Wireless Object Trajectory Tracking via Likelihood Tensors
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Solution Overview
Problem
Existing wireless tracking technologies face challenges in efficiently determining and tracking the trajectories of moving objects due to high computational requirements, especially in low-power devices, and struggle with disambiguating multiple signal paths in noisy radio environments.
Innovation Solution
The method involves converting likelihood vectors from multiple sensing events into a likelihood tensor to track object trajectories by identifying maxima, using phase-based ranging and signal processing techniques like MUSIC and GCC, and employing interpolation to reduce computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional trajectory tracking methods are used, then trajectory determination can be achieved, but computational requirements become excessively high for low-power devices
Solution Approach 1:
The patent segments the trajectory tracking problem by separating likelihood vector computation from trajectory determination. The likelihood vectors are pre-computed and stored, then combined into a likelihood tensor that directly provides trajectory information, avoiding redundant computations and reducing energy consumption while maintaining real-time performance.
Solution Approach 2:
The patent performs preliminary computation of likelihood vectors during sensing events and stores them for later use. By pre-computing and caching these likelihood vectors, the system avoids expensive real-time computations when trajectory tracking is needed, significantly reducing energy consumption while maintaining fast response time.
2Measurement precision
If multiple signal paths are present in noisy radio environments, then distance measurements can be obtained, but disambiguation between line-of-sight and multipath reflections becomes difficult
Solution Approach 1:
The patent transforms the one-dimensional distance measurement problem into a two-dimensional solution by introducing velocity as an additional dimension. The likelihood tensor combines distance and velocity information, allowing the system to disambiguate between line-of-sight and multipath reflections by analyzing the temporal evolution of distance measurements across multiple sensing events, not just instantaneous distance values.
3Measurement precision
If phase-based ranging and signal processing techniques are used, then distance measurements can be obtained, but computational load increases
Solution Approach 1:
The patent creates a simplified representation (copy) of the complex signal processing results in the form of likelihood vectors and likelihood tensors. These probabilistic representations capture the essential information from complex phase-based measurements in a compressed format that is easier to process and combine, reducing computational complexity while maintaining measurement precision.
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 enables fast, real-time, and computationally efficient trajectory tracking of objects in wireless networks, disambiguating line-of-sight from multipath reflections, and reducing computational resources needed.
Implementation Method 1
high-accuracy distance measurements (HADM) using time-of-flight (ToF) channel sensing
Implementation Method 2
AoA uses multiple sensors (antennas) that exploit differences in phases of one or more unmodulated tones arriving at the sensors
Data Source
AI summary
Implementations disclosed describe techniques and systems for efficient determination and tracking of trajectories of objects in an environment of a wireless device. The disclosed techniques include, among other things, determining multiple sets of sensing values that characterize one or more radio signals received, during a respective sensing event, from an object in an environment of the wireless device. Multiple likelihood vectors may be obtained using the sensing values and characterizing a likelihood that the object is at a certain distance from the wireless device. A likelihood tensor may be generated, based on the likelihood vectors, that characterizes a likelihood that the object is moving along one of a set of trajectories. The likelihood tensor may be used to determine an estimate of the trajectory of the object.