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.

Linear Track Estimation Using Double Pulse Sources for Near-Field Underwater Moving Target

The double pulse sources （DPS） method is presented for linear track estimation in this work. In the field of noise identification of underwater moving target, the Doppler will distort the frequency and amplitude of the radiated noise. To eliminate this, the track estimation is necessary. In the DPS method, we first estimate bearings of two sinusoidal pulse sources installed in the moving target through baseline positioning method. Meanwhile, the emitted and recorded time of each pulse are also acquired. Then the linear track parameters will be achieved based on the geometry pattern with the help of double sources spacing. The simulated results confirm that the DPS improves the performance of the previous double source spacing method. The simulated experiments were carried out using a moving battery car to further evaluate its performance. When the target is 40-60m away, the experiment results show that biases of track azimuth and abeam distance of DPS are under 0.6° and 3.4m, respectively. And the average deviation of estimated velocity is around 0.25m/s.

Kernel density estimation and marginalized-particle based probability hypothesis density filter for multi-target tracking

In order to improve the performance of the probability hypothesis density(PHD) algorithm based particle filter(PF) in terms of number estimation and states extraction of multiple targets, a new probability hypothesis density filter algorithm based on marginalized particle and kernel density estimation is proposed, which utilizes the idea of marginalized particle filter to enhance the estimating performance of the PHD. The state variables are decomposed into linear and non-linear parts. The particle filter is adopted to predict and estimate the nonlinear states of multi-target after dimensionality reduction, while the Kalman filter is applied to estimate the linear parts under linear Gaussian condition. Embedding the information of the linear states into the estimated nonlinear states helps to reduce the estimating variance and improve the accuracy of target number estimation. The meanshift kernel density estimation, being of the inherent nature of searching peak value via an adaptive gradient ascent iteration, is introduced to cluster particles and extract target states, which is independent of the target number and can converge to the local peak position of the PHD distribution while avoiding the errors due to the inaccuracy in modeling and parameters estimation. Experiments show that the proposed algorithm can obtain higher tracking accuracy when using fewer sampling particles and is of lower computational complexity compared with the PF-PHD.

Best linear unbiased estimation algorithm with Doppler measurements in spherical coordinates

In an active radar-tracking system,the target-motion model is usually modeled in the Cartesian coordinates,while the radar measurement usually is obtained in polar/spherical coordinates.Therefore the target-tracking problem in the Cartesian coordinates becomes a nonlinear state estimation problem.A number of measurement-conversion techniques,which are based on position measurements,are widely used such that the Kalman filter can be used in the Cartesian coordinates.However,they have fundamental limitations to result in filtering performance degradation.In fact,in addition to position measurements,the Doppler measurement or range rate,containing information of target velocity,has the potential capability to improve the tracking performance.A filter is proposed that can use converted Doppler measurements（i.e.the product of the range measurements and Doppler measurements） in the Cartesian coordinates.The novel filter is theoretically optimal in the rule of the best linear unbiased estimation among all linear unbiased filters in the Cartesian coordinates,and is free of the fundamental limitations of the measurement-conversion approach.Based on simulation experiments,an approximate,recursive implementation of the novel filter is compared with those obtained by four state-of-the-art conversion techniques recently.Simulation results demonstrate the effectiveness of the proposed filter.

Passive tracking and size estimation of volume target based on acoustic vector intensity

The special sections of volume target are observed with acoustic vector intensity according to the difference among their radiated-noise characteristics, then three sections are tracked with Kalman filtering, and target size is estimated. Simulation results indicate that in ideal condition three sections of a ship can be tracked and ship's size can be estimated even though one of three sections can not be observed.

SPATIAL TRAJECTORY PREDICTION OF VISUAL SERVOING

Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.

Tracking Power System State Evolution with Maximum-correntropy-based Extended Kalman Filter

This paper develops a novel approach to track power system state evolution based on the maximum correntropy criterion,due to its robustness against non-Gaussian errors.It includes the temporal aspects on the estimation process within a maximum-correntropy-based extended Kalman filter(MCEKF),which is able to deal with both nonlinear supervisory control and data acquisition(SCADA)and phasor measurement unit(PMU)measurement models.By representing the behavior of the state variables with a nonparametric model within the kernel density estimation,it is possible to include abrupt state transitions as part of the process noise with non-Gaussian characteristics.Also,a novel strategy to update the size of Parzen windows in the kernel estimation is proposed to suppress the effects of suspect samples.By properly adjusting the kernel bandwidth,the proposed MCEKF keeps its accuracy during sudden load changes and contingencies,or in the presence of bad data.Simulations with IEEE test systems and the Brazilian interconnected system are carried out.The results show that the method deals with non-Gaussian noises in both the process and measurement,and provides accurate estimates of the system state under normal and abnormal conditions.

Feedback structure based entropy approach for multiple-model estimation

The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode uncertainties.The model sequence set adaptation（MSA）is the key to design a better VSMM.However,MSA methods in the literature have big room to improve both theoretically and practically.To this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain conditions.The fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,MEVSMM.Firstly,the full Markov chains are used to achieve optimal solutions.Secondly,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and optimality.The numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.