robust parameter estimation sensor (13 résultats)

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Hardback or Cased Book. Etat : New. Robust Parameter Estimation with Sensor Arrays in Complex Electromagnetic Environments. Book.

Etat : New.

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Hardcover. Etat : new. Hardcover. Sensor arrays have been widely applied in various fields, e.g., wireless communication, radar, and navigation. Key roles of sensor arrays include providing spatial parameter estimations and enhancing parameter estimation performances in other domains. However, with the present complex electromagnetic environment, general estimation methods experience significant performance degradations when encountering complex signal propagation, such as multipath or occlusion situations. Meanwhile, a sensor array system itself also suffers from uncertainties, such as gain-phase errors and mutual coupling, which are classic but long-term problems. In this Special Issue, we have collected a range of representative research works on this topic. In response to errors in the sensor array itself, such as random array deformations and mutual coupling, sparse array design and small-aperture antenna design methods are proposed. Meanwhile, from a parameter estimation perspective, conformal maps, element selection, and deep learning-based methods are proposed. In response to the received data errors caused by complex environments, such as multipath propagation and low signal-to-noise ratios, methods based on reweighted sparse sensing, pseudo noise resampling, Bayesian estimation, and multiphase filters are proposed. Furthermore, in response to the high complexity brought by robust processing algorithms, methods such as Taylor Compensation and dimensionality reduction are proposed. The relevant methods have shown excellent performance under complex system errors through simulation tests or actual tests. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Etat : New.

Hardcover. Etat : new. Hardcover. Sensor arrays have been widely applied in various fields, e.g., wireless communication, radar, and navigation. Key roles of sensor arrays include providing spatial parameter estimations and enhancing parameter estimation performances in other domains. However, with the present complex electromagnetic environment, general estimation methods experience significant performance degradations when encountering complex signal propagation, such as multipath or occlusion situations. Meanwhile, a sensor array system itself also suffers from uncertainties, such as gain-phase errors and mutual coupling, which are classic but long-term problems. In this Special Issue, we have collected a range of representative research works on this topic. In response to errors in the sensor array itself, such as random array deformations and mutual coupling, sparse array design and small-aperture antenna design methods are proposed. Meanwhile, from a parameter estimation perspective, conformal maps, element selection, and deep learning-based methods are proposed. In response to the received data errors caused by complex environments, such as multipath propagation and low signal-to-noise ratios, methods based on reweighted sparse sensing, pseudo noise resampling, Bayesian estimation, and multiphase filters are proposed. Furthermore, in response to the high complexity brought by robust processing algorithms, methods such as Taylor Compensation and dimensionality reduction are proposed. The relevant methods have shown excellent performance under complex system errors through simulation tests or actual tests. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. Hardcover. Sensor arrays have been widely applied in various fields, e.g., wireless communication, radar, and navigation. Key roles of sensor arrays include providing spatial parameter estimations and enhancing parameter estimation performances in other domains. However, with the present complex electromagnetic environment, general estimation methods experience significant performance degradations when encountering complex signal propagation, such as multipath or occlusion situations. Meanwhile, a sensor array system itself also suffers from uncertainties, such as gain-phase errors and mutual coupling, which are classic but long-term problems. In this Special Issue, we have collected a range of representative research works on this topic. In response to errors in the sensor array itself, such as random array deformations and mutual coupling, sparse array design and small-aperture antenna design methods are proposed. Meanwhile, from a parameter estimation perspective, conformal maps, element selection, and deep learning-based methods are proposed. In response to the received data errors caused by complex environments, such as multipath propagation and low signal-to-noise ratios, methods based on reweighted sparse sensing, pseudo noise resampling, Bayesian estimation, and multiphase filters are proposed. Furthermore, in response to the high complexity brought by robust processing algorithms, methods such as Taylor Compensation and dimensionality reduction are proposed. The relevant methods have shown excellent performance under complex system errors through simulation tests or actual tests. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Buch. Etat : Neu. Robust Parameter Estimation with Sensor Arrays in Complex Electromagnetic Environments | Buch | Englisch | 2025 | MDPI AG | EAN 9783725843930 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.