![\"\"](\"https:\/\/senstar.com\/wp-content\/uploads\/Sensor_fusion_example_site-680x383.png\")
<\/p>\nSensor fusion helps make the most of your perimeter security by maximizing the potential of all the sensors in an installation and defeating nuisance alarms. We\u2019ve talked about what it is and the benefits it provides. However, because the described benefits of Boolean logic and sensor fusion are somewhat similar \u2013 with marketing speak often obscuring the differences \u2013 it\u2019s easy to confuse these two technologies as being the same.<\/p>\n
To understand why these two systems are very different, let\u2019s look at some use cases that illuminate their differences. We\u2019ll use one example setup with some real-life situations to see how they differ.<\/p>\n
For our example, we\u2019ll use a single building with a perimeter divided into six zones:<\/p>\n
<\/p>\n
The example system is equipped with sensors that split the fence into six zones, A-F. Additionally, each zone is divided into subzones, so that every zone can report up to four distinct locations to narrow down the position of a detected disturbance.<\/p>\n
Each zone is also monitored by a camera with analytics. That is, in addition to the camera video feeds, the cameras provide a signal when they detect people within the image.<\/p>\n
The system integration using Boolean logic requires both sensor types (fence and camera) working together to reduce nuisance alarms. This is accomplished by an \u201cAND\u201d operation that combines the output of the \u201cintrusion detected\u201d fence sensor for each zone with the \u201cperson detected\u201d output for the camera monitoring that zone. That way, both the fence needs to move AND a person needs to be visible before an alarm will be reported. Wind or vegetation disturbances that move the fence without a visible person won\u2019t trigger a false alarm, likewise neither will a person walking by the fence without touching it.<\/p>\n
The sensor fusion system is similar in that it examines both sensor types to decide if there\u2019s an intrusion attempt. However, it\u2019s different in that it looks at the low-level data of each sensor and runs it through a pattern-matching algorithm that correlates the sensor outputs against each other. This data has been trained with machine learning algorithms so that it\u2019s not looking for just a moving fence cable or a person-like shape, but rather a sum of sensor signal patterns that indicate problematic intent. Thus, it\u2019s far less likely to be fooled by innocent actions that independently might appear as intrusion attempts.<\/p>\n
Here are some examples to make this difference clearer.<\/p>\n
Example #1 – Intruder During Wind<\/a><\/p>\n Example #2 – Pedestrian During Wind<\/a><\/p>\n Example #3 – Intruder with Ladder<\/a><\/p>\n Example #4 – Thunderstorm with Guard and Intruder<\/a><\/p>\n Example #5 – Intruder in a Snowstorm<\/a><\/p>\n Although these examples have all been designed to show the more sophisticated capabilities of a sensor fusion system, they\u2019re not unusual situations to encounter. The flexibility and accuracy of these sensor fusion systems gives them a huge advantage when it comes to defeating nuisance alarms without needing to sacrifice site security. In fact, site security increases with better discrimination of sensor inputs, smarter correlation between sensor data, and maximum sensor sensitivity.<\/p>\n Given the large number of potential use cases that a thorough site security plan must manage, a sensor fusion system\u2019s trustworthiness makes it a highly valuable part of any perimeter security system.<\/p>\n Senstar\u2019s new\u00a0Senstar Symphony\u2122 Sensor Fusion Engine<\/a>\u00a0is driven by the\u00a0Senstar Symphony\u2122 Common Operating Platform<\/a>.<\/strong><\/p>\n","protected":false},"excerpt":{"rendered":" Sensor fusion helps make the most of your perimeter security by maximizing the potential of all the sensors in an installation and defeating nuisance alarms. We\u2019ve talked about what it is…<\/p>\n","protected":false},"author":3,"featured_media":10954,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[22,314,315,317],"tags":[],"class_list":["post-10958","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-security-digest","category-video-management","category-video-analytics","category-fence-sensors"],"_links":{"self":[{"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/posts\/10958","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/comments?post=10958"}],"version-history":[{"count":0,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/posts\/10958\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/media\/10954"}],"wp:attachment":[{"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/media?parent=10958"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/categories?post=10958"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/senstar.com\/fr\/wp-json\/wp\/v2\/tags?post=10958"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Conclusion<\/h2>\n
Read Blog #1<\/a>
\nRead Blog #2<\/a><\/h3>\n