Related Experiment Video
Updated: Oct 15, 2025
07:14
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
Published on: May 1, 2018
7.9K
A Novel Clutter Suppression Method Based on Sparse Bayesian Learning for Airborne Passive Bistatic Radar with
Jipeng Wang1, Jun Wang1, Yun Zhu2
1National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071, China.
Sensors (Basel, Switzerland)
|October 26, 2021
Summary
This study introduces a new sparse Bayesian learning algorithm to address multipath interference in airborne passive bistatic radar (PBR) systems. The method effectively estimates and suppresses spatial-temporal clutter, improving target detection performance.
Area of Science:
- Radar Systems Engineering
- Signal Processing
- Electromagnetics
Background:
- Airborne passive bistatic radar (PBR) systems are susceptible to multipath components in reference signals.
- Platform movement introduces varying Doppler frequencies in multipath components, broadening clutter power spectrum.
- This broadening degrades clutter estimation/suppression, increases detection blind zones, and causes target self-cancellation.
Purpose of the Study:
- To develop a novel algorithm for direct clutter estimation and multipath clutter suppression in PBR.
- To mitigate the adverse effects of multipath interference on PBR performance.
Main Methods:
- A sparse Bayesian learning (SBL) based algorithm is proposed.
- Spatial-temporal clutter is represented as covariance matrix vectors.
- Multipath effects are decorrelated within these vectors.
- Alternating iteration minimizes modeling error for clutter covariance matrix estimation.
Main Results:
- The proposed SBL algorithm effectively estimates spatial-temporal clutter.
- Multipath clutter components are successfully suppressed.
- The resulting clutter covariance matrix is free from multipath contamination.
- Simulations confirm the method's efficacy with contaminated reference signals.
Conclusions:
- The novel SBL algorithm provides an effective solution for clutter estimation and suppression in PBR systems with multipath interference.
- This approach enhances the robustness and performance of PBR systems operating in challenging environments.
Related Concept Videos
01:05Difference from Background: Limit of Detection
7.4K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
7.4K
01:30Classification of Signals
1.0K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.0K
01:10Propagation of Uncertainty from Systematic Error
1.0K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.0K
00:59Propagation of Uncertainty from Random Error
1.3K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.3K
01:11Residuals and Least-Squares Property
8.1K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.1K
01:20Cluster Sampling Method
13.1K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
13.1K