Adaptive Radar Calculator Dynamic Waveform Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current automotive radars are designed for human intervention and are not well-suited for truly autonomous vehicles, requiring complex and time-consuming multidimensional design processes due to their reliance on predefined waveforms, which are inadequate for dynamic scenarios without human oversight.
Innovation Solution
A processor-based system that dynamically adjusts radar operational parameters using multiple software models to evaluate and combine data from various criteria, such as vehicle and environmental states, to optimize radar modes, including range, spectral efficiency, phase, and angular coverage, allowing for real-time adjustments to improve radar performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If predefined waveforms are used in current automotive radars, then the radar system is simpler to design initially, but it cannot adapt to dynamic scenarios without human oversight and requires complex multidimensional design processes
Solution Approach 1:
The patent implements dynamic waveform generation by replacing predefined static waveforms with software models that continuously adapt radar parameters based on real-time environmental conditions, vehicle state, and detected objects. The system dynamically adjusts waveform characteristics including frequency, pulse width, and modulation patterns to optimize performance for each specific scenario.
Solution Approach 2:
The system changes multiple radar parameters simultaneously including frequency, pulse width, power level, and waveform type based on environmental conditions and detection requirements. This multi-parameter adaptation allows the radar to optimize its operation for different scenarios such as urban driving, highway cruising, or adverse weather conditions.
2Reliability
If multiple parameters are adjusted for radar mode design, then radar performance is optimized, but the design process becomes time-consuming and difficult
Solution Approach 1:
The radar system performs self-optimization through software models that automatically evaluate environmental conditions and select optimal parameters without requiring manual design intervention. The system continuously monitors its performance and autonomously adjusts parameters to maintain optimal detection capability, eliminating the need for time-consuming manual tuning sessions.
Solution Approach 2:
The system incorporates feedback loops where detection results and environmental sensor data are continuously fed back to the software models, which then adjust radar parameters in real-time. This closed-loop control enables the radar to learn from its performance and automatically optimize parameters based on actual operating conditions rather than relying on pre-designed parameter sets.
3Extent of automation
If predefined waveforms are used, then initial design is simpler, but the radar cannot effectively operate in truly autonomous vehicles without human intervention
Solution Approach 1:
The patent creates a universal software architecture that handles multiple radar functions including waveform generation, parameter optimization, environmental sensing integration, and detection algorithm selection. This multi-functional software core replaces numerous specialized components and enables the radar to operate autonomously across diverse scenarios without requiring human intervention or scenario-specific hardware modifications.
Data Source
AI summary
The present disclosure is directed to a plurality of different software models allowing a processor to perform calculations associated with different sets of criteria using data associated with variables that have a strong correlation with one or more of the different criteria sets. Methods consistent with the present disclosure may use several different sets of software that include instructions associated with the collection and evaluation of data associated with a vehicle and with an automated driving system. Different criteria sets may be associated with a range of operating modes, spectral content of received signals, phases of radar signals, and an angular coverage/field of view of the radar apparatus. Results generated by each of the software models may allow a processor to assign weights to the results generated by the different models to generate a combined result that in turn is used to update an operational mode of the radar apparatus.