Virtual Array Information Matrix for High Resolution Beamforming
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Solution Overview
Problem
Existing radar systems face challenges in achieving high angular resolution while minimizing computational burden, especially in distinguishing correlated targets in complex environments.
Innovation Solution
A method and system that utilize a semi-randomized signal permutation and reference selection algorithm to construct a virtual array information matrix, allowing for the separation of correlated input signals and the refinement of coarse angle estimations using low-computational complexity operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the aperture size is increased via introducing more antenna elements, then the angular resolution is improved, but the computational burden increases significantly
Solution Approach 1:
The patent segments the signal processing into two distinct stages: a coarse estimation stage using traditional beamforming and a refinement stage using the proposed semi-randomized signal permutation algorithm. This segmentation allows the system to achieve high angular resolution without requiring excessive computational resources in a single processing step, thereby resolving the contradiction between resolution improvement and computational burden.
Solution Approach 2:
The patent applies preliminary action by first obtaining coarse angle estimations using conventional beamforming methods before applying the more computationally intensive semi-randomized signal permutation algorithm for refinement. This preliminary coarse estimation reduces the computational complexity required in the subsequent refinement stage, enabling high angular resolution with reduced overall computational burden.
2Measurement precision
If digital and software methodologies are used to increase angular resolution, then the resolution is improved, but the processing time and computational cost increase
Solution Approach 1:
The patent divides the signal processing into two stages: coarse estimation using traditional beamforming and refinement using the proposed algorithm. This segmentation enables the system to achieve super-resolution accuracy while maintaining acceptable processing times, as the computationally intensive operations are applied only after obtaining initial estimates, thus resolving the contradiction between resolution improvement and processing time.
Solution Approach 2:
The patent performs preliminary coarse angle estimation using conventional beamforming before applying the more time-consuming semi-randomized signal permutation algorithm. This preliminary action reduces the computational time required for high-resolution processing, as the algorithm only needs to refine the angles rather than compute them from scratch, thereby reducing overall processing time while maintaining high resolution.
3Measurement precision
If matrix decomposition operations are applied to distinguish targets, then the resolution is improved, but the hardware complexity and memory consumption increase
Solution Approach 1:
The patent changes the mathematical approach from traditional matrix decomposition methods to a semi-randomized signal permutation algorithm that operates on transformed signal vectors. This parameter change in the mathematical methodology reduces the hardware complexity and memory requirements while maintaining the capability to distinguish between targets, as the new approach uses simpler operations on reduced-dimensional data representations.
Solution Approach 2:
The patent creates a virtual array information matrix that is a transformed copy of the original signal data, rather than working directly with the full original data through complex decompositions. This copying approach reduces the effective dimensionality and complexity of the data that needs to be processed, thereby reducing hardware complexity and memory consumption while maintaining target distinction capability.
4Adaptability or versatility
If correlated targets are present in the scene, then the complexity of the environment is increased, but the ability to distinguish targets deteriorates
Solution Approach 1:
The patent applies local quality by processing signal components corresponding to different target locations independently through the semi-randomized signal permutation algorithm. By treating each target's signal contribution separately in the refined estimation stage, the algorithm can distinguish between correlated targets that occupy different spatial locations, thereby maintaining high target separation accuracy even in complex environments with correlated targets.
Solution Approach 2:
The patent performs preliminary coarse angle estimation for all targets in the scene before applying the refinement algorithm. This preliminary action establishes initial separations between targets, and the subsequent refinement stage then enhances these separations by processing the signals with the semi-randomized permutation algorithm, which effectively handles correlated targets by leveraging the initial estimates to guide the refinement process.
Data Source
AI summary
A system and method to receive correlated input signals from targets from antennas in a linear or planar antenna array, receive coarse estimations of azimuth and/or elevation angles for the targets, apply a semi-randomized signal permutation and reference selection algorithm to the correlated input signals to construct a virtual array information matrix (VAIM) by shifting a reference point of each of the correlated input signals in a different manner from one another to separate the correlated input signals from one another in a signal space in the VAIM, and filter the VAIM to independently extract information of the coarse estimations from the VAIM for each specific azimuth angle or elevation angle to transform the VAIM to provide filtered output signals corresponding, respectively, to filtered versions of each of the coarse estimations of the at least one of azimuth angles and elevation angles for each of the targets.