort are introduced in order to obtain better performance, especially, in reducing the cost of the control effort in the case of small attenuation levels. Next, H-infinity tracking performance, which attenuates the influence of both wavelet network approximation error and external disturbances on the modified tracking error, is formulated. Our results indicate that a small attenuation level does not lead to a large control signal. The proposed method insures an optimal trade-off between the amplitude of control signals and the performance of tracking errors. An example is given to illustrate the design efficiency.     

Target tracking based on the extended H-infinity filter in wireless sensor networks

In this paper, we propose a new target tracking approach for wireless sensor networks (WSNs) by using the extended H-infinity filter. First, the extended H-infinity filter for nonlinear discrete-time systems is deduced through the Krein space analysis scheme. Then, the proposed extended H-infinity filtering algorithm is applied to target tracking in wireless sensor networks. Finally, experiments are conducted through a small wireless sensor network test-bed. Both experimental and simulation results illustrate that the extended H-infinity filtering algorithm is more accurate to track a moving target in wireless sensor networks than using the extended Kalman filter in the case of having no knowledge of the statistics of the environment and the target to be tracked.     

Energy-balanced multiple-sensor collaborative scheduling for maneuvering target tracking in wireless sensor networks

An energy-balanced multiple-sensor collaborative scheduling is proposed for maneuvering target tracking in wireless sensor networks (WSNs). According to the position of the maneuvering target, some sensor nodes in WSNs are awakened to form a sensor cluster for target tracking collaboratively. In the cluster, the cluster head node is selected to implement tracking task with changed sampling interval. The distributed interactive multiple model (IMM) filter is employed to estimate the target state. The estimat...     

Robust state estimation for uncertain linear systems with deterministic input signals

In this paper, we investigate state estimations of a dynamical system in which not only process and measurement noise, but also parameter uncertainties and deterministic input signals are involved. The sensitivity penalization based robust state estimation is extended to uncertain linear systems with deterministic input signals and parametric uncertainties which may nonlinearly affect a state-space plant model. The form of the derived robust estimator is similar to that of the well-known Kalman filter with a comparable computational complexity. Under a few weak assumptions, it is proved that though the derived state estimator is biased, the bound of estimation errors is finite and the covariance matrix of estimation errors is bounded. Numerical simulations show that the obtained robust filter has relatively nice estimation performances.     

A tracking and predicting scheme for ping pong robot

We describe a new tracking and predicting scheme applied to a lab-made ping pong robot. The robot has a monocular vision system comprised of a camera and a light. We propose an optimized strategy to calibrate the light center using the least square method. An ellipse fitting method is used to precisely locate the center of ball and shadow on the captured image. After the triangulation of the ball position in the world coordinates, a tracking algorithm based on a Kalman filter outputs an accurate estimation of the flight states including the ball position and velocity. Furthermore, a neural network model is constructed and trained to predict the following flight path. Experimental results show that this scheme can achieve a good predicting precision and success rate of striking an incoming ball. The robot can achieve a success rate of about 80% to return a flight ball of 5 m/s to the opposite court.     

Passive tracking from the combined set of bearings and frequency measurements by single satellite

In this paper,a new passive modified iterated extended Kalman filter(MIEKF) using the combined set of bearings and frequency measurements in Earth Centered Inertial(ECI) coordinate is proposed.A new measurement update equation of MIEKF is derived by modifying the objective function of the Gauss-Newton iteration.A new gain equation and iteration termination criteria are acquired by applying the property of the maximum likelihood estimate. The approximated second order linearized state propagation equation,Jacobian matrix of state transfer and measurement equations are derived in satellite two-body movement.The tracking performances of MIEKF,iterated extended Kalman filter(IEKF) and extended Kalman filter(EKF) are compared via Monte Carlo simulations through simulated data from STK8.1.Simulation results indicate that the proposed MIEKF is possible to passively track low earth circular orbit satellite by a high earth orbit satellite,and has higher tracking precision than the IEKF and EKF.