scholarly journals A dynamic adaptive AHRS algorithm for UAV based on SVDCKF

Author(s):  
Yue Yang ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Xuhang Liu ◽  
Yicong Guo

Aiming at the attitude solution accuracy and robustness for small UAVs in complex flight conditions, this paper proposes a dynamic adaptive attitude and heading systems(AHRS) estimator with singular value decomposition Cubature Kalman filter(SVDCKF). Considering the problem of random bias for the low-cost attitude sensor, this paper designs a method that the sensor random bias is used as the state vector to eliminate the effect of the sensor random bias. Due to the non-linearity of small UAVs AHRS model and the non-positive definite phenomenon of the covariance matrix, a nonlinear AHRS filter combined with the Cubature Kalman filter and singular value decomposition is designed to improve the attitude solution accuracy. In addition, when the UAV flies in the different flight conditions, the three-axis acceleration of the attitude sensor will affect the attitude solution. Thus, a dynamic adaptive factor based on adaptive filtering is used to adjust continuously the acceleration noise variance to improve the robustness of the AHRS. The experimental results show that the method and algorithm proposed not only improve the attitude solution accuracy, and satisfy the flight requirements of small UAVs, but also eliminate the influence of the attitude sensor random bias and three-axis acceleration for the attitude solution to improve the proposed algorithm robustness and anti-interference.

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Zhao ◽  
Huiguang Li ◽  
Liying Zou ◽  
Wenjuan Huang

The paper presents a nonlinear unknown input observer (NUIO) based on singular value decomposition aided reduced dimension Cubature Kalman filter (SVDRDCKF) for a special class of nonlinear systems, the nonlinearity of which is only caused by part of its states. Firstly, the algorithm of general NUIO is discussed and the unknown input observer based on singular value decomposition aided Cubature Kalman filter (SVDCKF) given. Then a special nonlinear system model with unknown input is introduced. Based on the proposed model and the corresponding NUIO, the equivalent integral form with partial sampling and all sampling of the state vector in Cubature Kalman filter is analyzed. Finally the nonlinear unknown input observer based on singular value decomposition aided reduced dimension Cubature Kalman filter is obtained. Simulation results show that the proposed algorithm can meet the requirements of the system and is more important to increase the calculating efficiency a lot, although it has a decline in the accuracy of the filter.


2014 ◽  
Vol 68 (3) ◽  
pp. 549-562 ◽  
Author(s):  
Qiuzhao Zhang ◽  
Xiaolin Meng ◽  
Shubi Zhang ◽  
Yunjia Wang

A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system.


Author(s):  
Rashmi Nadubeediramesh ◽  
Aryya Gangopadhyay

Incremental document clustering is important in many applications, but particularly so in healthcare contexts where text data is found in abundance, ranging from published research in journals to day-to-day healthcare data such as discharge summaries and nursing notes. In such dynamic environments new documents are constantly added to the set of documents that have been used in the initial cluster formation. Hence it is important to be able to incrementally update the clusters at a low computational cost as new documents are added. In this paper the authors describe a novel, low cost approach for incremental document clustering. Their method is based on conducting singular value decomposition (SVD) incrementally. They dynamically fold in new documents into the existing term-document space and dynamically assign these new documents into pre-defined clusters based on intra-cluster similarity. This saves the cost of re-computing SVD on the entire document set every time updates occur. The authors also provide a way to retrieve documents based on different window sizes with high scalability and good clustering accuracy. They have tested their proposed method experimentally with 960 medical abstracts retrieved from the PubMed medical library. The authors’ incremental method is compared with the default situation where complete re-computation of SVD is done when new documents are added to the initial set of documents. The results show minor decreases in the quality of the cluster formation but much larger gains in computational throughput.


2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Dazhang You ◽  
Pan Liu ◽  
Wei Shang ◽  
Yepeng Zhang ◽  
Yawei Kang ◽  
...  

An improved UKF (Unscented Kalman Filter) algorithm is proposed to solve the problem of radar azimuth mutation. Since the radar azimuth angle will restart to count after each revolution of the radar, and when the aircraft just passes the abrupt angle change, the radar observation measurement will have a sudden change, which has serious consequences and is solved by the proposed novel UKF based on SVD. In order to improve the tracking accuracy and stability of the radar tracking system further, the SVD-MUKF (Singular Value Decomposition-based Memory Unscented Kalman Filter) based on multiple memory fading is constructed. Furthermore, several simulation results show that the SVD-MUKF algorithm proposed in this paper is better than the SVD-UKF (Singular Value Decomposition of Unscented Kalman Filter) algorithm and classical UKF algorithm in accuracy and stability. Last but not the least, the SVD-MUKF can achieve stable tracking of targets even in the case of angle mutation.


