Blind receiver for OFDM systems via sequential Monte Carlo in factor graphs

Estimation and detection algorithms for orthogonal frequency division multiplexing (OFDM) systems can be de-veloped based on the sum-product algorithms, which operate by message passing in factor graphs. In this paper, we apply the sampling method (Monte Carlo) to factor graphs, and then the integrals in the sum-product algorithm can be approximated by sums, which results in complexity reduction. The blind receiver for OFDM systems can be derived via Sequential Monte Carlo (SMC) in factor graphs, the previous SMC blind receiver can be regarded as the special case of the sum-product algorithms using sampling methods. The previous SMC blind receiver for OFDM systems needs generating samples of the channel vector assuming the channel has an a priori Gaussian distribution. In the newly-built blind receiver, we generate samples of the virtual-pilots instead of the channel vector, with channel vector which can be easily computed based on virtual-pilots. As the size of the vir-tual-pilots space is much smaller than the channel vector space, only small number of samples are necessary, with the blind de-tection being much simpler. Furthermore, only one pilot tone is needed to resolve phase ambiguity and differential encoding is not used anymore. Finally, the results of computer simulations demonstrate that the proposal can perform well while providing sig-nificant complexity reduction.

Sequential Monte Carlo Method Toward Online RUL Assessment with Applications

Online assessment of remaining useful life(RUL) of a system or device has been widely studied for performance reliability, production safety, system conditional maintenance, and decision in remanufacturing engineering. However,there is no consistency framework to solve the RUL recursive estimation for the complex degenerate systems/device.In this paper, state space model(SSM) with Bayesian online estimation expounded from Markov chain Monte Carlo(MCMC) to Sequential Monte Carlo(SMC) algorithm is presented in order to derive the optimal Bayesian estimation.In the context of nonlinear & non-Gaussian dynamic systems, SMC(also named particle filter, PF) is quite capable of performing filtering and RUL assessment recursively. The underlying deterioration of a system/device is seen as a stochastic process with continuous, nonreversible degrading. The state of the deterioration tendency is filtered and predicted with updating observations through the SMC procedure. The corresponding remaining useful life of the system/device is estimated based on the state degradation and a predefined threshold of the failure with two-sided criterion. The paper presents an application on a milling machine for cutter tool RUL assessment by applying the above proposed methodology. The example shows the promising results and the effectiveness of SSM and SMC online assessment of RUL.

SSO Risk Evaluation for a Grid-connected PMSG-based Wind Farm Based on Sequential Monte Carlo Simulation

In a grid-connected wind farm based on permanent magnet synchronous generators(PMSGs),the wind speed and the number of operating PMSGs are the two most important influencing factors along with the stochastic nature of sub-synchronous oscillation(SSO)from the point view of the farm.This paper proposes a method of unstable SSO risk evaluation for grid-connected PMSG-based wind farms based on the sequential Monte Carlo simulation(SMCS).The determination of critical wind speed(CWS)of SSO and the sequential simulation strategy of wind speed states and PMSG states in a wind farm at the same wind speed(S-WF),as well as in a wind farm at different wind speeds(D-WF),are studied.Five indices evaluating the expectation,duration,frequency and energy loss of SsO risk are proposed.Moreover,a strategy to reduce SsO risk by adjusting the cut-in wind speed is discussed.The effectiveness of the discussed issues in this paper are proved by the case studies of a 750-PMSG wind farm based on the actual wind speed data collected.

An efficient measurement-driven sequential Monte Carlo multi-Bernoulli filter for multi-target filtering

We propose an efficient measurement-driven sequential Monte Carlo multi-Bernoulli(SMC-MB) filter for multi-target filtering in the presence of clutter and missing detection. The survival and birth measurements are distinguished from the original measurements using the gating technique. Then the survival measurements are used to update both survival and birth targets, and the birth measurements are used to update only the birth targets.Since most clutter measurements do not participate in the update step, the computing time is reduced significantly.Simulation results demonstrate that the proposed approach improves the real-time performance without degradation of filtering performance.

