Radar Target Classification Using MFBLP Scatterer Extraction

Solution Overview

Problem

Radar target classification methods, such as SAR and ISAR, face limitations including high computational load, classification errors, and resolution limitations that hinder real-time identification due to scatterer scintillation and complexity in characterizing targets with edges or decaying modes.

Innovation Solution

A radar system employing a large bandwidth pulse and Modified Forward Backward Linear Prediction (MFBLP) for high range resolution, estimating principal scatterers and forming feature vectors to classify targets using a posteriori conditional probability density, with Cramer-Rao bound computation for robustness and matched filter correlator for extracting range and amplitude pairs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If SAR and ISAR target imaging methods are used for target classification, then target scatterer decomposition in range and cross-range can be achieved, but the dwell time required is long (order of seconds) and computational load is substantial

Engineering Contradiction:

Improvetarget scatterer decomposition precisionVSAvoiddwell time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent extracts only the essential range profile information from the radar return signal, focusing on principal scatterers rather than performing complete SAR/ISAR imaging. This extraction approach obtains sufficient classification data without requiring the full imaging process, thereby reducing dwell time while maintaining classification accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies partial action by using a limited number of range bins and focusing computation only on principal scatterers rather than processing the entire signal space. This partial processing approach achieves classification with reduced computational load and faster execution compared to complete SAR/ISAR imaging

Inventive Principle:

Principle #16Partial or excessive action

2Reliability

If SAR and ISAR methods are used for target classification, then target identification can be achieved, but computational load on host computer is substantial

Engineering Contradiction:

Improvetarget identification reliabilityVSAvoidcomputational load

Core Design Contradiction:

ReliabilityVSPower

Solution Approach 1:

The patent extracts only the essential range profile information from the radar return signal, focusing on principal scatterers rather than performing complete SAR/ISAR imaging. This extraction approach obtains sufficient classification data without requiring the full imaging process, thereby reducing dwell time while maintaining classification accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies partial action by using a limited number of range bins and focusing computation only on principal scatterers rather than processing the entire signal space. This partial processing approach achieves classification with reduced computational load and faster execution compared to complete SAR/ISAR imaging

Inventive Principle:

Principle #16Partial or excessive action

3Device complexity

If conventional radar resolution is used, then system complexity is reduced, but the ability to separate significant scatterers within the same resolution cell is reduced, leading to scatterer scintillation

Engineering Contradiction:

Improveradar system complexityVSAvoidscatterer separation precision

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent changes the bandwidth parameter of the radar signal to achieve high range resolution. By using a large bandwidth pulse, the system resolves scatterers that would otherwise be indistinguishable within the same resolution cell, eliminating scintillation effects without requiring complex hardware modifications

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS7289060B1Super high range resolution amplitude and range principal scatterer (SHARP) classifier

Publication Date: 2007.10.30 RAYTHEON CO

AI summary

A radar classifies an unknown target illuminated with a large bandwidth pulse. The large bandwidth pulse may be algorithmically synthesized. The target reflects the large bandwidth pulse to form a return. The return is digitized into digital samples at range bin intervals. A computer extracts unknown range and amplitude pairs descriptive of the unknown target from the digital samples. Some range and amplitude pairs are located within one range bin interval. Principle scatterers are extracted from the unknown range and amplitude pairs using Modified Forward backward linear Prediction to form an unknown feature vector for the target. A plurality of pre-stored, known feature vectors containing known range and amplitude pairs are retrieved from the computer. The known range and amplitude pairs are descriptive of known targets, and are grouped in clusters having least dispersion for each of the known targets. The computer associates, for the principal scatterers, the unknown feature vector descriptive of the unknown target with each of the known feature vectors. The target is classified by using highest a posteriori conditional probability density obtained from comparing the known feature vectors with the unknown feature vector. The principal scatterers descriptive of the unknown, target are estimated using a Modified Forward Backward Linear Prediction. The Modified Forward Backward Linear Prediction also estimates range of the principal scatterers forming the unknown target. The principal scatterers are tested for decaying modes. The Modified Forward Backward Linear Prediction estimates are evaluated using Cramer Reo Bound computation for robustness.