A comparison between Unscented Kalman Filtering and particle filtering for RSSI-based tracking

Lee, Kung-Chung; Oka, A.; Pollakis, Emmanuel; Lampe, Lutz

doi:10.1109/wpnc.2010.5650817

Cited by 30 publications

(17 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Tracking functionality has been developed on Cricket [Smith et al 2004]. There are also others works like Paul and Wan [2008], Lee et al [2010], and Caceres et al [2009]. As pointed out in Smith et al [2004], EKF may run to bad state in some cases.…”

Section: Related Workmentioning

confidence: 99%

ColLoc

Zhu

Huang

Meng

et al. 2014

ACM Trans. Embed. Comput. Syst.

View full text Add to dashboard Cite

Localization in wireless sensor networks is an important functionality that is required for tracking personnel and assets in industrial environments, especially for emergency response. Current commercial localization systems such as GPS suffer from the limitations of either high cost or low availability in many situations (e.g., indoor environments that exclude direct line-of-sight signal reception). The development of industrial wireless sensor networks such as WirelessHART provides an alternative. In this article, we present the design and implementation of ColLoc: a collaborative location and tracking system on WirelessHART as an industrially viable solution. This solution is built upon several technological advances. First, ColLoc adds the roaming functionality to WirelessHART and thus provides a means for keeping mobile WirelessHART devices connected to the network. Second, ColLoc employs a collaborative framework to integrate different types of distance measurements into the location estimation algorithm by weighing them according to their precision levels. ColLoc adopts several novel techniques to improve distance estimation accuracy and decreases the RSSI presurvey cost. These techniques include introducing distance error range constraints to the measurements, judiciously selecting the initial point in location estimation and online updating the signal propagation models in the anchor nodes, integrating Extended Kalman Filter (EKF) with trilateration to track moving objects. Our implementation of ColLoc can be applied to any WirelessHART-conforming network because no modification is needed on the WirelessHART field devices. We have implemented a complete ColLoc system to validate both the design and the effectiveness of our localization algorithm. Our experiments show that the mobile device never drops out of the WirelessHART network while moving around; with the help of even one dependable anchor, using RSSI can yield at least 75% of distance errors below 5 meters, which is quite acceptable for many typical industrial automation applications.

show abstract

Section: Related Workmentioning

confidence: 99%

ColLoc

Zhu

Huang

Meng

et al. 2014

ACM Trans. Embed. Comput. Syst.

View full text Add to dashboard Cite

show abstract

“…For instance, in [9], a non-Gaussian trimodal process noise The authors are with the Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada (e-mail: leila.pishdad@mail.mcgill.ca; fabrice.labeau@mcgill.ca) distribution is used, corresponding to the three different states of motion: constant velocity, accelerating, and decelerating. Additionally, mutipath fading effects could result in multimodal non-Gaussian measurement noise distributions [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

Analytic Minimum Mean-Square Error Bounds in Linear Dynamic Systems With Gaussian Mixture Noise Statistics

Pishdad

Labeau

2020

IEEE Access

View full text Add to dashboard Cite

Using state-space representation, mobile object positioning problems can be described as dynamic systems, with the state representing the unknown location and the observations being the information gathered from the location sensors. For linear dynamic systems with Gaussian noise, the Kalman filter provides the Minimum Mean-Square Error (MMSE) state estimation by tracking the posterior. Hence, by approximating non-Gaussian noise distributions with Gaussian Mixtures (GM), a bank of Kalman filters or Gaussian Sum Filter (GSF), can provide the MMSE state estimation. However, the MMSE itself is not analytically tractable. Moreover, the general analytic bounds proposed in the literature are not tractable for GM noise statistics. Hence, in this work, we evaluate the MMSE of linear dynamic systems with GM noise statistics and propose its analytic lower and upper bounds. We provide two analytic upper bounds which are the Mean-Square Errors (MSE) of implementable filters, and we show that based on the shape of the GM noise distributions, the tighter upper bound can be selected. We also show that for highly multimodal GM noise distributions, the bounds and the MMSE converge. Simulation results support the validity of the proposed bounds and their behavior in limits. Index Terms-Minimum Mean-Square Error estimator, analytic bounds on Minimum Mean-Square Error, Gaussian mixture noise, online Bayesian filtering, Gaussian sum filter arXiv:1506.07603v1 [cs.SY]

show abstract

“…The inherent nonlinearity of the problem suggests that other Bayesian techniques should be investigated [14], [9] though. This is specially true if the system is to be extended to cope with cooperative multi-robot estimation or integrated with decision-making modules, and it is well known that the aforementioned filters are not well suited for these scenarios.…”

Section: Introductionmentioning

confidence: 99%

Indoor wireless sensor localization using mobile robot and RSSI

Carvalho

Santos

Iabrudi

et al. 2012

2012 IEEE 9th International Conference on Mobile Ad-Hoc and Sensor Systems (MASS 2012)

View full text Add to dashboard Cite

Reliable sensor node localization is a critical and difficult task in a large number of wireless sensor networks applications. Received signal strength indication (RSSI) measurements are a simple and inexpensive way to localize mobile robots, but they suffer from large errors due to noise, occlusions, and multi-path specially in indoor environments. Kalman filters and their variations are widely adopted in the community, although the inherent nonlinearity of the problem suggests the use of more general Bayesian techniques. In this paper, we take on the range-only probabilistic localization problem by a mobile robot that depends on RSSI measurements its only information of a sensor node's location. We propose a method based on a general Probabilistic Graphical Model and our main contribution is the definition of a reasonable, albeit simple, probabilistic RSSI likelihood model connecting distance to the observed RSSI values. Sensor position estimation is performed by integrating a standard robot's position estimator to our Bayesian estimator. Preliminary experimental evaluation indicates that our methodology leads to low error in sensor node's position estimation. Experiments were performed both by simulation, in the Player/Stage platform, and in a real world scenario, using a 802.11g sensor node and a Pioneer 3AT mobile robot.

show abstract

A comparison between Unscented Kalman Filtering and particle filtering for RSSI-based tracking

Cited by 30 publications

References 13 publications

ColLoc

ColLoc

Analytic Minimum Mean-Square Error Bounds in Linear Dynamic Systems With Gaussian Mixture Noise Statistics

Indoor wireless sensor localization using mobile robot and RSSI

Contact Info

Product

Resources

About