Effective implementation and improvement of fast labeled multi-Bernoulli filter

Effective implementation of the fast labeled multi-Bernoulli(FLMB)filter is addressed for target tracking with interval measurements.Firstly,a sequential Monte Carlo(SMC)implementation of the FLMB filter,SMC-FLMB filter,is derived based on generalized likelihood function weighting.Then,a box particle(BP)implementation of the FLMB filter,BP-FLMB filter,is developed,with a computational complexity reduction of the SMC-FLMB filter.Finally,an improved version of the BP-FLMB filter,improved BP-FLMB(IBP-FLMB)filter,is proposed,improving its estimation accuracy and real-time performance under the conditions of low detection probability and high clutter.Simulation results show that the BP-FLMB filter has a great improvement of the real-time performance than the SMC-FLMB filter,with similar tracking performance.Compared with the BP-FLMB filter,the IBP-FLMB filter has better estimation performance and real-time performance under the conditions of low detection probability and high clutter.

Joint state and parameter estimation in particle filtering and stochastic optimization

In this paper, an adaptive estimation algorithm is proposed for non-linear dynamic systems with unknown static parameters based on combination of particle filtering and Simultaneous Perturbation Stochastic Approxi- mation （SPSA） technique. The estimations of parameters are obtained by maximum-likelihood estimation and sampling within particle filtering framework, and the SPSA is used for stochastic optimization and to approximate the gradient of the cost function. The proposed algorithm achieves combined estimation of dynamic state and static parameters of nonlinear systems. Simulation result demonstrates the feasibilitv and efficiency of the proposed algorithm

A Novel Situation Assessment Method for Network Survivability

Survivability has emerged as a new phase for the development of network security technique, and quantifying survivability for network system helps to evaluate it exactly for the system in different environments. In this paper, we adopt a stochastic method called sequential Monte Carlo and try to reflect dynamic evolvement process of network survivability situation from several time sequences. The experiment results show that this method has the features of quantitative description, real-time calculation and dynamic tracking, and it is a good situation assessment solution for network survivability.

SMC-PHD based multi-target track-before-detect with nonstandard point observations model

Detection and tracking of multi-target with unknown and varying number is a challenging issue, especially under the condition of low signal-to-noise ratio(SNR). A modified multi-target track-before-detect(TBD) method was proposed to tackle this issue using a nonstandard point observation model. The method was developed from sequential Monte Carlo(SMC)-based probability hypothesis density(PHD) filter, and it was implemented by modifying the original calculation in update weights of the particles and by adopting an adaptive particle sampling strategy. To efficiently execute the SMC-PHD based TBD method, a fast implementation approach was also presented by partitioning the particles into multiple subsets according to their position coordinates in 2D resolution cells of the sensor. Simulation results show the effectiveness of the proposed method for time-varying multi-target tracking using raw observation data.

Multiple vehicle signals separation based on particle filtering in wireless sensor network

A novel statistical method based on particle filtering is presented for multiple vehicle acoustic signals separation problem in wireless sensor network. The particle filtering method is able to deal with non-Gaussian and nonlinear models and non-stationary sources. Using some instantaneously mixed observations of several real-world vehicle acoustic signals, the proposed statistical method is compared with a conventional non-stationary Blind Source Separation algorithm and attractive simulation results are achieved. Moreover, considering the natural convenience to transmit particles between sensor nodes, the algorithm based on particle filtering is believed to have potential to enable the task of multiple vehicles recognition collaboratively performed by sensor nodes in distributed wireless sensor network.

A Sensor-Service Collaboration Approach for Target Tracking in Wireless Camera Networks

Mobile target tracking is a necessary function of some emerging application domains, such as virtual reality, smart home and intelligent healthcare. However, existing portable devices for target tracking are resource intensive and high-cost. Camera tracking is an effective location tracking way for those emerging applications which can reuse the existing ubiquitous video monitoring system. This paper proposes a dynamic community-based camera collaboration(D3C) framework for target location and tracking. The contributions of D3C mainly include that(1) nonlinear perspective projection model is selected as the camera sensing model and sequential Monte Carlo is employed to predict the target location;(2) a dynamic collaboration scheme is proposed, it is based on the local community-detection theory deriving from social network analysis. The performance of proposed approach is validated by both synthetic datasets and real-world application. The experiment results show that D3C meets the versatility, real-time and fault tolerance requirements of target tracking applications.

