The Signal Separation for MIMO Radar Based on Particle Filter Algorithm

2014 ◽  
Vol 536-537 ◽  
pp. 34-38
Author(s):  
Yan Ping Zhu ◽  
Ke Tang ◽  
Jia Qiang Li ◽  
Jin Li Chen
Keyword(s):  
Particle Filter ◽  
Mimo Radar ◽  
Radar System ◽  
Signal Separation ◽  
Separation Performance ◽  
Good Separation ◽  
Match Filter ◽  
Chaotic Signals ◽  
Particle Filter Algorithm ◽  
Non Gaussian

The orthogonality of transmitting signals affects the performance of Muitple Input Mulitiple Output (MIMO) radar system. The chaotic signals was adopted at the transmitter to achieve the approximate orthogonal. A signal separation aprroach for MIMO Radar based on the particle filter in Non-Gaussian clutter environment was proposed. Before the match filter (MF), the particle filter is quite suitable for chaotic signals separation. Simulation results show that the proposed algorithm can realize a good separation performance. At the receiver, the coherent processing results show that this method has the better target resolution ability than the tranditional match filter alone.

scholarly journals IMM Iterated Extended Particle Filter Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yang Wan ◽  
Shouyong Wang ◽  
Xing Qin
Keyword(s):  
Particle Filter ◽  
Density Function ◽  
The State ◽  
Arbitrary Distribution ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Maneuvering Target ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Importance Density Function

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.

A New Adaptive UPF Algorithm through Improved Relative Entropy

2013 ◽  
Vol 658 ◽  
pp. 569-573
Author(s):  
Wen Tao Yu ◽  
Jun Peng ◽  
Xiao Yong Zhang
Keyword(s):  
Particle Filter ◽  
Relative Entropy ◽  
Particle Number ◽  
Time Step ◽  
Unscented Particle Filter ◽  
Posterior Probability Density ◽  
The Difference ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Number Of Particles

Unscented particle filter (UPF) has high accuracy of state estimation for nonlinear system with non-Gaussian noise. While the computation of traditional unscented particle filter is huge and this depends on the particle number. In this paper we propose a new adaptive unscented particle filter algorithm AUPF through improved relative entropy which can adaptively adjust the particle number during filtering. Firstly the relative entropy is used to measure the distance between the posterior probability density and the importance proposal and the least number of particles for the next time step is decided according to the relative entropy. Then the least number is adjusted to offset the difference between the importance proposal and the true distribution. This algorithm can effectively reduce unnecessary particles meanwhile reduce the computation. The simulation results show the effectiveness of AUPF.

scholarly journals Analysis of Single Channel Blind Source Separation Algorithm for Chaotic Signals

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Jiai He ◽  
Yuxiao Song ◽  
Panpan Du ◽  
Lei Xu
Keyword(s):  
Particle Filter ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Estimation Algorithm ◽  
Chaotic Signal ◽  
Blind Separation ◽  
Useful Signal ◽  
Chaotic Signals ◽  
Particle Filter Algorithm

In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems—based on the particle filter estimation algorithm—an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals.

A Novel Modified Particle Filter Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 1895-1899
Author(s):  
Qing Hua Gao ◽  
Jie Wang ◽  
Ming Lu Jin
Keyword(s):  
Probability Distribution ◽  
Particle Filter ◽  
State Estimation ◽  
Target Tracking ◽  
Optimization Method ◽  
Effective Solution ◽  
Optimal Probability ◽  
Particle Filter Algorithm ◽  
Non Gaussian ◽  
Filtering Problem

The particle filter (PF) algorithm provides an effective solution to the non-linear and non-Gaussian filtering problem. However, when the motion noises or observation noises are strong, the degenerate phenomena will occur, which leads to poor estimation. In this paper, we propose a modified particle filter (MPF) algorithm for improving the estimated precision through a particle optimization method. After calculating the coarse estimation with the traditional PF, we optimize the particles according to their weights and relative positions, then, move the particles toward the optimal probability distribution. The state estimation and target tracking experiments demonstrate the outstanding performance of the proposed algorithm.

scholarly journals Data assimilation with an improved particle filter and its application in the TRIGRS landslide model

