Synthetic Aperture Radar Range Resolution via Sub-band Segmentation
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Solution Overview
Problem
Current radar systems using step-frequency waveforms face limitations in range resolution due to grating lobe effects at the boundaries of radar image profiles.
Innovation Solution
A synthetic aperture radar imaging method that involves receiving radar return pulses, deskewing them to remove transmission pulse effects, determining maximum likelihood estimates of residual motion parameters, correcting inertial navigation system errors, and convolving pulses to generate range compressed images, which are then combined to reduce grating lobes and improve resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If step-frequency waveforms are used to combine RF bands to improve range resolution, then range resolution is improved, but grating lobe effects occur at the boundaries of the radar image profiles
Solution Approach 1:
The radar waveform is segmented into multiple disjoint RF bands (sub-bands) rather than using a continuous frequency sweep. Each sub-band is processed separately through individual range compression and image formation, then the resulting images are combined. This segmentation approach allows the system to achieve high range resolution by effectively utilizing the total bandwidth while avoiding grating lobe effects that occur when combining contiguous bands, as each sub-band operates independently without boundary interference.
2Measurement precision
If multiple RF bands are combined to improve range resolution, then range resolution is improved, but side lobe effects increase in the combined range profile image
Solution Approach 1:
The harmful side lobe effects are extracted and identified as originating from the boundaries between adjacent RF bands during the combining process. The invention addresses this by processing each sub-band independently through separate matched filtering and image formation, then combining the resulting images rather than directly combining the raw RF bands. This extraction approach isolates the side lobe generation mechanism and allows for individual optimization of each sub-band processing chain.
Solution Approach 2:
Different processing optimizations are applied to different parts of the signal chain. Each sub-band undergoes localized matched filtering with its own transmit waveform, followed by independent range compression and image formation. The combining operation then integrates these locally optimized sub-band images. This local quality approach allows each sub-band to be processed with optimal parameters for its specific frequency range, reducing overall side lobe effects in the final combined image.
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
The method achieves higher range resolution and reduces grating lobes, enabling the radar to distinguish between more closely spaced objects than conventional systems, thereby enhancing the radar's imaging capabilities.
Implementation Method 1
receiving a plurality of radar return pulses acquired by an airborne radar, wherein each radar return pulse is generated in response to a corresponding radar transmission pulse reflected from objects within a region of interest on the ground
Implementation Method 2
combining each radar return pulse with a sinusoid to reduce the radar return pulses to a baseband frequency
Implementation Method 3
convolving each radar return pulse with its corresponding radar transmission pulse to generate a range compressed image for each radar return pulse
Data Source
AI summary
A synthetic aperture radar imaging method that combines each radar return pulse with a sinusoid to reduce the radar return pulses to a baseband frequency and deskew each radar return pulse. It includes determining a maximum likelihood estimate (MLE) of residual motion parameters for a dominant scatterer on the ground relative to the airborne radar and correcting for errors in inertial navigation system measurements based on the MLE residual motion parameters. It includes convolving each radar return pulse with its corresponding radar transmission pulse to generate a range compressed image for each radar return pulse and generating a sub-band range profile image for each radar return pulse and its corresponding radar transmission pulse based on the corresponding range compressed image that has been corrected for residual motion. Performing bandwidth extrapolation on each sub-band and subsequently combining the three bands to produce an enhanced resolution image without grating lobes.