Maneuvering target track-before-detect via multiple-model Bernoulli particle filter

Target tracking using non-threshold raw data with low signal-to-noise ratio is a very difficult task, and the model uncertainty introduced by target's maneuver makes it even more challenging. In this work, a multiple-model based method was proposed to tackle such issues. The method was developed in the framework of Bernoulli filter by integrating the model probability parameter and implemented via sequential Monte Carlo(particle) technique. Target detection was accomplished through the estimation of target's existence probability, and the estimate of target state was obtained by combining the outputs of modeldependent filtering. The simulation results show that the proposed method performs better than the TBD method implemented by the conventional multiple-model particle filter.

Particle filter for joint frequency offset and channel estimation in MIMO-OFDM systems

A particle filter is proposed to perform joint estimation of the carrier frequency offset （CFO） and the channel in multiple-input multiple-output orthogonal frequency division multiplexing （MIMO-OFDM） wireless communication systems. It marginalizes out the channel parameters from the sampling space in sequential importance sampling （SIS）, and propagates them with the Kalman filter. Then the importance weights of the CFO particles are evaluated according to the imaginary part of the error between measurement and estimation. The varieties of particles are maintained by sequential importance resampling （SIR）. Simulation results demonstrate this algorithm can estimate the CFO and the channel parameters with high accuracy. At the same time, some robustness is kept when the channel model has small variations.

A Particle Filter of Blind Equalization and Multiuser Detection in Asynchronous DS/CDMA Systems

The particle filter （PF） is proposed to be the asynchronous direct-sequence code-division multiple-access （DS/CDMA） multiuser detector without knowing the channel state information. The PF performs symbol detection according to the joint posterior density probability of simulated particles including relative delays, fading gains and symbols via sequential importance sample and resample. A simplified scheme is also proposed by separating the indepent relative delays and fading with symbols. These parameters are modeled as the extended aggressive processes and estimated by the Kalman filter, so as to provide their arbitrary distribution for symbol detection. Simulation results show that the bit error rate of the PF is less than conventional detectors. Moreover, the complexity of PF is moderate comparable to other nonlinear suboptimal approaches.

BLIND CHANNEL AND SYMBOL JOINT ESTIMATION IN COOPERATIVE MIMO FOR WIRELESS SENSOR NETWORK

In this paper,application of Sequential Quasi Monte Carlo(SQMC)to blind channel andsymbol joint estimation in cooperative Multiple-Input Multiple-Output(MIMO)system is proposed,which does not need to transmit training symbol and can save the power and channel bandwidth.Additionally,an improved version of SQMC algorithm by taking advantage of current received signal isdiscussed.Simulation results show that the SQMC method outperforms the Sequential Monte Carlo(SMC)methods,and the incorporation of current received signal improves the performance of theSQMC obviously.

Estimating the instantaneous reproduction number(R_(t))by using particle filter

Background:Monitoring the transmission of coronavirus disease 2019(COVID-19)requires accurate estimation of the effective reproduction number(Rt).However,existing methods for calculating Rt may yield biased estimates if important real-world factors,such as delays in confirmation,pre-symptomatic transmissions,or imperfect data observation,are not considered.Method:To include real-world factors,we expanded the susceptible-exposed-infectiousrecovered(SEIR)model by incorporating pre-symptomatic(P)and asymptomatic(A)states,creating the SEPIAR model.By utilizing both stochastic and deterministic versions of the model,and incorporating predetermined time series of Rt,we generated simulated datasets that simulate real-world challenges in estimating Rt.We then compared the performance of our proposed particle filtering method for estimating Rt with the existing EpiEstim approach based on renewal equations.Results:The particle filtering method accurately estimated Rt even in the presence of data with delays,pre-symptomatic transmission,and imperfect observation.When evaluating via the root mean square error(RMSE)metric,the performance of the particle filtering method was better in general and was comparable to the EpiEstim approach if perfectly deconvolved infection time series were provided,and substantially better when Rt exhibited short-term fluctuations and the data was right truncated.Conclusions:The SEPIAR model,in conjunction with the particle filtering method,offers a reliable tool for predicting the transmission trend of COVID-19 and assessing the impact of intervention strategies.This approach enables enhanced monitoring of COVID-19 transmission and can inform public health policies aimed at controlling the spread of the disease.

