Radar Target Detection via 2D Multi-Dimensional Folding

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Current radar systems face challenges in detecting slow-moving targets due to limitations in radar dwell duration, clutter interference, and the difficulty in separating target signals from clutter returns, especially in environments with wind-blown, rain, and bird-flock clutter, which overwhelm target trackers and clutter maps.

Innovation Solution

A 2-D multi-dimensional folding method is employed to process radar return signals, determining noise power levels, estimating Doppler and azimuth frequencies, and distinguishing between dispersive and non-dispersive scatterers to separate target information from clutter, using a computer-implemented system with processors and signal processing algorithms.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional STAP technique is used to detect endo-clutter targets, then target detection capability is improved, but the system requires large number of radar return snapshots for training and stationary interference data, increasing system complexity and data requirements

Engineering Contradiction:

Improvetarget detection capabilityVSAvoiddata requirements

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent extracts and separates target signals from clutter by identifying and removing dispersive scatterers (clutter) from the radar return signal, leaving only non-dispersive scatterers (targets). This extraction approach eliminates the need for large training datasets required by STAP, as it directly processes the received signal to isolate targets.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms the signal processing approach by analyzing scatterers in the Doppler-azimuth frequency domain rather than relying on temporal snapshots. By estimating Doppler and azimuth frequencies and identifying dispersive characteristics in this transformed domain, the method achieves target detection without requiring extensive training data across multiple time snapshots.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

2Speed

If radar dwell duration is increased to improve slow target detection, then minimum detectable velocity is improved, but the system performance is limited by clutter spread in Doppler and azimuth, reducing measurement precision

Engineering Contradiction:

Improveminimum detectable velocityVSAvoidtarget-clutter separation accuracy

Core Design Contradiction:

SpeedVSMeasurement precision

Solution Approach 1:

The patent applies local quality by treating different scatterers differently based on their dispersive characteristics. Non-dispersive scatterers (targets) are preserved while dispersive scatterers (clutter) are removed. This localized processing in the Doppler-azimuth domain allows precise separation of targets from clutter even when they overlap in velocity space.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent changes the analysis parameters from time-domain snapshot processing to frequency-domain analysis of Doppler and azimuth components. By estimating complex frequencies and identifying dispersive characteristics, the method transforms the problem into a parameter space where target-clutter separation becomes more precise regardless of dwell duration.

Inventive Principle:

Principle #35Parameter changes

3Loss of information

If clutter spread in Doppler is present due to wind-blown ground clutter, rain, or bird flocks, then the clutter map becomes overwhelmed with detections, but target tracking accuracy is maintained by separating dispersive from non-dispersive scatterers

Engineering Contradiction:

Improvetarget tracking accuracyVSAvoidclutter interference

Core Design Contradiction:

Loss of informationVSObject-generated harmful factors

Solution Approach 1:

The patent converts the harmful clutter interference into a beneficial classification criterion. By analyzing the dispersive characteristics of scatterers in the Doppler-azimuth domain, the method identifies that clutter (wind-blown, rain, bird flocks) exhibits dispersive behavior while targets remain non-dispersive. This transforms clutter from a harmful factor into a identifiable class that can be systematically removed, actually improving target detection accuracy.

Inventive Principle:

Principle #22Blessing in disguise (Convert harm into benefit)

Data Source

PatentUS8912951B2Moving target detection using a two-dimensional folding approach

Publication Date: 2014.12.16 RAYTHEON CO

AI summary

A system and method for discrimination and identification of a target including: receiving a radar return signal including target information and clutter information; determining a two-fold forward or forward-backward data matrix from the received signal, using a multi-dimensional folding (MDF) process; computing singular values of the two-fold forward or forward-backward data matrix; using the computed singular values to determine a noise power level of the radar return signal; determining the number of scatterers in the radar return signal according to a predetermined threshold value above the noise power; estimating complex Doppler and azimuth frequencies of each scatterer from the determined number of scatterers using the MDF process; determining dispersive scatterers and non-dispersive scatterers using the estimated Doppler and azimuth complex frequencies of each scatterer; and distinguishing the target information from the clutter information, according to the determined dispersive scatterers and non-dispersive scatterers.