Through-the-Wall Imaging Sparse Inversion Ghost Artifact Suppression
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Solution Overview
Problem
Existing through-the-wall imaging techniques struggle to effectively eliminate multi-path reflections without prior knowledge of the scene geometry, leading to ghost artifacts in images due to indirect reflections from walls, which are not feasible in practical scenarios.
Innovation Solution
The method employs sparse inversion to iteratively recover time-domain primary impulse responses and determine a delay convolution operator, using l1 regularized sparse recovery for target detection and reflection-operator estimation, allowing for target localization without prior knowledge of scene or wall parameters, even in noisy conditions, and compensates for waveform distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional multi-path elimination techniques are used, then ghost artifacts can be reduced, but prior knowledge of scene geometry is required which is not feasible in practice
Solution Approach 1:
The patent inverts the traditional approach by not assuming known scene geometry to eliminate multi-path effects, but rather using sparse inversion to simultaneously recover both target locations and wall reflection characteristics from the received signals, effectively solving the inverse problem without prior geometric information
Solution Approach 2:
The system performs self-calibration by using the received signals themselves to estimate both the target impulse responses and the wall reflection operator, eliminating the need for external reference data or prior geometric knowledge about the scene configuration
2Measurement precision
If sparse inversion with l1 regularized sparse recovery is applied, then target detection accuracy improves without prior knowledge, but computational complexity increases
Solution Approach 1:
The patent transforms the complex non-convex inverse problem into a convex optimization problem by applying l1 regularization, changing the mathematical formulation to enable efficient iterative solution through sparse recovery algorithms that balance accuracy and computational feasibility
3Object-affected harmful factors
If iteratively recovering primary impulse responses and determining delay convolution operator is performed, then multi-path elimination effectiveness improves, but processing time increases
Solution Approach 1:
The patent implements an iterative feedback mechanism where the estimated wall reflection operator from one iteration is used to refine the primary impulse response estimation in the next iteration, progressively eliminating multi-path effects while converging to the optimal solution
Solution Approach 2:
The system continuously refines the impulse response and reflection operator estimates through multiple iterations, maintaining useful computational action throughout the process to achieve progressive improvement in multi-path elimination rather than requiring a single complex computation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively removes internal wall reflections, localizes targets behind the wall, and suppresses ghosting artifacts, enabling accurate target detection directly in the image domain even from randomly subsampled arrays and severe noise scenarios.
Implementation Method 1
a transmitter emits an electromagnetic (EM) radar pulse, which propagates through a wall. The pulse is reflected by a target on the other side of the wall
Implementation Method 2
The pulse is reflected by a target on the other side of the wall, and then propagates back to a receiver as an impulse response convolved with the emitted pulse
Implementation Method 3
the received signal is often corrupted with indirect, secondary reflections from the wall, which result in ghost artifacts in an image that appear as noise
Implementation Method 4
The method iteratively recovers time-domain primary impulse responses of targets behind a wall and then determines a delay convolution operator that best maps the primary impulse response of each target to the multi-path reflections available in the received signal
Implementation Method 5
Typically, the transmitter and receiver use an antenna array
Data Source
AI summary
Targets are detected in a scene behind a wall by first transmitting a pulse through the wall. Then, a primary impulse response is detected by a sparse regularized least squares inversion applied to received signals corresponding to the reflected pulse. A delay operator that matches the primary impulse response to similar impulse responses in the received signals is also determined. A distortion of the pulse after the pulse passes through the wall but before the pulse is reflected by the target can also be determined. The distortion is used in an iterative process to refine the detection of the target and to suppress ghosting artifacts.