Multi-reconnaissance-platform pulse-to-pulse agile radar radiation source frequency information association method
A technology of frequency information and radiation source, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of false fusion, the frequency similarity of pulse agile radar radiation source is not very accurate, and improve the accuracy rate. Effect
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2020-03-13
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Abstract
Description
technical field
[0001] The invention belongs to the radiation source information fusion technology of multi-reconnaissance platforms, and specifically relates to a frequency information correlation method of pulse-agile radar radiation sources of multi-reconnaissance platforms. Background technique
[0002] Multi-reconnaissance platform radiation source information fusion refers to comparing the radiation source information reported by each reconnaissance platform, and judging whether the information reported by each reconnaissance platform is from the source by calculating the similarity between the radiation source frequency, pulse repetition interval, pulse width and other information on the same radar. In the fusion process of multi-reconnaissance platform radiation source information, the accuracy of parameter similarity calculation is very important. With the rapid development of electronic countermeasures technology, more and more military radars have adopted frequen...
Examples
Embodiment
[0064] A pulse-to-pulse agility radar signal is simulated with a radar signal simulator, and the detailed parameter settings are shown in Table 1.
[0065] Table 1
[0066]
[0067] figure 2 with image 3 Shown are the statistical histogram and timing diagram of the frequency samples reported by reconnaissance platform A and reconnaissance platform B, respectively. Among them, reconnaissance platform A has a higher frequency measurement accuracy and a smaller pulse loss probability (15%). Merge the frequency sample sequences reported by reconnaissance platform A and reconnaissance platform B, and use the nearest neighbor clustering algorithm to classify the combined frequency sample sequences, the categories obtained after clustering and the frequencies corresponding to the two reconnaissance platforms in each category The sample size is shown in Table 2.
[0068] Table 2
[0069]
[0070]
[0071] Construct the frequency sample interception vector X of reconnai...