Radar Signal Processing Using Mixed Density Function Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional radar return signal processing systems fail to accurately discriminate between target signals and clutter signals when they have equivalent frequency changes, leading to incorrect elimination of target signals and impaired observation of precipitation and wind velocity.
Innovation Solution
A radar return signal processing apparatus that acquires a frequency spectrum determined by average Doppler frequency, spectrum width, and received power, and estimates an optimum mixed density function by learning modeling the frequency spectrum, using a likelihood function with penalties based on prior knowledge to accurately separate target and clutter signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If a low-pass filter with preset cutoff frequency is used to eliminate clutter signal, then clutter signal elimination is achieved, but target signal may be incorrectly eliminated when target and clutter have equivalent frequency changes
Solution Approach 1:
The patent changes the filtering approach from fixed frequency-based filtering to parameter-based filtering using average Doppler frequency and spectrum width. By estimating these parameters and comparing them against predefined ranges for clutter versus target signals, the system adapts the filtering criteria to the actual signal characteristics rather than using a static cutoff frequency, thereby preserving target signals with equivalent frequency changes.
Solution Approach 2:
The patent introduces dynamic parameter estimation and comparison mechanisms that adapt to varying signal conditions. Instead of a static low-pass filter, the system dynamically calculates average Doppler frequency and spectrum width for each echo group, compares these against clutter-specific ranges, and selectively eliminates only those echoes matching clutter characteristics, making the filtering process adaptive and signal-specific.
2Device complexity
If conventional frequency-based filtering is applied, then processing simplicity is maintained, but accurate separation of target and clutter signals with equivalent frequency changes becomes impossible
Solution Approach 1:
The patent extends the parameter space from simple frequency filtering to multi-parameter analysis including average Doppler frequency and spectrum width. By incorporating spectrum width as an additional discriminative parameter alongside average Doppler frequency, the system achieves better signal separation without proportionally increasing complexity, as these parameters are derived through systematic estimation procedures.
Solution Approach 2:
The patent segments the signal processing into distinct stages: frequency spectrum acquisition, parameter estimation (average Doppler frequency and spectrum width), clutter range determination, and selective echo elimination. This segmentation organizes the processing complexity into manageable modules, making the enhanced discrimination capability achievable through structured, stepwise processing rather than a monolithic complex algorithm.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate discrimination and extraction of target signals from clutter, even when they have equivalent frequency changes, allowing for precise observation of precipitation and wind velocity.
Implementation Method 1
Weather radar exploits the Doppler effect exhibited by an electromagnetic wave to determine wind velocity by analyzing the Doppler frequency of a radar return signal.
Data Source
Figure 1
Figure 2~3
Figure 4~5
AI summary
According to one embodiment, a radar return signal processing apparatus includes an detection means (S11), an estimation means (S12, S13) and an extraction means (S14). The detection means (S11) detects an average Doppler frequency, a spectrum width, and a received power of each of echoes, from the radar return signal obtained repeatedly at regular intervals. The estimation means (S12, S13) estimates an optimum mixed density function by learning modeling a shaped of the frequency spectrum by calculating repeatedly a sum of density functions of each of the echoes. The extraction means (S14) extracts information on any one of the echoes included in the radar return signal, from a parameter of the estimated mixed density function.