Yan Liang

Application of Unscented Transformation for Nonlinear State Smoothing

Abstract Motivated by the well-known fact that the state estimate of a smoother is more accurate than that of the corresponding filter, this paper is concerned with the state smoothing problem for a class of nonlinear stochastic discrete systems. Firstly, a novel type of optimal smoother, which provides a unified theoretical framework for the solution of state smoothing problem no matter that system is linear or nonlinear, is derived on the basis of minimum mean squared error (MMSE) estimation theory. Further, in the case that the dynamic model and measurement functions are all nonlinear, a new suboptimal smoother is developed by applying the unscented transformation for approximately computing the smoothing gain in the optimal smoothing framework. Finally, the superior performance of the proposed smoother to the existing extended Kalman smoother (EKS) is demonstrated through a simulation example.

An Effective Hough Transform Based Track Initiation

In this paper, a Hough transform (HT) based track initiation method is proposed to detect targets in 3L (low signal-to-clutter-ratio, low signal-to-noise-ratio, and low detection probability) environment using a new and effective accumulation method. In the new accumulation method, the contributions of each cells votes are determined by the neighbors of the resolving cell, instead of just the cell itself so that the disturbance of sampled probability density, due to finite samples, can be smoothed. Simulation results show that our method not only makes significant improvement in reducing false tracks but also has stronger robustness to measurement noise and clutters, compared with standard HT based track initiation with binary accumulation

A Survey of PHD Filter Based Multi-target Tracking

Probability hypothesis density(PHD) filter has attracted much attention in multi-target tracking, trafc control, image processing, multi-sensor management and other fields. An overview of the emergence, the development and the present research situation of the PHD filter in target tracking is presented here. Special attention is paid to the following areas: PHD filter, its implementation method, the peak and track extraction technology, multi-sensor multitarget tracking, multi-sensor management, PHD smoother, the assessment metrics of multi-target tracking performance,and also the relevant applications. Finally, based on the progress of existing PHD filters, some key issues which need to be focused on for PHD filters in multi-target tracking are introduced.

A novel PSO based acoustic source localization algorithm in Wireless Sensor Network

Acoustic source localization is a very important Wireless Sensor Network (WSN) surveillance task. In real-world implementations, the localization algorithm, which is essentially an optimization of a certain cost function involving all received sensor observations, must be feasible under stringent communication, computation and energy constrains. In this paper, we propose a novel light-weight dynamic population Particle Swarm Optimization (PSO) based method to search the Maximum Likelihood Estimate (MLE) solution for the source location, reducing computation complexity as well as mitigating the affects of local optimum solutions. Moreover, instead of raw data, only signal energy observations are transmitted to the fusion center, such that bandwidth and energy consumptions can be largely decreased. The results of extensive simulations have shown the superior performance of our method in various scenarios.

Design of an Adaptive Particle Filter Based on Variance Reduction Technique

Abstract The main problem of particle filter (PF) in nonlinear state estimation is the particle degeneracy. Resampling operation solves degeneracy to some extent, but it results in the problem of sample impoverishment. Variance reduction technique is proposed to deal with the degeneration phenomenon in this paper, which reduces the variance of the particle weights by selecting an exponential fading factor, and this factor can be chosen adaptively and iteratively in terms of the effective particle number. A theorem is presented to show that this idea is feasible, and the procedure of this new adaptive particle filtering (APF) algorithm is presented. Then, the principle of parameter choice and the limitation of APF are discussed. Finally, a numerical example illustrates that the proposed APF has a higher estimation precision than particle filter – sampling importance resampling (PF-SIR), genetic particle filter (GPF), and particle swarm optimization particle filter (PSOPF), while the computation load of APF is mild.

Kernel based multiple targets tracking

In multiple targets tracking, the split, mergence or occlusion of objects will deteriorate the tracking performance, so a multiple targets tracking method based on kernel is proposed. Making use of the defined matching measurement rule, a matching matrix is constructed according to the detection results based on a Gaussian mixture model and the kernel-based tracking results, and then the spilt and merging operation is completed to realize tracking target initialization, deletion, split, merging and occlusion. The experimental results show that the proposed method can reliably track multiple targets under occlusion in real time.

On Robust Fault-Tolerant Control of Missile Control System

Abstract In order to avoid ballistic missile self-destruction because of sensor failures caused by element aging of long time storage, a reconstructed feedback robust fault-tolerant control scheme is presented. In our scheme, a fault detection method based on the adjacent coefficient of Mallat wavelet is proposed firstly so that the fault information, which is masked by several elastic oscillation signals with time-varying magnitudes and frequencies, can be extracted efficiently. As soon as a fault is detected, the switch control unit immediately isolates the failed sensor and switch from the original control model to the reconstructed one. To remain the stability of the system and control the tracking error in case of time-varying of missile parameters, a frequency domain robust stability control is designed, so that speed test feedback of engine swing angle and gain feedback of normal sensor are used to ensure the control stability and restrict the tracking error within a designed value. Finally, the effectiveness of our method is shown by an example of a Soviet missile flight simulation.

Signal Processing for Output Signals of the Inertia Sensors in the Missile Control System

To improve the reliability of missiles, the reconfiguring fault tolerant control system (RFTCS) should be built in the missile control system. The key problem for building RFTCS is to separate the rigid body angle signal and different order elastic oscillation caused angle signals (RSADS) from the output signals of inertia sensors (OSOIS). Firstly, the accurate location characteristics of FFT in frequency domain are used. OSOIS are transformed by FFT. The frequencies at extrema of frequency response obtained via FFT are the frequencies of RSADS. Secondly, an elliptical filter is designed to let the signals of the frequencies go through and the test signal is designed with combined sine signals whose initial phases are zero and whose frequencies are the frequencies at extrema. The test signal is filtered by the elliptical filter. The phase lag of each sine signal in the test signal is the initial phase of each filtered sine signal. OSOIS are also filtered by the elliptical filter. The filtered signal separated from inertia sensors are adjusted with the phase lags test by the test signal. Finally, RSADS are obtained in real-time. The simulation shows the effectiveness of the approach.

Performance analysis of interacting multiple model algorithm

Abstract A new non-simulation method for performance analysis of Interacting Multiple Model Algorithm is proposed. Firstly, how input-interaction effects model-conditional estimation is analyzed. Four conclusions are made qualitatively. Besides this, the compression ratio of model-conditional error is defined. So the parameters and modeling can be chosen quantitatively. Secondly, how input-interaction effects model probability is analyzed. Input-interaction is found not only to decide the upper and lower limits of model probability but also to lessen the difference among model probabilities, then in the sense of model probability, decaying-memory filtering, damping coefficient and regulating-time are defined. All these works may be useful to choose optimal parameters and design new adaptive filters.