Radar Target Localization via Multilateration and Velocity Correlation
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Solution Overview
Problem
Current radar systems face challenges in accurately localizing targets in a 360° environment with high separability and minimizing ambiguities, particularly when using single-channel sensors with overlapping fields of view, due to limited angular resolution and the need for multiple sensors to cover a wide field of view.
Innovation Solution
A system comprising multiple single-channel radar sensors with overlapping fields of view, where simultaneous radar signal measurements are used to derive range information, determine intersection points, and select regions of high density to estimate the most likely target position, utilizing multilateration and velocity information to improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beam forming or beam steering is used for target localization, then angular separability is improved, but the number of antenna elements and signal processing channels increases
Solution Approach 1:
The patent divides the 360° environment into multiple overlapping fields of view from different sensor locations. Each sensor handles a portion of the scene, and the results are combined through multilateration, avoiding the need for a single complex beam-forming system to cover all directions.
Solution Approach 2:
The patent introduces multilateration as an intermediary method that combines range information from multiple simple sensors to achieve accurate localization without requiring complex signal processing or phase synchronization between sensors.
2Adaptability or versatility
If a large number of beam forming sensors are used to cover 360° environment, then field of view coverage is improved, but system complexity and cost increase
Solution Approach 1:
The system segments the 360° coverage task across multiple distributed sensors with overlapping fields of view. Each sensor is simple and low-cost, but together they provide complete environmental coverage through geometric distribution rather than requiring complex individual sensors.
Solution Approach 2:
The patent uses multiple copies of simple single-channel sensors distributed in space, rather than one complex multi-channel sensor. The redundancy of having multiple simple sensors is compensated by the multilateration algorithm that fuses their measurements.
3Device complexity
If multilateration is used with distributed sensors, then device complexity is reduced, but localization ambiguities increase
Solution Approach 1:
The patent resolves ambiguities by adding the temporal dimension through velocity measurements. By combining range information (spatial dimension) with velocity information (temporal dimension), the system can disambiguate target positions that would otherwise be indistinguishable from range data alone.
Solution Approach 2:
The system uses velocity measurements as feedback to resolve ambiguities in position estimation. The velocity information provides additional constraints that help the multilateration algorithm select the correct target position among multiple possible solutions.
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 target localization precision and reduces ambiguities by correlating range and velocity data from distributed sensors, allowing for accurate estimation of target position and movement without the need for complex phase synchronization or extensive signal processing channels.
Implementation Method 1
obtain range and velocity information of the potential target from radar signal measurements
Data Source
AI summary
A device comprising circuitry configured to: obtain radar signal measurements simultaneously acquired by two or more radar sensors having overlapping fields of view, derive range information of one or more potential targets from samples of radar signal measurements of said two or more radar sensors acquired at the same time or during the same time interval, the range information of a single sample representing a ring segment of potential positions of a potential target at a particular range from the respective radar sensor in its field of view, determine intersection points of ring segments of the derived range information, determine a region of the scene having one of the highest densities of intersection points, select a ring segment per sensor that goes through the selected region, and determine the most likely target position of the potential target from the derived range information of the selected ring segments.