Adaptive Signal Processing Resource Allocation for Radar
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Solution Overview
Problem
Real-time signal processing of high signal loads often faces challenges due to limited computational resources, necessitating efficient data processing methods for streaming signal data, particularly in radar detection scenarios where high performance algorithms require significant computational power.
Innovation Solution
A method for adaptive signal processing resource allocation, where streaming signal data is processed using a first computational resource for initial analysis and a second resource for detailed analysis based on contextual relationships, allowing for efficient allocation of resources and use of high-performance algorithms only where necessary, with the option to store data for later reanalysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high performance signal processing algorithms are applied to all streaming signal data, then measurement precision and reliability are improved, but use of energy and computational resources are excessive
Solution Approach 1:
The patent segments the signal processing task into two distinct stages: a first stage that processes all streaming signal data using a first algorithm, and a second stage that processes only selected subsets using a second high-performance algorithm. This segmentation allows the system to apply computational resources selectively rather than uniformly, reducing overall energy consumption while maintaining measurement precision for critical data portions.
Solution Approach 2:
The patent applies local quality by using different processing algorithms in different spatial or temporal regions of the data stream. The first algorithm is applied broadly to all data, while the second high-performance algorithm is applied locally only to selected subsets that meet specific criteria. This ensures high measurement precision is concentrated where most needed rather than uniformly distributed across all data.
2Use of energy by moving object
If simplified signal processing algorithms are used to conserve computational resources, then use of energy is reduced, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent implements dynamic algorithm selection where the processing approach adapts based on the characteristics of the data subset. The system dynamically determines which subsets require the high-performance second algorithm versus those sufficient for the first algorithm. This dynamic adjustment allows the system to maintain high measurement precision for critical subsets while conserving computational resources for less critical portions.
Solution Approach 2:
The patent changes the processing parameters (algorithm complexity) based on the specific characteristics of different data subsets. By evaluating subset properties and adjusting the processing intensity accordingly, the system optimizes the balance between measurement precision and computational resource consumption, applying high-performance processing only where parameters indicate it is necessary.
3Measurement precision
If high performance algorithms are applied to all data subsets, then measurement precision is improved, but productivity decreases due to excessive computational load
Solution Approach 1:
The patent segments the data stream into multiple subsets and applies different processing algorithms to different segments. The first algorithm processes all segments quickly, while the second high-performance algorithm processes only selected segments in detail. This segmentation enables the system to maintain high productivity by avoiding the application of computationally intensive algorithms to the entire data set, while still achieving high measurement precision for critical segments.
Solution Approach 2:
The patent applies partial action by using the high-performance second algorithm on only a portion of the data subsets rather than all of them. This selective application ensures that measurement precision is enhanced where necessary while maintaining overall processing throughput and productivity, avoiding the excessive computational load that would result from applying high-performance algorithms uniformly.
4Productivity
If data is processed in real-time without storage, then productivity is maintained, but adaptability decreases when reanalysis is needed
Solution Approach 1:
The patent performs preliminary processing of all streaming signal data through the first algorithm before potential secondary analysis. This preliminary action creates an initial processing layer that can be quickly executed in real-time, while also preparing the data for potential subsequent analysis using the second high-performance algorithm. This preliminary processing maintains productivity while enabling adaptability for reanalysis when needed.
Solution Approach 2:
The patent maintains continuity of useful action by implementing a multi-stage processing pipeline where the first algorithm continuously processes all incoming data in real-time, and selected subsets are continuously evaluated for potential second-stage processing. This continuous multi-layered approach ensures both real-time productivity and the adaptability to perform additional analysis on selected subsets without interrupting the ongoing data stream processing.
Data Source
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AI summary
The present disclosure relates to a method for signal processing resource allocation. The method comprises receiving (S10) streaming signal data. The method further comprises, at a first computational resource, determining (S20) a first data subset of the streaming signal data based on a selection criterion, the selection criterion having a contextual relationship with a predetermined scenario. The method also comprises determining (S25) a second data subset of the received streaming signal data based on the first data subset and the contextual relationship. The method additionally comprises, at a second computational resource, analysing (S30) the second data subset using an algorithm based on the contextual relationship. The method further comprises forming (S40) aggregated signal data based on the received streaming signal data and the analysed second data subset. The method yet further comprises outputting (S50) data based on the aggregated signal data. The present disclosure also relates to corresponding systems and computer programs.