Iterative Channel Estimation for Full-Duplex Radar
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current full-duplex communication systems face challenges in efficiently modeling and estimating high-order nonlinear adaptive filters due to increased complexity and computational burden, especially when dealing with long FIR channels and quasi-stable nonlinear components, which limits their performance in applications like radar systems.
Innovation Solution
A low-complexity approach is introduced, where the high-order nonlinear adaptive filter is modeled as two separate filters, with an iterative method that estimates the nonlinear Hammerstein model cascaded with a linear FIR channel, reducing the number of filter parameters and computational cost by initializing parameters from recent operations and storing them for future use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-order nonlinear adaptive filter is used to model full-duplex communication systems, then filtering accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The high-order nonlinear adaptive filter is segmented into a cascade connection of a nonlinear filter (Hammerstein model) and a linear FIR channel. This segmentation reduces the overall filter order while maintaining filtering accuracy by separating the nonlinear characteristics from the linear channel response, thereby reducing computational complexity
2Measurement precision
If a high-order nonlinear adaptive filter is used, then filtering accuracy is improved, but processing time increases
Solution Approach 1:
By segmenting the filter into nonlinear and linear components that can be estimated separately through iterative methods, the processing time is reduced while maintaining accuracy. The segmentation allows for more efficient computation compared to estimating a single high-order filter
Solution Approach 2:
The nonlinear filter parameters are estimated first as a preliminary step, providing an initial characterization of the system. This preliminary estimation simplifies subsequent linear channel estimation, reducing overall processing time while maintaining accuracy
3Measurement precision
If iterative parameter estimation is used, then filtering accuracy is improved, but computational cost increases
Solution Approach 1:
The iterative estimation process is segmented into separate stages for nonlinear and linear parameters. This segmentation reduces the computational cost of each iteration by breaking down the complex joint estimation problem into more manageable sub-problems while achieving the same estimation accuracy
Data Source
AI summary
Signal cancellation is implemented by way of a non-linear filter model feeding to an FIR channel. Parameters defining each element are iteratively established, first by initially estimating the FIR channel then jointly estimating the two elements.