Wireless 3D Localization Concept for Industrial Automation Based on a Bearings Only Extended Kalman Filter

Lipka, Melanie; Sippel, Erik; Hehn, Markus; Adametz, Julian; Vossiek, Martin; Dobrev, Yassen; Gulden, Peter

doi:10.23919/apmc.2018.8617483

Cited by 10 publications

(10 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The direction with the largest aperture (y-direction) is the most accurate, while the z-direction is the least accurate due to the use of planar arrays facing downwards. Comparing the results to a triangulation-based EKF in the same setup [13], we observe a similar behavior due to the aperture. Our holographic EKF is as such more accurate overall.…”

Section: B Resultssupporting

confidence: 61%

“…The classical localization approach relying only on phase information would be to evaluate the angle-of-arrival using a beamformer or imaging algorithms. Multiple radar base stations are deployed for AOA and some kind of multiangulation or an angle-based EKF [13] may be used for positioning. But this means that beamforming and therefore intricate searching is needed.…”

Section: E Interpretation and Advantagesmentioning

confidence: 99%

“…The conventional approach would be to determine the AOA with a beamformer, e.g., Delay and Sum or MUSIC. Specifically, the angle for every fixed station is calculated first, then the location is determined using the combined information from multiple base stations [13]. In landmark-based scenarios, a mobile station measures the angle to multiple fixed transponders [14] and computes its pose and position.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

2019

Self Cite

View full text Add to dashboard Cite

Radio-based indoor localization is currently a very vibrant scientific research field with many potential use cases. It offers high value for customers, for example, in the fields of robotics, logistics, and automation, or in context-aware IT services. Especially for autonomous systems, dynamic human-machine interaction, or augmented reality applications, precise localization coupled with a high update rate is a key. In this paper, we present a completely novel localization concept whereby received radio signal phase values that are fed into an extended Kalman filter (EKF) without any preprocessing are evaluated. Standard preprocessing steps, such as angle-of-arrival estimation, beamforming, and time-of-flight or time-differenceof-arrival estimations are not required with this approach. The innovative localization concept benefits from the high sensitivity of radio signals' phase to distance changes and the fast and straightforward recursive computation offered by the EKF. It completely forgoes the computational burden of other phase-based high-precision localization techniques, such as synthetic aperture methods. To verify the proposed method, we use an exemplary setup employing a 24 GHz frequency-modulated continuous-wave (CW) single-input multiple-output secondary radar with 250 MHz bandwidth. A high-precision six-axis robotic arm serves as a 3D positioning reference. The test setup emulates a realistic industrial indoor environment with significant multipath reflections. Despite the challenging conditions and the rather low bandwidth, the results show an outstanding localization 3D RMSE of around 1.7 cm. The proposed method can easily be applied to nearly any type of radio signal with CW carrier and is an attractive alternative to common multilateration and multiangulation localization approaches. We think it is a quantum leap in wireless locating, as it has the potential for precise, simple, and low-cost wireless localization even with standard narrowband communication signals. INDEX TERMS FMCW, radar, localization, extended Kalman filter, near field, indoor.

show abstract

Section: B Resultssupporting

confidence: 61%

Section: E Interpretation and Advantagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…One of the major tasks in this context is localize and keep track of hand-driven tools, autonomous robots, and various types of vehicles. This problem was investigated in the work performed by Lipka et al [26]. The authors proposed an approach that uses microwave-based localization system such as the 2.4 GHz modulated continuous wave radar and utilized the measurements of the Angle of Arrival (AoA) for 3D velocity and location estimation.…”

Section: Kalman Filter Extensions Of Kalman Filter and Particle Filtermentioning

confidence: 99%

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

Elbes

Alkhatib

Al-Fuqaha

et al. 2019

Pers Ubiquit Comput

View full text Add to dashboard Cite

Localization is an important primitive that is utilized in a number of important applications such as location-based mobile services, augmented reality, and autonomous mobile robotics. While the GPS technology is considered the de facto standard for outdoor localization, it is known to suffer from significant accuracy limitation in urban areas. In this work, we present a particle filter-based data fusion technique for localization in urban areas. The proposed localization technique provides more accurate location estimation results due to its ability to efficiently fuse together information collected from diverse sensor technologies. The novelty of our proposed approach stems from its ability to fuse data from diverse sources, namely, phase shift fingerprints collected from Low Power AM Radio (LPAM) towers and inertial measurement sensors. Our simulation results indicate that the proposed approach can achieve an accuracy of 0.5 m using a limited number of LPAM towers as low as 5. Also, the proposed approach requires the collection of a low number of LPAM phase shift fingerprints. Our simulations indicate that 30 fingerprints are enough to provide 0.5 m accuracy in a 100 × 100 m 2 deployment. Keywords Smart cities • Localization in urban areas • Data fusion • Fingerprinting • Channel state information 1 Introduction In recent years, several critical national infrastructures (such as the electric grid, intelligent transportation, water systems) Mohammed Elbes

show abstract

“…In this example, which will also be used subsequently, the transmitter can be located using both the signal's amplitude and the phase measured at spatially distributed antennas. In common solutions, the complex valued measurements are preprocessed, yielding real valued data, such as the received signal strength (RSS) [10], or the angle of arrival (AOA) [11], [12]. Then, usually maximum likelihood estimators (MLE) or least squares (LS) estimators are applied [13], [14] to estimate a system state.…”

Section: Introductionmentioning

confidence: 99%

An Iterative Extended Kalman Filter for Coherent Measurements of Incoherent Network Nodes in Positioning Systems

2020

Self Cite

View full text Add to dashboard Cite

Many positioning and tracking applications use spatially distributed sensor stations, each equipped with coherent measurement channels. The coherent data set of each node is incoherently measured to the data sets of all other nodes, avoiding expensive synchronization procedures between the stations. Usually, the measurements are evaluated by mapping the incoherently measured complex valued data on real valued data like angle of arrival or received signal strength. After this preprocessing step, recursive filters fuse the real valued data to estimate a system state. Unfortunately, even though the original measurements are performed in additive white Gaussian noise environments, the preprocessing step can result in correlated, noise shaped errors, whose variance is system state dependent. Hence, the additive white Gaussian noise assumption, which is commonly drawn in Kalman filters, is violated. Therefore, this paper proposes an iterative extended Kalman filter, which estimates the state of a nonlinear real valued system via a complex valued measurement model that consists of incoherent sensor stations, each with several coherent measurement channels. Since the proposed iterative extended Kalman filter is well suited for distributed positioning systems with several stations, a transmitter is localized in a simulation via two sensor stations, each measuring the received signal's amplitude and phase at four channels. To illustrate the advantages of the proposed algorithm, the direct measurement evaluation using the proposed algorithm is compared to a Kalman filter that evaluates the received signal strengths and angle of arrivals at each sensor station. Finally, a reflecting wall is incorporated into the simulation scenario to demonstrate the flexibility of the proposed Kalman filter.

show abstract

Wireless 3D Localization Concept for Industrial Automation Based on a Bearings Only Extended Kalman Filter

Cited by 10 publications

References 10 publications

An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

An Extended Kalman Filter for Direct, Real-Time, Phase-Based High Precision Indoor Localization

Using phase shift fingerprints and inertial measurements in support of precise localization in urban areas

An Iterative Extended Kalman Filter for Coherent Measurements of Incoherent Network Nodes in Positioning Systems

Contact Info

Product

Resources

About