Multi-Target Detection Using Convex Sparsity for Interfering Signals
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Solution Overview
Problem
Existing surveillance systems face challenges in detecting multiple closely spaced targets due to signal interference and computational complexity, particularly when targets have weak signals relative to noise, leading to inefficient and non-optimal detection and tracking solutions.
Innovation Solution
A convex sparsity-inducing optimization method is employed to detect multiple targets by formulating an optimization problem that can be solved without pre-enumerated trajectories, using a convex sparsity prior to promote sparse solutions and leverage the Viterbi algorithm for efficient detection, even in real-time applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object detection methods are used, then detection can be performed, but detection accuracy deteriorates when targets are closely spaced due to signal interference
Solution Approach 1:
The patent segments the detection problem by formulating it as a sparse coding problem where each target is represented by a separate code vector. By segmenting the signal space and using dictionary learning, the method can resolve closely spaced targets even when their signals interfere with each other, directly addressing the detection accuracy issue under signal interference conditions
2Reliability
If exhaustive trajectory enumeration is used, then detection completeness improves, but computational complexity increases significantly
Solution Approach 1:
Instead of performing exhaustive trajectory enumeration (excessive action), the patent uses partial action through iterative optimization. The method performs a limited number of iterations where in each iteration, only the necessary trajectory components are updated based on current estimates, achieving detection completeness without the full computational burden of exhaustive search
Solution Approach 2:
The patent changes the optimization parameters dynamically during the iterative process. By adjusting the sparsity constraint parameter and iteration count, the system achieves reliable detection while controlling computational complexity. The parameter changes allow the algorithm to converge to good solutions faster than exhaustive methods would require
3Measurement precision
If multiple frames of sensor data are processed, then detection accuracy for weak signals improves, but processing time increases
Solution Approach 1:
The patent applies continuity of useful action by processing multiple frames continuously through iterative optimization. Rather than analyzing each frame separately (which would be discontinuous and time-consuming), the method integrates information across frames within a single optimization cycle, maintaining detection accuracy for weak signals while reducing total processing time through continuous joint optimization
Data Source
AI summary
Methods and systems for detecting multiple targets from one or more senor frames. The methods and systems can include jointly detecting multiple targets from one or more sensor frames, identifying a detected path for each of the multiple targets from the one or more sensor frames, where the multiple targets include targets close enough to each other that cause noise in one or more sensor frames for detecting each of the multiple targets, and combining a convex sparsity prior value to the one or more sensor frames and incrementally removing the detected path for each of the multiple targets from the one or more sensor frames.