Acoustic Gunshot Location Using Discrete Pulse Time Domain Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing acoustic gunshot location systems face challenges in accurately processing acoustic impulses from supersonic projectiles and muzzle blasts due to varying bullet-muzzle pulse spacings, interference from spurious pulses, and the need for high communication bandwidth and processing power, especially when sensors are far apart.
Innovation Solution
The system processes acoustic impulses by transforming initial bullet data into discrete pulses, dividing them into subsets, generating time domain representations, and determining alignment between pulse features, allowing for efficient pulse identification and alignment between sensors, using synthetic time domain representations and cross-correlation techniques to reduce bandwidth and processing requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard cross-correlation techniques are used to align signals from distributed sensors, then location accuracy may be maintained, but communication bandwidth and processing power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential pulse timing information from the full acoustic signals. Instead of transmitting and processing complete audio waveforms, the system identifies discrete pulse arrivals (bullet pulses and muzzle pulses) and transmits only their timing data for cross-correlation, dramatically reducing bandwidth while preserving location accuracy
Solution Approach 2:
The patent segments the continuous acoustic signal into discrete pulse events. By dividing the signal into individual bullet pulses and muzzle pulses with identifiable arrival times, the system enables efficient processing through cross-correlation of these segmented events rather than processing entire continuous waveforms
2Area of stationary object
If sensors are placed far apart to improve spatial coverage, then area coverage increases, but signal similarity decreases making cross-correlation less effective
Solution Approach 1:
The patent introduces synthetic time domain representations as an intermediary between the actual sensor signals and the cross-correlation process. These synthetic representations are generated from pulse timing data and serve as a common reference frame that enables effective cross-correlation even when sensors are far apart and signals are highly dissimilar
Solution Approach 2:
The patent changes the parameter basis for cross-correlation from raw audio signal amplitude and waveform characteristics to pulse arrival time differences. By transforming the problem into the time domain representation of pulse spacing rather than frequency domain audio similarity, the system maintains cross-correlation effectiveness across large sensor separations
3Productivity
If all detected pulses are used for location calculation, then data utilization increases, but location accuracy decreases due to incorrect pulse selection
Solution Approach 1:
The patent converts the potentially harmful effect of spurious pulses (echoes and reverberations) into a beneficial filtering process. By using cross-correlation to identify pulses with consistent timing relationships across sensors, the system automatically distinguishes valid bullet and muzzle pulses from spurious echoes, utilizing only reliable data for location calculation
Solution Approach 2:
The patent implements a feedback mechanism where cross-correlation results are used to validate pulse selections. The timing relationships identified through cross-correlation provide feedback that confirms whether detected pulses belong to the same shot event, enabling the system to confidently include or exclude pulses based on their consistency with the overall pattern
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 accurate gunshot location with reduced communication bandwidth and processing power, effectively handling disparate sensor signals and varying pulse spacings, while suppressing echoes and reverberations, leading to precise identification of gunshot sources.
Implementation Method 1
processing the time domain representations to determine alignment between one or more of pulse features, pulses, pairs of channels, and/or pairs of sensors
Implementation Method 2
transforming initial bullet data associated with one or more sensors into a set of discrete pulses, dividing the discrete pulses into pulse subsets, generating, for the subsets, time domain representations of the pulses
Data Source
AI summary
Systems and method are disclosed for processing signals. In one exemplary implementation, a method may include transforming initial bullet data associated with one or more sensors into a set of discrete pulses, dividing the discrete pulses into pulse subsets, generating, for the subsets, time domain representations of the pulses, wherein the time domain representations include waveforms having pulse features, and processing the time domain representations to determine alignment between one or more of pulse features, pulses, pairs of channels, and/or pairs of sensors. One or more further implementations may include determining identity of pulses in association with a matching process performed as a function of the alignment, as well as, optionally, other pulse processing features/functionality.