Signal Extraction via Histogram Analysis of 1-Bit Quantized Samples
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Solution Overview
Problem
Existing systems fail to effectively extract low-level signals from high-level noisy signals, particularly in scenarios like radar signals buried in ambient noise, often requiring costly and power-hungry devices that are not suitable for outdoor or long-term use.
Innovation Solution
A method and system that sample noisy signals in series, associate samples with bins to create a histogram, and calculate signal values based on these distributions, allowing for signal extraction even at low Signal-to-Noise Ratios (SNR), using a circuitry that includes a sampler and processing unit to determine signal values through normalized histograms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional signal detection devices are used to extract low-level signals from noisy signals, then signal extraction capability is improved, but power consumption increases and device portability decreases
Solution Approach 1:
The patent segments the signal processing task into two distinct stages: (1) a low-power preprocessing stage using a 1-bit ADC to quantize the noisy signal into coarse levels, and (2) a digital signal processing stage that uses computational algorithms to extract the weak signal from the quantized data. This segmentation allows the power-intensive digital processing to work on already-quantized data rather than full-resolution data, significantly reducing overall power consumption while maintaining signal extraction capability.
Solution Approach 2:
The patent replaces the conventional approach of using high-performance analog-to-digital converters (which are power-hungry) with a simplified 1-bit quantizer followed by digital signal processing. This substitution trades analog precision for digital computation, where the signal extraction is achieved through statistical processing of the quantized samples rather than through high-precision analog conversion, thereby reducing power consumption.
2Measurement precision
If high-performance signal processing devices are used, then signal extraction accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The patent substitutes complex high-resolution analog-to-digital conversion hardware with a simple 1-bit quantizer and compensates for the reduced resolution through digital signal processing algorithms. The signal extraction accuracy is maintained by using statistical methods that process the quantized samples, replacing the need for complex analog preprocessing circuits and high-resolution ADCs.
Solution Approach 2:
The patent takes multiple copies (samples) of the quantized signal over time and processes them collectively to extract the weak signal. By accumulating and processing multiple 1-bit quantized samples through histogram analysis and statistical methods, the system achieves signal extraction accuracy that would otherwise require much more complex single-sample processing hardware.
3Reliability
If continuous high-power signal processing is used, then signal detection capability is improved, but battery life decreases
Solution Approach 1:
The patent segments the signal processing into a minimal-power quantization stage and an efficient digital processing stage, eliminating the need for continuous high-power analog signal conditioning and high-resolution conversion. This segmentation enables the system to maintain reliable signal detection while consuming enough power only for the essential quantization and digital processing operations.
Solution Approach 2:
The patent fundamentally changes the parameter of signal representation from high-resolution continuous analog values to 1-bit quantized discrete values. This parameter change reduces the power required for signal processing while maintaining detection capability through statistical analysis of the quantized samples, thereby extending battery life for portable applications.
Data Source
AI summary
A method for extracting a sought signal from a noisy signal. The method includes sampling a plurality of samples in a series of cycles of the noisy signal wherein each sample having an n-bit sampled value (n≥1), giving rise to a plurality of samples each associated with a respective cycle of the series, wherein each sample is sampled at time T relative to the origin of the respective cycle. The method further includes associating data indicative of the plurality of n-bit samples to N bins according to the corresponding sampled values, wherein N is a function of n, and calculating data indicative of a number of samples for each bin, giving rise to data indicative N-bins histogram or normalized N-bins histogram. The method further includes determining the signal value based on the data indicative of the N-bins histogram or normalized N-bins histogram.