Radar Plot Density Estimation via Adaptive Basis Functions

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Solution Overview

Problem

Current radar systems face challenges in accurately estimating local plot density, leading to issues with tracking targets and distinguishing between real and false detections, particularly in cluttered environments where statistical uncertainty and resolution are not well-balanced.

Innovation Solution

A method that establishes a set of M-dimensional basis functions and corresponding coefficients to represent local plot density, updating these based on radar data, and adjusting them to maintain a predetermined number of plots within each cell, thereby controlling statistical uncertainty and improving resolution.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If basis functions are recursively divided based on plot density threshold, then local plot density estimation is improved, but statistical uncertainty and resolution balance deteriorates

Engineering Contradiction:

Improvelocal plot density estimation accuracyVSAvoidstatistical uncertainty balance

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent changes the control parameter from plot density threshold to number of plots per basis function. This parameter transformation allows the system to maintain reliable statistical uncertainty by ensuring each basis function contains a sufficient number of plots, while still achieving high resolution through adaptive subdivision when needed

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements dynamic adjustment of basis function granularity based on the actual number of plots observed. The system adapts the resolution locally by subdividing basis functions only in regions where the plot count warrants it, creating a dynamic balance between resolution and statistical reliability

Inventive Principle:

Principle #15Dynamics

2Manufacturing precision

If basis functions are recursively divided to increase resolution, then local plot density resolution is improved, but device complexity increases

Engineering Contradiction:

Improvelocal plot density resolutionVSAvoidrecursive division complexity

Core Design Contradiction:

Manufacturing precisionVSDevice complexity

Solution Approach 1:

The patent segments the observation volume into basis functions and further subdivides them only where necessary based on plot count criteria. This selective segmentation approach achieves high local resolution in cluttered regions while maintaining coarser resolution in empty regions, reducing overall system complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent applies different levels of basis function refinement to different regions of the observation volume based on local plot density. High-resolution fine-grained basis functions are used only where needed (in cluttered regions), while low-resolution coarse-grained basis functions are used in empty regions, optimizing the complexity-resolution tradeoff

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS9864051B2Method of estimating a local plot density in a radar system; a plot density estimator and a radar system with a plot density estimator

Publication Date: 2018.01.09 TERMA AS

AI summary

A method of estimating a local plot density in a radar system observing an observation volume, the radar system configured to generate plots with plot attributes, by establishing a non-empty set of M-dimensional basis functions and corresponding coefficients, and repeatedly updating at least one coefficient based on at least one plot as obtained from the radar system, adjusting the basis functions and corresponding coefficients to represent a number of plots in a predetermined adjusting interval, and estimating the local plot density at a given point in the observation volume.