Active Denial Security System with ML Detection and Acoustic Deterrents
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Solution Overview
Problem
Existing physical security systems are limited to observation and remote monitoring, lacking the capability to actively prevent or deter unauthorized access or behavior without human intervention.
Innovation Solution
The development of an active denial system that integrates machine learning, computer vision, 3D mapping, non-lethal countermeasures, and distributed computation to detect and deter unauthorized behavior, using technologies such as acoustic and photonic deterrents, and mobile platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional observation-based security systems are used, then system simplicity is maintained, but the capability to actively prevent unauthorized access is lost
Solution Approach 1:
The security system is divided into distinct functional modules: detection subsystem (sensors, cameras), analysis subsystem (machine learning models, computer vision algorithms), and countermeasure subsystem (acoustic, photonic, kinetic deterrents). Each module operates independently but communicates through standardized interfaces, allowing the system to gain advanced prevention capabilities while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system integrates multiple detection and countermeasure capabilities into a single platform that can handle various threat types (unauthorized access, behavioral anomalies, perimeter violations) using diverse technologies (acoustic sensors, visual cameras, LIDAR, multiple deterrent methods). This multi-functional approach enables active prevention across different scenarios without requiring separate specialized systems for each function.
2Object-affected harmful factors
If non-lethal countermeasure technologies are deployed, then safety of personnel is improved, but the system's deterrent effectiveness may be reduced
Solution Approach 1:
The system implements a progressive escalation strategy where warning signals (acoustic alerts, visual warnings) are issued before deploying stronger countermeasures. This preliminary action gives intruders multiple opportunities to comply voluntarily, reducing the need for forceful intervention while maintaining deterrent effectiveness through staged responses that increase in intensity based on intruder behavior.
Solution Approach 2:
The countermeasure system dynamically adjusts its response based on real-time threat assessment. Machine learning algorithms continuously evaluate intruder behavior, proximity, and resistance to warnings, automatically escalating or de-escalating countermeasure intensity. This dynamic adaptation ensures the minimum necessary force is applied to achieve compliance, maximizing safety while maintaining deterrent reliability.
3Loss of time
If real-time detection and response is implemented, then response time is reduced, but computational resource requirements increase
Solution Approach 1:
Machine learning models and detection algorithms are pre-trained and configured during system setup, with detection thresholds and response protocols established in advance. This preliminary configuration allows the system to make rapid real-time decisions by comparing sensor inputs against pre-computed patterns, achieving fast response times without requiring intensive computational resources during active operation.
Solution Approach 2:
Edge computing devices are deployed as intermediaries between sensors and central processing systems. These edge devices perform initial data filtering, feature extraction, and preliminary anomaly detection locally, reducing the volume and complexity of data transmitted to central servers. This intermediary processing layer enables real-time response for critical events while minimizing overall computational energy consumption.
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
The system effectively prevents, reduces, or minimizes negative behavior by actively denying access and de-escalating situations, reducing the risk of fatal injury to both security personnel and intruders, while operating within legal and policy guidelines.
Implementation Method 1
An example deterrent may be an acoustic system which emits a sound having a range of frequencies from 50 Hz-20,000 Hz
Implementation Method 2
Another embodiment of a deterrent may be a system which implements a spatial light modulation of a red, green, blue, and/or other wavelength lasers to obscure visual perception of the human
Data Source
AI summary
A method that includes (a) receiving a request to authenticate a user of an active denial management system, (b) generating a scan result of a security facility after the request, wherein the scan result includes a representation of a set of activities at a location of the security facility, (c) applying a machine learning model to the scan result such that the machine learning model determines whether the result includes at least one activity of the set of activities that is prohibited by a security policy set before the request is received, (d) performing based on the security policy and the at least one activity, an authorization of an activation of a deterrent control system expected to deter the at least one activity, (e) generating the authorization of the activation of the deterrent control system for the user, and (f) taking an action based on the authorization.