Indoor reduction of noise in RF signal with Kalman Filter

Özcan, Hikmetcan; Şahin, Sühap; Mentesoglu, Mustafa; Pir, Fatih

doi:10.1109/siu.2015.7130260

Cited by 3 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wiener filter [15]- [17] Kalman filter [18], [19] Neural network [20]- [22] Evolutionary algorithm [23], [24] Non-adaptive Low-pass/high-pass/pass-band filter [25] Wavelet transform [26]- [28] Beam-forming [29] Singular value decomposition Independent component analysis [30] Principal component analysis [31], [32] Singular spectrum analysis [33]- [35] Other [36]- [38] Moreover, the ego-noise is highly non-stationary, as it typically depends on the characteristics of the movements being performed, e.g., speeds and accelerations. The noise produced by a drone has three main components, namely, the mechanical noise generated by the rotation of the motors, the noise generated by the propellers cutting through the air, and the noise of the airflow generated by the propellers themselves.…”

Section: Adaptivementioning

confidence: 99%

“…Classical adaptive filters do not need as much data as neural networks. Several works use Kalman [18], [19] or Wiener filters [15]- [17] successfully to denoise different types of signals. However, such filters have their limitations as well.…”

Section: B Software-based Noise Reductionmentioning

confidence: 99%

“…Then, as described in (19), the inverse hankelization of the outer product of a column vector and a row vector is equivalent to the full discrete convolution b over a over the full discrete convolution of a vector of b ones over a vector of a ones. Moreover, the denominator of this fraction can be approximated as b.…”

Section: ) Principal Components Extraction and Diagonal Averagingmentioning

confidence: 99%

See 2 more Smart Citations

Singular Spectrum Analysis for Source Separation in Drone-Based Audio Recording

et al. 2021

View full text Add to dashboard Cite

The usage of drones is increasingly spreading into new fields of application, ranging from agriculture to security. One of these new applications is sound recording in areas of difficult access. The challenge that arises when using drones for this purpose is that the sound of the recorded sources must be separated from the noise produced by the drone. The intensity of the noise emitted by the drone depends on several factors such as engine power, propeller rotation speed, or propeller type. Noise reduction is thus one of the greatest challenges for the next generations of unmanned aerial vehicles (UAVs) and unmanned aerial systems (UAS). Even though some advances have been made on that matter, drones still produce a considerable noise. In this article, we approach the problem of removing drone noise from singlechannel audio recordings using blind source separation (BSS) techniques, and in particular, the singular spectrum analysis algorithm (SSA). Furthermore, we propose an optimization of this algorithm with a spatial complexity of O(nt), which is significantly lower than the naive implementation which has a spatial complexity of O(tk 2) (where n is the number of sounds to be recovered, t is the signal length and k is the window size). The best value for each parameter (window length and number of components used to reconstruct the source) is selected by testing a wide range of values on different noise-sound ratios. Our system can greatly reduce the noise produced by the drone on said recordings. On average, after the recording has been processed by our method, the noise is reduced by 1.41 decibels.

show abstract

Section: Adaptivementioning

confidence: 99%

Section: B Software-based Noise Reductionmentioning

confidence: 99%

Section: ) Principal Components Extraction and Diagonal Averagingmentioning

confidence: 99%

See 1 more Smart Citation

Singular Spectrum Analysis for Source Separation in Drone-Based Audio Recording

et al. 2021

View full text Add to dashboard Cite

show abstract

“…At the same time, in an indoor positioning system, the robustness of the filtering algorithm is also crucial to accuracy. The Kalman filtering algorithm is widely used in navigation and positioning [ 15 , 16 , 17 ]. However, in indoor locations, the continuous dynamic process and slow clock change lead to time correlation between noise calendar elements; that is, the noise is colored [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

An Indoor Positioning Method Based on UWB and Visual Fusion

Peng

Xia

et al. 2022

Sensors

View full text Add to dashboard Cite

Continuous positioning and tracking of multi-pedestrian targets is a common concern for large indoor space security, emergency evacuation, location services, and other application areas. Among the sensors used for positioning, the ultra-wide band (UWB) is a critical way to achieve high-precision indoor positioning. However, due to the existence of indoor Non-Line-of-Sight (NLOS) error, a single positioning system can no longer meet the requirement for positioning accuracy. This research aimed to design a high-precision and stable fusion positioning system which is based on the UWB and vision. The method uses the Hungarian algorithm to match the identity of the UWB and vision localization results, and, after successful matching, the fusion localization is performed by the federated Kalman filtering algorithm. In addition, due to the presence of colored noise in indoor positioning data, this paper also proposes a Kalman filtering algorithm based on principal component analysis (PCA). The advantage of this new filtering algorithm is that it does not have to establish the dynamics model of the distribution hypothesis and requires less calculation. The PCA algorithm is firstly used to minimize the correlation of the observables, thus providing a more reasonable Kalman gain by energy estimation and the denoised data, which are substituted into Kalman prediction equations. Experimental results show that the average accuracy of the UWB and visual fusion method is 25.3% higher than that of the UWB. The proposed method can effectively suppress the influence of NLOS error on the positioning accuracy because of the high stability and continuity of visual positioning. Furthermore, compared with the traditional Kalman filtering, the mean square error of the new filtering algorithm is reduced by 31.8%. After using the PCA-Kalman filtering, the colored noise is reduced and the Kalman gain becomes more reasonable, facilitating accurate estimation of the state by the filter.

show abstract

Localization Using Saw Rfid Sensor Based on Kalman Algorithm

Han

Zhang

et al. 2019

2019 14th Symposium on Piezoelectrcity, Acoustic Waves and Device Applications (SPAWDA)

View full text Add to dashboard Cite

Indoor reduction of noise in RF signal with Kalman Filter

Cited by 3 publications

References 2 publications

Singular Spectrum Analysis for Source Separation in Drone-Based Audio Recording

Singular Spectrum Analysis for Source Separation in Drone-Based Audio Recording

An Indoor Positioning Method Based on UWB and Visual Fusion

Localization Using Saw Rfid Sensor Based on Kalman Algorithm

Contact Info

Product

Resources

About