2018 ◽  
Vol 18 (10) ◽  
pp. 2801-2807 ◽  
Author(s):  
Changhu Xue ◽  
Guigen Nie ◽  
Haiyang Li ◽  
Jing Wang
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Gaussian State ◽  
Assimilation Experiment ◽  
Resampling Method ◽  
Particle Propagation ◽  
Particle Filter Algorithm ◽  
Mean Square Difference ◽  
Non Gaussian ◽  
Landslide Model

Abstract. Particle filters have become a popular algorithm in data assimilation for their ability to handle nonlinear or non-Gaussian state-space models, but they have significant disadvantages. In this work, an improved particle filter algorithm is proposed. To overcome the particle degeneration and improve particles' efficiency, the processes of particle resampling and particle transfer are updated. In this improved algorithm, particle propagation and the resampling method are ameliorated. The new particle filter is applied to the Lorenz-63 model, and its feasibility and effectiveness are verified using only 20 particles. The root-mean-square difference (RMSD) of estimations converges to stable when there are more than 20 particles. Finally, we choose a peristaltic landslide model and carry out an assimilation experiment of 20 days. Results show that the estimations of states can effectively correct the running offset of the model and the RMSD is convergent after 3 days of assimilation.

The Particle Filter Algorithm Research for Target Tracking Based on Information Fusion

2012 ◽  
Vol 628 ◽  
pp. 440-444 ◽  
Author(s):  
Juan Li ◽  
Hui Juan Hao ◽  
Mao Li Wang
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Information Fusion ◽  
Particle Filtering ◽  
Tracking System ◽  
Distributed Sensor ◽  
Multi Target Tracking ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Non Gaussian

This paper researches the particle filters Algorithms for target tracking based on Information Fusion, it combines the traditional Kalman filter with the particle filter. For multi-sensor and multi-target tracking system with complex application background, which is nonlinear and non-gaussian system, the paper proposes an effective particle filtering algorithm based on information fusion for distributed sensor, this algorithm contributes to the solution of particle degradation problems and the phenomenon of particle lack, and achieve high precision for target tracking.

Multi-User Detection Based on Regularized Particle Filter in Impulsive Noise

2014 ◽  
Vol 543-547 ◽  
pp. 2444-2447
Author(s):  
Jin Long Xian ◽  
Ming Hai Dong
Keyword(s):  
Particle Filter ◽  
Code Division Multiple Access ◽  
Multiple Access ◽  
Impulsive Noise ◽  
Cdma System ◽  
Particle Filter Algorithm ◽  
Gaussian System ◽  
Regularized Particle Filter ◽  
Non Gaussian ◽  
Code Division Multiple

In this paper, we proposed a regularized particle filter (RPF) algorithm for multi-user detection (MUD) in synchronous code division multiple access (CDMA) system. It is especially suitable for non-linear non-Gaussian system. In the standard particle filter algorithm. If particles weights have vast difference, we needed to re-sampling, but it replicates particles with larger weights and discards particles with small weights, so that it will lose the diversity of particles. Regularized particle filter algorithm can effectively overcome this shortcoming.

scholarly journals Probabilistic Data Association-Feedback Particle Filter for Multiple Target Tracking Applications

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Tao Yang ◽  
Prashant G. Mehta
Keyword(s):  
Particle Filter ◽  
Target Tracking ◽  
Data Association ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Probabilistic Data ◽  
Probabilistic Data Association ◽  
Particle Filter Algorithm ◽  
Gaussian Case ◽  
Non Gaussian

This paper is concerned with the problem of tracking single or multiple targets with multiple nontarget-specific observations (measurements). For such filtering problems with data association uncertainty, a novel feedback control-based particle filter algorithm is introduced. The algorithm is referred to as the probabilistic data association-feedback particle filter (PDA-FPF). The proposed filter is shown to represent a generalization—to the nonlinear non-Gaussian case—of the classical Kalman filter-based probabilistic data association filter (PDAF). One remarkable conclusion is that the proposed PDA-FPF algorithm retains the error-based feedback structure of the classical PDAF algorithm, even in the nonlinear non-Gaussian case. The theoretical results are illustrated with the aid of numerical examples motivated by multiple target tracking (MTT) applications.

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