Multiplicative Sensor Processing for High-Resolution Object Detection
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Solution Overview
Problem
Conventional wireless object-detection and classification systems rely on linear, additive processing for beamforming and range profiling, which are inefficient and fail to achieve comparable results with shorter sensor arrays, limiting their resolution and accuracy in detecting multiple proximate objects.
Innovation Solution
The implementation of multiplicative processing techniques, such as Time Delay Product Processing (TDPP) and Array Product Processing (APP), which factorize polynomial expansions of sensor array measurements, allowing for efficient combination of signals to estimate direction of arrival and range, reducing the effective bandwidth required for range resolution and enhancing detection capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear additive processing is used for beamforming and range profiling, then the system can detect objects, but the resolution and accuracy are limited and processing efficiency is low
Solution Approach 1:
The patent transforms the processing domain from linear additive operations to multiplicative operations in the complex signal space. By applying multiplicative processing to the polynomial expansions of sensor array measurements, the system achieves superior range resolution and detection accuracy while reducing computational complexity compared to conventional linear methods
Solution Approach 2:
The patent replaces the conventional linear additive signal processing mechanism with a multiplicative processing mechanism. This substitution enables the system to achieve higher resolution with fewer measurements by exploiting the mathematical properties of polynomial factorization and complex signal multiplication, thereby improving both precision and efficiency
2Measurement precision
If longer sensor arrays are used to improve resolution, then detection accuracy increases, but device complexity and measurement requirements increase
Solution Approach 1:
The patent changes the processing parameter from linear combination to multiplicative combination of sensor signals. This transformation allows the system to achieve the same or better detection accuracy with a shorter sensor array by exploiting the information contained in the multiplicative interactions of the signal components
Solution Approach 2:
The patent factorizes the polynomial expansion of sensor measurements into multiple factors, each representing a specific spatial or temporal component. This segmentation allows the system to process and analyze individual factors separately, achieving high resolution with reduced array length by focusing computational resources on critical signal components
3Measurement precision
If more measurements are taken to improve resolution, then detection accuracy improves, but processing time increases
Solution Approach 1:
The patent transforms the measurement processing approach from linear accumulation to multiplicative synthesis. This parameter change enables the system to achieve high resolution with fewer measurements because the multiplicative operation inherently emphasizes significant signal components and suppresses noise, reducing the number of measurements needed for accurate detection
Solution Approach 2:
The patent applies multiplicative processing weights to the polynomial factors before combining measurements. This preliminary weighting action optimizes the contribution of each measurement to the final result, allowing the system to achieve high resolution with fewer measurements and thereby reducing processing time
4Measurement precision
If conventional linear processing is used, then the system is simple to implement, but it fails to detect multiple proximate objects accurately
Solution Approach 1:
The patent factorizes the polynomial expansion into multiple factors, each corresponding to different spatial or spectral components of the signal. This segmentation enables the system to resolve multiple proximate objects by analyzing the distinct factors, achieving superior object separation that cannot be obtained with conventional linear processing
Solution Approach 2:
The patent changes the processing operation from addition to multiplication in the complex domain. This parameter change enhances the system's ability to distinguish between multiple proximate objects because the multiplicative operation creates unique interaction patterns for each object, improving separation resolution despite increased processing method complexity
Data Source
AI summary
System, computer products, and methods can improve the resolution of data from a sensor array. One of these methods include receiving, from an analog to digital converter, a series of measurements representing frequency samples and spatial samples from a sensor array. The method includes generating a plurality of factors based on a polynomial. The method includes applying one or more complex weights to the measurements based on the factors. The method includes combining the complex weighted measurements into a plurality of values. The method also includes identifying a characteristic of an object detected by the sensor array based on the plurality of values.