Iteratively Reweighted Least Squares Velocity Estimation
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Solution Overview
Problem
Existing methods for robust estimation of target velocity using radar technology face challenges in controlling estimation quality and efficiency, particularly in real-time applications, due to high computational resource usage and potential inaccuracies from noise and outliers.
Innovation Solution
A method that controls the number of iterations in the iteratively reweighted least squares algorithm by using statistical measures and threshold conditions to assess plausibility and convergence, ensuring accurate velocity estimation with reduced computational effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the iteratively reweighted least squares algorithm is applied to robust velocity estimation, then the estimation accuracy is improved, but the computational resource usage increases
Solution Approach 1:
The patent applies preliminary action by implementing early stopping criteria based on statistical measures (covariance matrix eigenvalues, residual analysis) before the iterative algorithm completes all planned iterations. This allows the system to stop computation when sufficient accuracy is achieved, preventing unnecessary computational resource consumption while maintaining robust velocity estimation.
Solution Approach 2:
The patent uses partial action by selectively applying the full iterative reweighted least squares algorithm only when necessary (when initial estimates are unreliable or statistical measures indicate non-convergence), and using simplified or truncated versions when conditions permit, thus balancing accuracy requirements with computational efficiency.
2Reliability
If the number of iterations is increased to improve estimation accuracy, then the robustness against noise and outliers is enhanced, but the processing time increases
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring statistical measures (covariance matrix eigenvalues, residual distributions, weight convergence) during the iterative process. Based on this feedback, the algorithm dynamically adjusts the number of iterations performed, stopping early when convergence criteria are met, thus maintaining robustness while minimizing processing time.
Solution Approach 2:
The patent applies dynamics by making the iteration count adaptive rather than fixed. The algorithm dynamically determines the optimal number of iterations based on real-time assessment of data quality, noise levels, and convergence behavior, allowing the system to use more iterations when needed for robustness and fewer iterations when data quality is high.
3Measurement precision
If statistical measures and threshold conditions are applied to control iterations, then the estimation quality is improved, but the algorithm complexity increases
Solution Approach 1:
The patent extracts and separates the complex statistical assessment functions into distinct, modular components (covariance matrix calculation, eigenvalue analysis, residual computation, threshold comparison). This modular extraction makes the algorithm easier to implement, debug, and optimize while maintaining the sophisticated quality control that improves estimation precision.
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 provides a more accurate and efficient estimation of target velocity by optimizing the number of iterations, improving the robustness and validity of the estimation process while minimizing computational complexity.
Implementation Method 1
a radar sensor unit adapted to receive signals that are emitted from a host vehicle and reflected by a target
Implementation Method 2
Doppler radar technology implemented in a radar sensor unit which is adapted to receive signals that are emitted from a host vehicle and reflected by a target
Data Source
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AI summary
A method for robust estimation of the velocity of a target using a host vehicle equipped with a radar system comprising determining a plurality of radar detection points, determining a compensated range rate, and determining an estimation of a first component of the velocity profile equation of the target and an estimation of a second component of the velocity profile equation of the target by using an iteratively reweighted least squares methodology comprising at least one iteration. The estimations and of the first and second components and of the velocity profile equation are not determined from a further iteration of the iteratively reweighted least squares methodology if at least one statistical measure representing the deviation of an estimated dispersion of the estimations and of the first and second components and of a current iteration from a previous iteration and/or the deviation of an estimated dispersion of the residual from a predefined dispersion of the range rate meets a threshold condition.