Particle filtering with applications in networked systems: a survey

Li, Wenshuo; Wang, Zidong; Yuan, Yuan

doi:10.1007/s40747-016-0028-2

Cited by 25 publications

(19 citation statements)

References 175 publications

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“…The developed algorithms in this paper can be applied to a wide range of areas where the systems under investigation are nonlinear and/or non-Gaussian, and the state vectors are subject to soft constraints. Examples may include ground vehicle tracking, air traffic monitoring, maritime navigation, and the other areas beyond target tracking such as networked systems [36] and source term estimation [37]. Finally, we point out that, for those applications where a continuous-time dynamic system is discretized, the information provided by soft state constraints can be applied in a higher sampling rate than the sensor measurements, and hence having a potential to further increase the filtering performance.…”

Section: Discussionmentioning

confidence: 99%

Particle Filtering With Soft State Constraints for Target Tracking

Liu

Chen

2019

IEEE Trans. Aerosp. Electron. Syst.

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In practice, additional knowledge about the target to be tracked, other than its fundamental dynamics, can often be modeled as a set of soft constraints and utilized in a filtering process to improve the tracking performance. This paper develops a general approach to the modeling of soft inequality constraints, and investigates particle filtering (PF) with soft state constraints for target tracking. We develop two PF algorithms with soft inequality constraints, i.e., a sequential-importance-resampling particle filter and an auxiliary sampling mechanism. The latter probabilistically selects the candidate particles from the soft inequality constraints of the state variables so that they are more likely to comply with the soft constraints. The performances of the proposed algorithms are evaluated using Monte Carlo simulations in a target tracking scenario.

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Section: Discussionmentioning

confidence: 99%

Particle Filtering With Soft State Constraints for Target Tracking

Liu

Chen

2019

IEEE Trans. Aerosp. Electron. Syst.

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“…At each iteration, the sampling step is employed to reject some particles, increasing the significance of regions with advanced posterior probability. The particle algorithm is comprise of the following steps [97], [113]- [117]:…”

Section: ) Particle Filtermentioning

confidence: 99%

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

2020

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Autonomous mobile robots are becoming more prominent in recent time because of their relevance and applications to the world today. Their ability to navigate in an environment without a need for physical or electro-mechanical guidance devices has made it more promising and useful. The use of autonomous mobile robots is emerging in different sectors such as companies, industries, hospital, institutions, agriculture and homes to improve services and daily activities. Due to technology advancement, the demand for mobile robot has increased due to the task they perform and services they render such as carrying heavy objects, monitoring, search and rescue missions, etc. Various studies have been carried out by researchers on the importance of mobile robot, its applications and challenges. This survey paper unravels the current literatures, the challenges mobile robot is being faced with. A comprehensive study on devices/sensors and prevalent sensor fusion techniques developed for tackling issues like localization, estimation and navigation in mobile robot are presented as well in which they are organised according to relevance, strengths and weaknesses. The study therefore gives good direction for further investigation on developing methods to deal with the discrepancies faced with autonomous mobile robot.

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“…Since the network environment is a nonlinear system, the noise distribution is non-gaussian. Therefore, particle filter algorithm can eliminate noise interference compared with other filtering algorithms, accurately analyze the crosscorrelation model and have more accurate detection effect [18]. This section mainly takes the frequency domain signal in 3.2 as the observed value, and uses the particle filter algorithm for estimation.…”

Section: B the Prediction Error Detection Approachmentioning

confidence: 99%

The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN

Liu

Feng

et al. 2020

IEEE Access

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Named Data Networking (NDN) is one of the main research projects of the information center network (ICN), and its efficient forwarding mechanism attaches the attention of researchers. Like other networks, NDN also faces the threat of cyber attacks. With the assistance of the colluding server, the Collusive Interest Flooding Attacks (CIFA) can use the defects of the NDN's internal forwarding mechanism to send malicious interest packets in the form of pulses. It affects the normal requests of legitimate users and reduces the quality of NDN network services in this way. By analyzing the characteristics of network traffic and CIFA model, a new CIFA detecting method based on the prediction error between particle filter and one-step prediction algorithm is proposed. This scheme samples the network traffic and judges whether the network is under attack by comparing the normalized error value of the one-step prediction and the estimate of the particle filter. Experimental analysis shows that the detection scheme in this paper has higher detection rate than the existing detection schemes.

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Particle filtering with applications in networked systems: a survey

Cited by 25 publications

References 175 publications

Particle Filtering With Soft State Constraints for Target Tracking

Particle Filtering With Soft State Constraints for Target Tracking

A Review on Challenges of Autonomous Mobile Robot and Sensor Fusion Methods

The Detection Method of Collusive Interest Flooding Attacks Based on Prediction Error in NDN

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