Performance Degradation Due to Particle Impoverishment in Particle Filtering

Lim, Jaechan The Korean Institute of Electrical Engineers 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Particle filtering (PF) has shown its outperforming results compared to that of classical Kalman filtering (KF), particularly for highly nonlinear problems. However, PF may not be universally superior to the extended KF (EKF) although the case (i.e. an example that the EKF outperforms PF) is seldom reported in the literature. Particularly, PF approaches show degraded performance for problems where the state noise is very small or zero. This is because particles become identical within a few iterations, which is so called particle impoverishment (PI) phenomenon; consequently, no matter how many particles are employed, we do not have particle diversity regardless of if the impoverished particle is close to the true state value or not. In this paper, we investigate this PI phenomenon, and show an example problem where a classical KF approach outperforms PF approaches in terms of mean squared error (MSE) criterion. Furthermore, we compare the processing speed of the EKF and PF approaches, and show the better speed performance of classical EKF approaches. Therefore, PF approaches may not be always better option than the classical EKF for nonlinear problems. Specifically, we show the outperforming result of unscented Kalman filter compared to that of PF approaches (which are shown in Fig. 7(c) for processing speed performance, and Fig. 6 for MSE performance in the paper).

A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics

Jaechan Lim 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.6

In this paper, we propose and assess the performance of “H infinity filter ( H∞ , HIF)” and “cost reference particle filter (CRPF)” in the problem of tracking a target based on the measurements of the range and the bearing of the target. HIF and CRPF have the common advantageous feature that we do not need to know the noise statistics of the problem in their applications. The performance of the extended Kalman filter (EKF) is also compared with that of the proposed filters, but the noise information is perfectly known for the applications of the EKF. Simulation results show that CRPF outperforms HIF, and is more robust because the tracking of HIF diverges sometimes, particularly when the target track is highly nonlinear. Interestingly, when the tracking of HIF diverges, the tracking of the EKF also tends to deviate significantly from the true track for the same target track. Therefore, CRPF is very effective and appropriate approach to the problems of highly nonlinear model, especially when the noise statistics are unknown. Nonetheless, HIF also can be applied to the problem of timevarying state estimation as the EKF, particularly for the case when the noise statistcs are unknown. This paper provides a good example of how to apply CRPF and HIF to the estimation of dynamically varying and nonlinearly modeled states with unknown noise statistics.

Frequency-selective and nonlinear channel estimation with unknown noise statistics

Jaechan Lim,Daehyoung Hong IEEE 2010 IEEE communications letters Vol.14 No.3

<P>We propose cost reference particle filter (CRPF) and extended game theory-based H<SUB>¿</SUB> filter approaches to the problem of estimating frequency-selective and slowly varying nonlinear channels with unknown noise statistics. The proposed approaches have a common advantageous feature that the noise information is not required in their applications. The simulation results justify that both approaches are effective, and that CRPF is more robust against highly nonlinear and drastically varying channels.</P>

Cost Reference Particle Filtering Approach to High-Bandwidth Tilt Estimation

Jaechan Lim,Daehyoung Hong IEEE 2010 IEEE transactions on industrial electronics Vol.57 No.11

<P>In this paper, cost reference particle filter (CRPF) approach in estimating 1-D “tilt” of a vehicle attitude is proposed. CRPF has a couple of advantageous features compared to standard particle filtering; particularly, it does not require noise statistics in its application. H_ filter (HF) has common features as that of CRPF. The extended HF (EHF) is employed, which uses the approximate linearization of the nonlinear measurement function as the extended Kalman filter is extended. The performance of both approaches is investigated and compared in this paper. Low-cost “accelerometer” and “gyroscope” sensors are cooperatively employed instead of inclinometer in measuring the tilt. Simulation results show that CRPF outperforms EHF in estimating the tilt due to its robustness against the nonlinearity of the measurement equation, whereas EHF outperforms CRPF in estimating the tilt rate whose measurement equation is linear. Notably, an efficient CRPF outperforms EHF in tracking the tilt with just ten particles.</P>

Inter-Carrier Interference Estimation in OFDM Systems With Unknown Noise Distributions

Jaechan Lim,Daehyoung Hong IEEE 2009 IEEE signal processing letters Vol.16 No.6

<P>There are a number of approaches to estimating carrier frequency offset (CFO) that causes inter-carrier interference (ICI) in orthogonal frequency division multiplexing (OFDM) systems: self-cancelation method, the extended Kalman filter (EKF), particle filter, H<SUB>infin</SUB> filter (HF), etc. In particular, the HF is of interest because prior statistical noise information is not necessarily required in its application. Cost reference particle filter (CRPF), newly developed in the particle filtering framework, has the same feature as HF; it also does not require the prior noise information of the state and the measurement equation. In this letter, we compare and analyze the performances of two similar methods. The simulation results show that CRPF outperforms HF, particularly when the bit energy to noise ratio of the measurement is low. Therefore, CRPF is very effective and robust, especially when the noise statistics are unknown with a low bit energy to noise ratio.</P>

