FM/CW Radar Altimeter Noise Floor Estimation
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Solution Overview
Problem
FM/CW radar altimeters face challenges in determining noise levels, which vary with intermediate frequency, time, and temperature, requiring continuous estimation during normal operation without disrupting the altimeter's function.
Innovation Solution
A system that includes a processing unit with a high pass filter, analog to digital converter, and software instructions to continuously estimate and update the noise floor by removing target data, applying FFT, and calculating a noise threshold using a noise floor estimation function and threshold identification instructions, allowing for accurate noise floor representation and signal processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the noise level is determined from measurements containing normal ground reflection data during continuous operation, then the noise floor can be estimated without disrupting altimeter operation, but the noise level determination becomes complex due to the varying noise characteristics across different intermediate frequencies and environmental conditions
Solution Approach 1:
The patent segments the received signal data into different frequency bins through FFT processing, allowing noise analysis to be performed separately for each intermediate frequency. This segmentation enables the system to handle the varying noise characteristics across frequencies independently, resolving the complexity of determining a single noise level for the entire bandwidth while maintaining continuous operation.
Solution Approach 2:
The patent implements dynamic noise threshold adjustment by continuously updating the noise floor estimation based on current operational conditions. The noise threshold is not a fixed value but adapts in real-time to changing environmental conditions, temperature, and production variations, allowing the system to maintain accurate noise determination during continuous operation without disruption.
2Measurement precision
If the noise threshold is adjusted to account for varying noise levels across different intermediate frequencies, temperature, and production variations, then measurement precision improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The patent performs preliminary FFT processing and noise floor estimation during normal operational data collection, preparing the noise characteristics data in advance for subsequent threshold determination. By pre-processing the signal data and organizing it into frequency bins, the system reduces the computational burden during actual noise threshold calculation, achieving high measurement precision without excessive processing complexity.
Solution Approach 2:
The system uses its own operational data to determine its noise characteristics. By analyzing the ground reflection data that is already being collected during normal altimeter operation, the system self-determines its noise floor without requiring external calibration equipment or separate measurement procedures, thereby improving precision while minimizing additional processing requirements.
3Measurement precision
If target data is removed from the gathered data before noise floor estimation, then the noise threshold determination becomes more accurate, but the data processing time and computational load increase
Solution Approach 1:
The patent applies partial action by removing only the necessary target data portions that interfere with noise floor estimation, rather than processing or analyzing all data. By selectively eliminating target signals from specific frequency bins where they appear, the system achieves accurate noise threshold determination without the excessive time cost of completely reprocessing all operational data.
Solution Approach 2:
The patent extracts and removes target data from the gathered signal before performing noise floor estimation. By taking out the target components that would otherwise contaminate the noise measurement, the system achieves higher noise threshold accuracy. The extraction is performed efficiently by working with the already-FFTprocessed frequency-domain data, minimizing additional processing time.
Data Source
AI summary
Systems and methods for automatically determining a noise threshold are provided. In one implementation, a system comprises: an antenna configured to gather data about a surrounding environment; a processing unit configured to remove samples representing target data from the gathered data; to estimate the noise floor from the gathered data with the removed target data; and to determine a noise threshold from the estimated noise floor; and a memory device configured to store the estimated noise floor.