Radar Detection Method Using Multi-Level Pulse Grouping
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Solution Overview
Problem
Current radar detection methods fail to accurately discriminate between multiple radars with similar settings, often missing some radars or incorrectly attributing modes, leading to unreliable reports of active radars in the environment.
Innovation Solution
A multi-step method involving primary, secondary, and tertiary grouping of pulses based on various characteristics such as frequency, duration, direction, and internal modulation, using algorithms like OPTICS or DBSCAN for partitioning and similarity-based merging, to precisely identify the number of radars regardless of their type or parameter settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deinterlacing algorithms are used to separate pulses from multiple radars, then pulse separation is attempted, but the algorithms fail to detect all radars or incorrectly attribute modes, leading to unreliable detection results
Solution Approach 1:
The patent segments the detection process into three distinct grouping stages: primary grouping based on pulse characteristics (frequency, duration, level), secondary grouping that merges primary groups with similarity metrics, and tertiary grouping that further refines discrimination. This multi-level segmentation allows progressive refinement of radar identification, improving both accuracy and reliability by breaking down the complex separation task into manageable stages with increasing specificity.
2Quantity of substance
If multiple radar signals are interlaced in the acquisition signal, then all radars are captured in the signal, but it becomes difficult to discriminate between different radars
Solution Approach 1:
The patent moves the discrimination process from a single-dimension approach to a multi-dimensional framework by introducing multiple grouping levels with different characteristic dimensions. Primary grouping uses basic pulse dimensions (frequency, duration, level), secondary grouping adds temporal and spectral relationship dimensions, and tertiary grouping incorporates mode attribution dimensions. This dimensional expansion transforms the difficult single-step discrimination into a systematic multi-stage process that handles interlaced signals effectively.
3Adaptability or versatility
If radars have similar settings, then they operate with comparable parameters, but discrimination between them becomes even more difficult
Solution Approach 1:
The patent applies local quality by assigning different levels of analytical depth to different groups of pulses. Primary groups are formed using basic characteristics for initial separation, secondary groups apply more sophisticated similarity metrics to distinguish similar radars, and tertiary groups perform final precise identification. This localized application of increasing analytical rigor ensures that radars with similar settings receive the detailed examination needed for accurate discrimination, while maintaining efficiency for clearly distinguishable cases.
Data Source
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AI summary
One aspect of the invention relates to a method for detecting at least one radar transmitter in an environment, the method being implemented by a device comprising a computing unit and an antenna array comprising at least one antenna capable of acquiring the environment in the form of acquisition signals and of receiving and transmitting the acquisition signals in digital form to the computing unit, the computing unit being capable of processing digitized signals, the method comprising the following steps: - Receiving and digitizing the acquisition signals in the form of digitized signals and transmitting the digitized signals to the computing unit; - Segmenting the digitized signals into pulses, characterizing each pulse to obtain primary and secondary characteristics; - Grouping the pulses into pulse blocks;- For each pulse block: • Group the pulses according to their primary characteristics to produce primary groups; • For each primary group: • Calculate the primary characteristics of primary groups based on the primary characteristics of the pulses grouped in the primary group; • Group the primary groups based on the primary characteristics of the primary groups to produce secondary groups; • Group the secondary groups based on the secondary characteristics of the pulses grouped in each secondary group to produce tertiary groups; • Detect at least one radar, with each tertiary group considered as a radar or a radar mode.