Reliability Assessment of Integrated Electricity and Natural Gas Transmission Systems in Presence of Power-to-gas and Combined Heat and Power Technologies

Assessing the reliability of integrated electricity and gas systems has become an important issue due to the strong dependence of these energy networks through the power-to-gas(P2G)and combined heat and power(CHP)technologies.The current work,initially,presents a detailed energy flow model for the integrated power and natural gas system in light of the P2G and CHP technologies.Considering the simultaneous load flow of networks,a contingency analysis procedure is proposed,and reliability is assessed through sequential Monte Carlo simulations.The current study examines the effect of independent and dependent operation of energy networks on the reliability of the systems.In particular,the effect of employing both P2G and CHP technologies on reliability criteria is evaluated.In addition,a series of sensitivity analysis are performed on the size and site of these technologies to investigate their effects on system reliability.The proposed method is implemented on an integrated IEEE 24-bus electrical power system and 20-node Belgian natural gas system.The simulation procedure certifies the proposed method for reliability assessment is practical and applicable.In addition,the results prove connection between energy networks through P2G and CHP technologies can improve reliability of networks if the site and size of technologies are properly determined.

A measurement-driven adaptive probability hypothesis density filter for multitarget tracking

This paper studies the dynamic estimation problem for multitarget tracking. A novel gat- ing strategy that is based on the measurement likelihood of the target state space is proposed to improve the overall effectiveness of the probability hypothesis density （PHD） filter. Firstly, a measurement-driven mechanism based on this gating technique is designed to classify the measure- ments. In this mechanism, only the measurements for the existing targets are considered in the update step of the existing targets while the measurements of newborn targets are used for exploring newborn targets. Secondly, the gating strategy enables the development of a heuristic state estima- tion algorithm when sequential Monte Carlo （SMC） implementation of the PHD filter is investi- gated, where the measurements are used to drive the particle clustering within the space gate. The resulting PHD filter can achieve a more robust and accurate estimation of the existing targets by reducing the interference from clutter. Moreover, the target birth intensity can be adaptive to detect newborn targets, which is in accordance with the birth measurements. Simulation results demonstrate the computational efficiency and tracking performance of the proposed algorithm.

Reliability Assessment of a Cyber Physical Microgrid System in Island Mode

As cyber physical systems,microgrids(MGs),with distributed generations and energy management systems,can improve the reliability of power supply for customers in MGs.To quantify the reliability of isolated MGs,a cyber-physical assessment model is proposed.In this model,the circuit breakers and distributed energy resources are treated as the coupling elements between the cyber system and physical system,where the circuit breakers are uniquely modelled by using the Markov process theory based on the indirect interdependencies between cyber physical elements.For the cyber system,the reliability model of communication networks is formulated based on the link connectivity evaluation method.For the physical system,a system state generating method is presented to account for the optimal operation strategy,which considers the influence of the optimization strategy on the failure consequence analysis.With the proposed cyber and physical reliability models,the sequential Monte Carlo(SMC)simulation method is adopted to assess the reliability of islanded MGs.Simulations are carried out on a test system,and results verify the feasibility and effectiveness of proposed assessment method.Furthermore,one application of the proposed method is on the parameter setting of the cyber system,in terms of enhancing MGs reliability.

Particle-Based Iterative Blind Receiver for Orthogonal Frequency-Division Multiplexing with Frequency Offset

Orthogonal frequency-division multiplexing （OFDM） systems are sensitive to carrier frequency offset （CFO） which introduces intercarder interference and significantly degrades system performance. This paper describes an iterative blind receiver consisting of a sequential Monte Carlo detector, a CFO estimator, and a compensator to reduce intercarrier interference. The framework is of low complexity due to the separation of tasks in a joint detection problem. In addition, the CFO estimator utilizes soft output of the sequential Monte Carlo detector, which reduces the information loss caused by hard decisions and can obtain the CFO estimate in only one OFDM symbol. Simulation results demonstrate the effectiveness of the algorithm.

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.