Weight Function Generation for Radar Side Lobe Suppression
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Solution Overview
Problem
Conventional radar devices face significant signal processing loss and inadequate suppression of side lobes and spurious frequency components, leading to masking of minute input signals due to large aperture plane distribution losses and insufficient reduction of side lobes in the frequency domain.
Innovation Solution
Transforming the time domain signal into the frequency domain, multiplying it by window functions such as Hamming, Hanning, Gaussian, BlackmanHarris, or FlatTop windows, and then restoring it to the time domain to generate a weight function that reduces signal processing loss and suppresses spurious components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-generated harmful factors
If a weight function is multiplied by a reception signal in time domain to reduce side lobe level, then side lobe level is reduced, but signal processing loss increases
Solution Approach 1:
The patent transforms the weight function from time domain to frequency domain using Fourier transform, applies the window function in frequency domain, and then transforms back to time domain. This dimensional change allows the weight function to simultaneously reduce side lobe levels and minimize signal processing loss by operating in the frequency domain where the trade-off can be optimized.
Solution Approach 2:
The patent changes the domain parameter of the weight function from time domain to frequency domain. By representing the weight function in frequency domain and applying appropriate window functions (Hamming, Hanning, Gaussian, Blackman-Harris, or FlatTop), the signal processing loss is reduced while maintaining effective side lobe suppression.
2Object-generated harmful factors
If aperture plane distribution is applied to reduce side lobe, then side lobe level is reduced, but signal processing loss becomes large
Solution Approach 1:
The patent applies aperture plane distribution in the frequency domain rather than time domain. By transforming the weight function to frequency domain and applying the aperture distribution pattern there, the side lobe reduction is achieved with significantly reduced signal processing loss compared to conventional time domain application.
Solution Approach 2:
The patent changes the operational domain parameter from time to frequency for applying aperture plane distribution. This parameter change allows the system to achieve effective side lobe suppression while minimizing the associated signal processing loss through frequency domain optimization.
3Reliability
If weight function multiplication is used to improve characteristics, then side lobe is reduced, but signal processing loss remains large
Solution Approach 1:
The patent improves signal characteristics by performing weight function multiplication in the frequency domain. This dimensional change enables better control over the trade-off between side lobe reduction and signal processing loss, achieving improved reliability with reduced energy loss compared to time domain multiplication.
Solution Approach 2:
The patent changes the domain parameter where weight function multiplication is performed from time domain to frequency domain. This allows optimization of the multiplication operation to achieve better signal characteristics with reduced signal processing loss through frequency domain processing.
4Object-generated harmful factors
If spurious frequency component suppression is attempted in time domain, then processing is simple, but suppression effectiveness is insufficient
Solution Approach 1:
The patent suppresses spurious frequency components by transforming to frequency domain, applying appropriate window functions, and transforming back. This dimensional change enables effective suppression of spurious components that cannot be adequately achieved in time domain, significantly improving measurement precision.
Solution Approach 2:
The patent changes the domain parameter for spurious component suppression from time domain to frequency domain. This parameter change enables the use of frequency domain window functions that are specifically designed to suppress spurious components, achieving much higher suppression effectiveness.
Data Source
AI summary
A rectangular wave for determining a range of a weight function is transformed to frequency domain by an FFT or the like, and after being multiplied by a window function (BlackmanHarris window function, for example) generated on a frequency axis by a multiplier, the frequency domain is transformed again to the time domain by an IFFT or the like thereby to generate a weight function.