Radar Tracking Using Measurement Domain Coordinate Transformation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional FMCW radar systems introduce errors in range and velocity calculations due to derived signal attributes, and neglect Doppler frequency contributions, leading to inaccurate tracking and phase corruption when multiple targets merge, reducing overall tracking accuracy.
Innovation Solution
Maintaining measurement coordinates in the f(chirp+Doppler) space and using an observation matrix to convert between measurement and object coordinates, allowing for accurate noise modeling and track association, while handling crossing tracks by converting between polar or Cartesian coordinates and measurement coordinates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional FMCW radar systems use derived signal attributes for tracking updates, then the tracking process is simplified, but range and velocity calculation errors increase
Solution Approach 1:
The patent introduces an intermediary coordinate transformation process using an observation matrix that converts between measurement domain parameters (f(chirp+Doppler)) and object domain parameters (range, velocity). This intermediary transformation allows the system to maintain simplicity in tracking updates while preserving measurement accuracy by properly accounting for Doppler frequency contributions throughout the transformation process.
Solution Approach 2:
The patent changes the parameter domain by maintaining measurement coordinates in the f(chirp+Doppler) space rather than directly using derived range and velocity parameters for tracking updates. This parameter change approach allows accurate noise modeling and track association while properly incorporating Doppler frequency contributions, thereby improving measurement precision without significantly complicating the tracking process.
2Device complexity
If Doppler frequency contributions are neglected in conventional systems, then calculation complexity is reduced, but tracking accuracy deteriorates
Solution Approach 1:
The observation matrix serves as an intermediary that systematically incorporates Doppler frequency contributions during the coordinate transformation from measurement domain to object domain. This approach maintains calculation manageability while ensuring Doppler effects are properly accounted for in range and velocity determinations, thereby improving tracking accuracy without excessive complexity increase.
Solution Approach 2:
The patent replaces the conventional approach of directly calculating range and velocity (which neglects Doppler) with a substitution based on coordinate transformation through an observation matrix. This substitution method automatically incorporates Doppler frequency contributions in a systematic way, improving tracking accuracy while keeping calculation complexity manageable through matrix operations.
3Reliability
If multiple targets merge in conventional systems, then detection robustness improves, but phase corruption occurs and track integrity is lost
Solution Approach 1:
The patent segments the tracking process into distinct measurement domain and object domain operations, with the observation matrix serving as the transformation interface. This segmentation allows the system to maintain separate phase information for multiple targets in the measurement domain while performing robust detection, then accurately transform individual target parameters to the object domain without phase corruption, thereby preserving track integrity even when targets merge.
Solution Approach 2:
The observation matrix acts as an intermediary that preserves phase information during coordinate transformation. When multiple targets merge, the measurement domain representation maintains distinct phase information for each target, and the observation matrix transformation ensures this phase information is properly preserved when converting to object domain parameters, preventing track integrity loss.
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
This approach enhances tracking accuracy by directly using measured signal attributes, reducing noise propagation and improving track association, and mitigates phase corruption by maintaining track integrity in the presence of multiple targets.
Implementation Method 1
radar system 10 includes one or more radar modules 12, which process radar transmit and receive signals
Implementation Method 2
Radar sensor module 12 generates and transmits radar signals into the region of interest
Implementation Method 3
measurement coordinates in the f(chirp+Doppler) space
Data Source
AI summary
Parameters of a propagated object state in a radar tracking system are converted from an object state domain to a measurement domain. The measurement domain includes parameters of a superposition of a chirp and a Doppler frequency of the reflected signal and the Doppler frequency. Deltas between measured states and propagated states are computed in the measurement domain to improve updating of the object state. An object track is more accurately updated based on the object state delta. Data association may be performed simultaneously in both the measurement domain and object domain. Propagated object state parameters in object domain coordinates can be checked for signal collisions to avoid signal collision errors. An improved noise model is also constructed in the measurement domain.