Sensor Control System Using Ising Model Annealing for Real-Time Object Assignment
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Solution Overview
Problem
Assigning multiple moving objects to multiple sensors is a combinatorial optimization problem that becomes challenging with increasing numbers of sensors and moving objects, making it difficult to calculate all combination patterns in real-time using existing methods like greedy algorithms.
Innovation Solution
A sensor control system that constructs Ising model data to optimize sensor resource assignment, maps this data to an annealing machine to obtain assignment results, and controls sensors to acquire assigned moving objects, using techniques like QUBO-style optimization to minimize missed acquisitions and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general optimization algorithms (greedy algorithm, genetic algorithm, branch-and-bound method, annealing method, auction algorithm) are used to solve the combinatorial optimization problem of assigning multiple moving objects to multiple sensors, then optimization accuracy can be improved, but calculation time increases and real-time performance deteriorates
Solution Approach 1:
The patent segments the complex combinatorial optimization problem into two parts: (1) constructing an Ising model that represents the assignment problem with binary variables, and (2) solving the Ising model using a dedicated annealing machine. This segmentation allows the problem to be solved more efficiently by leveraging specialized hardware for the computation-intensive part.
Solution Approach 2:
The patent replaces traditional software-based optimization algorithms (genetic algorithm, branch-and-bound method, etc.) with a hardware-based annealing machine that solves the Ising model. This substitution of computational mechanism enables real-time solving of the combinatorial optimization problem by utilizing quantum or thermal annealing processes in dedicated hardware.
2Adaptability or versatility
If the number of sensors, moving objects, and driving range of sensors increases, then system capability and coverage are improved, but the complexity of calculating all combination patterns increases exponentially
Solution Approach 1:
The patent changes the representation parameters of the optimization problem by mapping sensor assignment variables to binary spin variables in the Ising model. This parameter transformation allows the system to handle increased numbers of sensors and moving objects efficiently, as the Ising model solver can process the binary variable representation at constant time complexity regardless of problem size.
3Reliability
If traditional optimization algorithms are used for sensor resource assignment, then comprehensive optimization can be achieved, but the processing speed is insufficient for real-time applications
Solution Approach 1:
The patent introduces the Ising model as an intermediary representation between the sensor assignment problem and the annealing machine solver. This intermediary formulation allows the system to maintain optimization completeness while achieving real-time processing speeds, as the Ising model captures all constraints and objectives in a form that the annealing machine can solve efficiently.
Data Source
AI summary
The input means 81 accepts input of position of a sensor that acquires a moving object and direction of the sensor, as well as position of the moving object. The model construction means 82 constructs Ising model data that models an optimization problem to optimally assign a moving object to be acquired by the sensor from a relationship between a position of the moving object and an area that can be acquired based on a position of the sensor and a direction of the sensor. The optimization processing means 83 maps the Ising model data to an annealing machine to obtain an execution result indicating a moving object to be assigned to the sensor. The control means 84 controls the sensor to acquire an assigned moving object based on the execution result.