Radar Signal Propagation Hole Detection
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Solution Overview
Problem
Radar detection systems face disturbances due to the 'propagation hole' phenomenon, which affects the accuracy of primary parameters like signal level, angle of arrival, frequency, and pulse width, leading to errors in source identification and analysis.
Innovation Solution
A method to detect propagation holes by calculating empirical variance and covariance of primary parameters, using a block-wise approach to identify disturbances through variance estimation and covariance analysis, and applying statistical tests to determine the presence of propagation holes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signal acquisition is performed in disturbed conditions (propagation holes), then detection coverage is maintained, but measurement precision of primary parameters deteriorates
Solution Approach 1:
The patent applies preliminary action by calculating empirical variance and covariance of primary parameters before final detection decisions are made. The system pre-processes the radar signal data by computing statistical characteristics (variance for each parameter and covariance between parameters) to establish a baseline that accounts for propagation hole effects, thereby improving subsequent measurement precision without sacrificing detection coverage
Solution Approach 2:
The patent introduces an intermediary statistical analysis layer between signal acquisition and final detection. By computing empirical variance and covariance as intermediate variables, the system creates a mediator that captures the effects of propagation holes and allows the detection algorithm to compensate for these disturbances, thereby maintaining measurement precision under adverse conditions
2Measurement precision
If block-wise statistical analysis is applied to detect propagation holes, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the radar signal data into blocks and performing statistical analysis on each block independently. This block-wise approach segments the complex detection problem into manageable units, allowing empirical variance and covariance to be calculated for discrete segments, which improves parameter estimation accuracy while keeping computational complexity localized to each block rather than requiring complex global processing
3Reliability
If empirical variance and covariance calculations are performed on all primary parameters, then detection reliability improves, but loss of time increases
Solution Approach 1:
The patent applies partial action by selectively calculating empirical variance for each primary parameter and empirical covariance only where necessary for propagation hole detection. Rather than performing exhaustive analysis on all possible signal characteristics, the system focuses computational resources on the specific statistical measures (variance and covariance of primary parameters) that are most relevant for detecting propagation holes, thereby improving detection reliability without excessive time consumption
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
Improves the accuracy of primary parameter measurements and analysis by identifying and mitigating the effects of propagation holes, enhancing the detection and identification of radar sources.
Implementation Method 1
The present application concerns the field of radar detectors and more particularly the detection of so-called 'propagation hole' phenomena through an analysis of the empirical variance of the primary parameters
Implementation Method 2
Said phenomenon results from the superposition on the one hand of an electromagnetic wave 11 emitted by a distant radar 12 reaching the receiver along a direct path with the same version of said wave using an indirect path 13 via a reflection on a plane obstacle
Data Source
Figure 1~2
Figure 3~4
AI summary
The method involves calculating empirical covariance of a set of parameters. A determinant of a covariance matrix is calculated. A quantity is calculated by using a formula that contains predefined value and nominal deviation. A disturbance is detected and not detected on index block, if the quantity is greater than 1+ beta and is lesser than or equal to 1- beta, respectively. The index block is disturbed or non-disturbed based on information that is identical to information temporary index block, if the quantity is lesser than or equal to 1+ beta and greater than or equal to 1-beta. The beta is a given positive integer.