2013 ◽  
Vol 31 (1) ◽  
pp. 75
Author(s):  
Oscar F. Mojica ◽  
Milton J. Porsani ◽  
Michelangelo G. da Silva

This study investigates the adaptive filtering approach based on the Singular Value Decomposition (SVD) method to improve velocity analysis and ground-roll attenuation. The SVD filtering is an adaptive multichannel filtering method where each filtered seismic trace keeps a degree of coherence with the immediate neighboring traces. Before applying the adaptive filtering, in order to flatten the primary reflections the seismogram is corrected using the Normal Move Out (NMO) method. The SVD filtering helps to strengthen the spatial coherence of reflectors. It works as multichannel and can be applied by selecting a set of seismic traces taken from around the target trace. Thus traces from different shots can be represented by a five-point areal operator, which we call five-point cross operator. In this paper we run this operator along the coverage map of the seismic survey. At each operator position, the filtered trace (center of the operator) is obtained by taking the firstor adding the first eigenimages. Thereby we enhance the coherence corresponding to the primary reflections in detriment of the remaining events (ground-roll, multiples, and other non-correlated events) remained in the other eigenimages. The method was tested on a seismic line of the Tacutu, Brazil. The obtained results show the velocity spectra with better definition, as well as better post-stacked section exhibiting better continuity of seismic reflections and lower noise, compared with the raw processing results (without SVD filtering). RESUMO. No presente trabalho aplicamos o método de filtragem adaptativa baseada no método SVD (Singular Value Decomposition) para a melhoria da análise de velocidades e atenuação do ruído coerente associado à fonte sísmica (ground-roll). A filtragem SVD pode ser vista como um método de filtragem adaptativa multicanal onde cada traço filtrado guarda certo grau de coerência com os traços imediatamente vizinhos. Antes da aplicação do método é feita a correção de decalagem normal (normal move out – NMO) dos sismogramas, tendo como finalidade deixar as reflexões de interesse aproximadamente horizontais. A filtragem SVD permite reforçar a coerência espacial dos refletores. Ela trabalha na forma multicanal e pode ser aplicada seguindo um procedimento padrão que consiste na seleção de um conjunto de traços tomados ao redor do traço-alvo da filtragem. Desta forma traços de diferentes tiros podem ser utilizados na filtragem SVD. A coleta de traços pertencentes a diferentes tiros, no mapa de cobertura, pode ser representada por um operador espacial de cinco pontos que denominamos de operador em cruz. No presente trabalho utilizamos um operador de cinco pontos que opera sobre todos os traços do mapa de cobertura do levantamento sísmico. A cada posição do operador, o traço filtrado (centro do operador) é obtido tomando-se a primeira ou somando-se a(s) primeira(s) autoimagem(ns) do painel de 5 traços selecionados. Desta forma, reforçamos a coerência correspondente às reflexões primárias, em detrimento dos eventos restantes (ground-roll, múltiplas e demais eventos não correlacionados), localizado nas demais autoimagens. O método foi testado sobre uma linha sísmica terrestre da Bacia do Tacutu, Brasil. Os resultados obtidos mostram espectros de velocidades com melhor definição, como também seções empilhadas exibindo melhor continuidade das reflexões e menor ruído ground-roll, comparado com os resultados do processamento bruto (sem a filtragem SVD).Palavras-chave: empilhamento CMP; processamento sísmico; filtragem SVD, atenuação do ground-roll; análise de velocidade


2016 ◽  
Vol 34 (2) ◽  
Author(s):  
Washington Oliveira Martins ◽  
Milton José Porsani ◽  
Michelângelo G. da Silva

ABSTRACT. We applied an adaptive seismic data filtering method, based on the singular value decomposition (SVD) to improve the identification of reflectors and geological structures in 3D stacked seismic volumes...Keywords: seismic data processing, SVD filtering, 3D pos-stacked filtering, adaptive filtering. RESUMO. Nós aplicamos um método de filtragem adaptativa de dados sísmicos, baseado na decomposição em valores singulares (SVD), para melhorar a identificação de refletores e estruturas geológicas em volumes sísmicos empilhados 3D...Palavras-chave: processamento sísmico, filtragem SVD, filtragem pós-stack 3D, filtragem adaptativa.


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