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Updated: Jul 7, 2026
10:53
Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques
Published on: March 12, 2019
Clutter rejection filters in color flow imaging: a theoretical approach
1Dept. of Physiol. and Biomed. Eng., Norwegian Univ. of Sci. and Technol., Trondheim.
Summary
This study introduces linear clutter rejection filters and quantifies their bias on velocity and velocity spread estimates. The findings are crucial for accurate signal processing in radar and sonar applications.
Area of Science:
- Signal Processing
- Radar Meteorology
- Array Signal Processing
Background:
- Linear clutter rejection filters, including FIR/IIR and regression filters, are essential for removing unwanted signals.
- These filters, described by complex matrices, can introduce biases in velocity and velocity spread estimations due to generated frequency components.
Purpose of the Study:
- To describe a general class of linear clutter rejection filters.
- To analyze the bias introduced by these filters on velocity and velocity spread estimates.
- To quantify this bias using a two-dimensional signal model.
Main Methods:
- Describing linear clutter rejection filters using complex matrices.
- Defining frequency responses for these filters.
- Utilizing a two-dimensional (axial and temporal) model of received signals.
- Quantifying bias for autocorrelation and time shift cross-correlation estimators.
Main Results:
- General linear filters may create frequency components absent in the input signal, causing bias.
- The clutter filter's effect on autocorrelation estimates is described by a frequency domain transfer function, varying with temporal lag.
- Bias in velocity and velocity spread estimates was quantified for both autocorrelation and cross-correlation algorithms.
Conclusions:
- Linear clutter rejection filters, while effective, introduce quantifiable bias in velocity and velocity spread estimates.
- Understanding this bias is critical for accurate interpretation of processed signals.
- Theoretical expressions and numerical examples are provided for bias quantification.
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