Measurement of Transmission Properties of HBC Channel and Its Impulse Response Model

We develop a new frequency-domain dynamic response method to model integrated Inertial Navigation System (INS) and Global Positioning System (GPS) architectures and provide an accurate impulse-response-based INS-only navigation solution when GPS signals are denied (GPS outages). The input to such a dynamic system is the INS-only solution and the output is the INS/GPS integration solution; both are used to derive the transfer function of the dynamic system using Least Squares Frequency Transform (LSFT). The discrete Inverse Least Squares Frequency Transform (ILSFT) of the transfer function is applied to estimate the impulse response of the INS/GPS system in the time domain. It is shown that the long-term motion dynamics of a DQI-100 IMU/Trimble BD950 integrated system are recovered by 72%, 42%, 75%, and 40% for north and east velocities, and north and east positions respectively, when compared with the INS-only solution (prediction mode of the INS/GPS filter). A comparison between our impulse response model and the current state-of-the-art time-domain feed-forward neural network shows that the proposed frequency-dependent INS/GPS response model is superior to the neural network model by about 26% for 2D velocities and positions during GPS outages.

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A unified impulse response model for DCE-MRI

Magnetic Resonance in Medicine ◽

10.1002/mrm.24162 ◽

2012 ◽

Vol 68 (5) ◽

pp. 1632-1646 ◽

Cited By ~ 36

Author(s):

Matthias C. Schabel

Keyword(s):

Impulse Response ◽

Response Model ◽

Dce Mri

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An Impulse Response Model for the 60 Ghz Channel Based on Spectral Techniques of a-stable Processes

2007 IEEE International Conference on Communications ◽

10.1109/icc.2007.832 ◽

2007 ◽

Cited By ~ 1

Author(s):

N. Azzaoui ◽

L. Clavier

Keyword(s):

Impulse Response ◽

Stable Processes ◽

60 Ghz ◽

Response Model ◽

Spectral Techniques

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GIR v1.0.0: a generalised impulse-response model for climate uncertainty and future scenario exploration

10.5194/gmd-2019-379 ◽

2020 ◽

Cited By ~ 1

Author(s):

Nicholas James Leach ◽

Zebedee Nicholls ◽

Stuart Jenkins ◽

Christopher J. Smith ◽

John Lynch ◽

...

Keyword(s):

Greenhouse Gas ◽

Impulse Response ◽

Radiative Forcing ◽

Climate Models ◽

Temperature Response ◽

Structural Complexity ◽

Common Denominator ◽

Response Model ◽

State Dependent ◽

Effective Radiative Forcing

Abstract. Here we present a Generalised Impulse Response (GIR) model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis and teaching. This model is based on a set of only six equations, which correspond to the standard Impulse Response model used for greenhouse gas metric calculations by the IPCC, plus one physically-motivated additional equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. These six equations are simple and transparent enough to be easily understood and implemented in other models without reliance on the original source code, but flexible enough to reproduce observed well-mixed greenhouse gas (GHG) concentrations and atmospheric lifetimes, best-estimate effective radiative forcing, and temperature response. We describe the assumptions and methods used in selecting the default parameters, but emphasize that other methods would be equally valid: our focus here is on identifying a minimum level of structural complexity. The tunable nature of the model lends it to use as a fully transparent emulator of complex Earth System Models, such as those participating in CMIP6, while also reproducing the behaviour of other simple climate models. We argue that this GIR model is adequate to reproduce the global temperature response to global emissions and effective radiative forcing, and that it should be used as a lowest-common denominator to provide consistency and continuity between different climate assessments. The model design is such that it can be written in tabular data analysis software, such as Excel, increasing the potential user base considerably.

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FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration

Geoscientific Model Development ◽

10.5194/gmd-14-3007-2021 ◽

2021 ◽

Vol 14 (5) ◽

pp. 3007-3036

Author(s):

Nicholas J. Leach ◽

Stuart Jenkins ◽

Zebedee Nicholls ◽

Christopher J. Smith ◽

John Lynch ◽

...

Keyword(s):

Greenhouse Gas ◽

Impulse Response ◽

Climate Models ◽

Global Climate ◽

Climate System ◽

System Response ◽

Common Denominator ◽

Response Model ◽

Wide Range ◽

Complex Models

Abstract. Here we present an update to the FaIR model for use in probabilistic future climate and scenario exploration, integrated assessment, policy analysis, and education. In this update we have focussed on identifying a minimum level of structural complexity in the model. The result is a set of six equations, five of which correspond to the standard impulse response model used for greenhouse gas (GHG) metric calculations in the IPCC's Fifth Assessment Report, plus one additional physically motivated equation to represent state-dependent feedbacks on the response timescales of each greenhouse gas cycle. This additional equation is necessary to reproduce non-linearities in the carbon cycle apparent in both Earth system models and observations. These six equations are transparent and sufficiently simple that the model is able to be ported into standard tabular data analysis packages, such as Excel, increasing the potential user base considerably. However, we demonstrate that the equations are flexible enough to be tuned to emulate the behaviour of several key processes within more complex models from CMIP6. The model is exceptionally quick to run, making it ideal for integrating large probabilistic ensembles. We apply a constraint based on the current estimates of the global warming trend to a million-member ensemble, using the constrained ensemble to make scenario-dependent projections and infer ranges for properties of the climate system. Through these analyses, we reaffirm that simple climate models (unlike more complex models) are not themselves intrinsically biased “hot” or “cold”: it is the choice of parameters and how those are selected that determines the model response, something that appears to have been misunderstood in the past. This updated FaIR model is able to reproduce the global climate system response to GHG and aerosol emissions with sufficient accuracy to be useful in a wide range of applications and therefore could be used as a lowest-common-denominator model to provide consistency in different contexts. The fact that FaIR can be written down in just six equations greatly aids transparency in such contexts.

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