Management of Neighbor Cell Lists and Physical Cell Identifiers in Self-Organizing Heterogeneous Networks

Jaechan Lim,홍대형 한국통신학회 2011 Journal of communications and networks Vol.13 No.4

In this paper, we propose self-organizing schemes for the initial configuration of the neighbor cell list (NCL), maintenance of the NCL, and physical cell identifier (PCI) allocation in heteroge-neous networks such as long term evolution systems where lower transmission power nodes are additionally deployed in macrocell networks. Accurate NCL maintenance is required for efficient PCI allocation and for avoiding handover delay and redundantly in-creased system overhead. Proposed self-organizing schemes for the initial NCL configuration and PCI allocation are based on evolved universal terrestrial radio access network NodeB (eNB) scanning that measures reference signal to interference and noise ratio and reference symbol received power, respectively, transmitted from adjacent eNBs. On the other hand, the maintenance of the NCL is managed by adding or removing cells based on periodic user equipment measurements. We provide performance analysis of the proposed schemes under various scenarios in the respects of NCL detection probability, NCL false alarm rate, handover delay area ratio, PCI conflict ratio, etc.

A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics

Lim, Jaechan The Korean Institute of Electrical Engineers 2013 Journal of Electrical Engineering & Technology Vol.8 No.6

In this paper, we propose and assess the performance of "H infinity filter ($H_{\infty}$, HIF)" and "cost reference particle filter (CRPF)" in the problem of tracking a target based on the measurements of the range and the bearing of the target. HIF and CRPF have the common advantageous feature that we do not need to know the noise statistics of the problem in their applications. The performance of the extended Kalman filter (EKF) is also compared with that of the proposed filters, but the noise information is perfectly known for the applications of the EKF. Simulation results show that CRPF outperforms HIF, and is more robust because the tracking of HIF diverges sometimes, particularly when the target track is highly nonlinear. Interestingly, when the tracking of HIF diverges, the tracking of the EKF also tends to deviate significantly from the true track for the same target track. Therefore, CRPF is very effective and appropriate approach to the problems of highly nonlinear model, especially when the noise statistics are unknown. Nonetheless, HIF also can be applied to the problem of timevarying state estimation as the EKF, particularly for the case when the noise statistcs are unknown. This paper provides a good example of how to apply CRPF and HIF to the estimation of dynamically varying and nonlinearly modeled states with unknown noise statistics.

Mobility and Handover Management for Heterogeneous Networks in LTE-Advanced

Lim, Jaechan,Hong, Daehyoung Springer-Verlag 2013 Wireless personal communications Vol.72 No.4

Performance Degradation Due to Particle Impoverishment in Particle Filtering

Jaechan Lim 대한전기학회 2014 Journal of Electrical Engineering & Technology Vol.9 No.6

Particle filtering (PF) has shown its outperforming results compared to that of classical Kalman filtering (KF), particularly for highly nonlinear problems. However, PF may not be universally superior to the extended KF (EKF) although the case (i.e. an example that the EKF outperforms PF) is seldom reported in the literature. Particularly, PF approaches show degraded performance for problems where the state noise is very small or zero. This is because particles become identical within a few iterations, which is so called particle impoverishment (PI) phenomenon; consequently, no matter how many particles are employed, we do not have particle diversity regardless of if the impoverished particle is close to the true state value or not. In this paper, we investigate this PI phenomenon, and show an example problem where a classical KF approach outperforms PF approaches in terms of mean squared error (MSE) criterion. Furthermore, we compare the processing speed of the EKF and PF approaches, and show the better speed performance of classical EKF approaches. Therefore, PF approaches may not be always better option than the classical EKF for nonlinear problems. Specifically, we show the outperforming result of unscented Kalman filter compared to that of PF approaches (which are shown in Fig. 7(c) for processing speed performance, and Fig. 6 for MSE performance in the paper).

Mixture Filtering Approaches to Blind Equalization Based on Estimation of Time-Varying and Multi-Path Channels

Lim, Jaechan The Korea Institute of Information and Commucation 2016 Journal of communications and networks Vol.18 No.1

In this paper, we propose a number of blind equalization approaches for time-varying andmulti-path channels. The approaches employ cost reference particle filter (CRPF) as the symbol estimator, and additionally employ either least mean squares algorithm, recursive least squares algorithm, or $H{\infty}$ filter (HF) as a channel estimator such that they are jointly employed for the strategy of "Rao-Blackwellization," or equally called "mixture filtering." The novel feature of the proposed approaches is that the blind equalization is performed based on direct channel estimation with unknown noise statistics of the received signals and channel state system while the channel is not directly estimated in the conventional method, and the noise information if known in similar Kalman mixture filtering approach. Simulation results show that the proposed approaches estimate the transmitted symbols and time-varying channel very effectively, and outperform the previously proposed approach which requires the noise information in its application.