Improved Auxiliary Particle Filter for SINS/SAR Navigation

In order to obtain the relatively appropriate importance density function and alleviate the problem of particle degradation, a new improved auxiliary particle filter algorithm is proposed. After calculating the auxiliary variable, the adaptive regulator is employed to obtain the state estimation. So, the latest measurement information is efficiently utilized to establish a better importance density function in the importance sampling process. Then, the process of particle weights’ adaptive adjustment and random-weighted calculation can keep the diversity of particles and improve the filter precision; thus, it can better solve the filter problem of nonlinear system model error and noise interference. The simulation and analysis result show that the proposed algorithm can optimize the filter performance and improve the calculation precision in the positioning of the SINS/SAR integrated navigation system, compared with the other two existing filters.

Study on Maneuvering Target On-axis Tracking Algorithm of Modified Current Statistical Model

Aiming at the model adaptability and the filter precision on the maneuvering target on-axis tracking, The paper put forward a filter algorithm based on modified current statistical model. The algorithm can enhance the model adaptability to the weak and non-maneuvering maneuvering target. The method uses Unscented Kalman Filter to obtain the importance density function of each particle, improves the Particle Filter estimation performance.By applying the proposed algorithm to the on-axis tracking system, the simulation results demonstrate that algorithm can effectively improve filter performance and tracking precision.

IMM Iterated Extended Particle Filter Algorithm

In order to solve the tracking problem of radar maneuvering target in nonlinear system model and non-Gaussian noise background, this paper puts forward one interacting multiple model (IMM) iterated extended particle filter algorithm (IMM-IEHPF). The algorithm makes use of multiple modes to model the target motion form to track any maneuvering target and each mode uses iterated extended particle filter (IEHPF) to deal with the state estimation problem of nonlinear non-Gaussian system. IEHPF is an improved particle filter algorithm, which utilizes iterated extended filter (IEHF) to obtain the mean value and covariance of each particle and describes importance density function as a combination of Gaussian distribution. Then according to the function, draw particles to approximate the state posteriori density of each mode. Due to the high filter accuracy of IEHF and the adaptation of system noise with arbitrary distribution as well as strong robustness, the importance density function generated by this method is more approximate to the true sate posteriori density. Finally, a numerical example is included to illustrate the effectiveness of the proposed methods.

OPTIMIZED PARTICLES FOR 3D TRACKING

3D visual tracking is useful for many applications. In this paper, we propose two different ways for different system configurations to optimize particle filter for enhancing 3D tracking performances. On the one hand, a new data fusion method is proposed to obtain the optimal importance density function for active vision systems. With this method, the importance density function in particle filter can be modified to represent posterior states by particle crowds in a better way. Thus, it makes the tracking system more robust to noise and outliers. On the other hand, we develop a method for reconfigurable vision systems to maximize the effective sampling size in particle filter, which consequentially helps to solve the degeneracy problem and minimize the tracking error. Simulation and experimental results verified the effectiveness of the proposed method.

The Research on Chaotic Signal Tracking Algorithm for SR-UKF-PF

In view of the difficult in the chaotic signal detection and track in the low signal-to-noise(SNR) environment, a modified SR-UKF-PF is developed that has much better robustness than the traditional SR-UKF and gets almost the same performance as the Particle filter. The main idea of this algorithm is to calculated by the system state transition matrix and the error covariance matrix which are gained from the SR-UKF and the sequential fusion to construct the importance density function of the particle filter. Then the importance density function can integrates the latest observation into system state transition density, and the proposal distribution can approximate the posterior distribution maximumly. To demonstrate the effectiveness of this model, simulations are carried out based on tracking algorithm for the typical chaotic time series of low dimension chaos mapping and super chaos mapping. The simulation results show that this algorithm can overcome the flaw that it is hard to get the optimization importance density function in the particle filter and significantly improves the accuracy of state estimation, and demonstrates the superiorities of particle filtering in the low SNR.