Jekyll2023-09-15T07:50:17+00:00https://debrief.github.io/feed.xmlDebrief WebsiteDebrief Maritime Analysis Tool – Powerful, Fast, Free, and IntuitiveNew Feature: Splitting Tracks2019-11-19T09:52:08+00:002019-11-19T09:52:08+00:00https://debrief.github.io/debrief-help/Splitting-Tracks<p>Learn more about handling periods of missing data in Debrief</p>
<h1 id="missing-data">Missing data</h1>
<p>By default, Debrief joins all track points with straight lines.</p>
<p>But, when there is a period of missing data, the viewer of a report may
mistakenly think the platform is on steady course and speed.</p>
<p>In circumnstances such as these, it would make more sense to split a track into several segments (legs).</p>
<p>A split in the track can be recognised by a period of missing data. Depending on the frequency of the data, a gap may be represented by one minute or one hour of missing data.</p>
<p>Here is an example of a track with missing periods of data.</p>
<p><img class="img-fluid" src="/assets/images/TrackWithJumps.png" alt="Tracks with jumps" /></p>
<p>By splitting the track into segments, we can convey the periods of missing data.</p>
<h1 id="right-click-menu">Right-click menu</h1>
<p>We’ve added a new right-click menu option for tracks. On selecting <code class="language-plaintext highlighter-rouge">Split track into segments...</code> a drop-down menu of time interval sizes is offered:
<img class="img-fluid" src="/assets/images/SplitTracksMenu.png" alt="split tracks menu" /></p>
<p>Once clicked, any selected tracks are inspected, being split into segments whenever there is a period of missing data greater than the provided threshold.</p>
<p>Here’s a split track.</p>
<p><img class="img-fluid" src="/assets/images/SplitTrack.png" alt="split tracks" /></p>
<h1 id="formatting-helper">Formatting helper</h1>
<p>Right-clicking on a track is fine for individual tracks, but if you’re processing a high volume of tracks, you may wish to auto-split them on missing data.</p>
<p>Learn more about them here: <a href="https://debrief.github.io/tutorial/reference.html#replay_format_annotations"> https://debrief.github.io/tutorial/reference.html#replay_format_annotations</a></p>
<p>This can be supported by inserting <code class="language-plaintext highlighter-rouge">Formatting Helper</code> instructions into a <code class="language-plaintext highlighter-rouge">.rep</code> file. These are commands that advice Debrief on how to process data loaded from that file.</p>
<p>See the instruction below:</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>;SPLIT_TRACK: One_Hour 3600000 PLATFORM_HOST
</code></pre></div></div>
<p>This instructs Debrief to look out for any track called <code class="language-plaintext highlighter-rouge">PLATFORM_HOST</code>, and split it whenever there is a gap of 3.6 million milliseconds (that’s one hour in new money).</p>
<div class="language-plaintext highlighter-rouge"><div class="highlight"><pre class="highlight"><code>;SPLIT_TRACK: 48_Hours 172800000
</code></pre></div></div>
<p>This instructs Debrief to split any track when there is a gap of more than 48 hours from the previous measured position.</p>ianmayoLearn more about handling periods of missing data in DebriefDetermining version of Java2019-03-06T09:52:08+00:002019-03-06T09:52:08+00:00https://debrief.github.io/debrief-help/determining-version-of-Java<p>On occasion it’s useful to determine the version of Java that a Debrief installation is using.</p>
<p>Follow this process to determine the version:</p>
<ol>
<li>Open Debrief</li>
<li>Click on <code class="language-plaintext highlighter-rouge">Help</code> / <code class="language-plaintext highlighter-rouge">About Debrief NG</code></li>
<li>From the dialog that opens, click on <code class="language-plaintext highlighter-rouge">Installation details</code></li>
<li>The <code class="language-plaintext highlighter-rouge">DebriefNG installation details</code> dialog will open</li>
<li>Open the <code class="language-plaintext highlighter-rouge">Configuration</code> tab</li>
<li>Scroll down to the <code class="language-plaintext highlighter-rouge">-arch</code> entry. If this ends in <code class="language-plaintext highlighter-rouge">_64</code> then it’s a 64-bit java installation, otherwise it’s 32-bit.</li>
</ol>
<p><img class="img-fluid" src="/assets/images/JVM_Version_Screenshot.png" alt="Viewing JVM Version" /></p>ianmayoOn occasion it’s useful to determine the version of Java that a Debrief installation is using.New Feature Pre-release: Use of scripting to update data2019-01-15T12:12:08+00:002019-01-15T12:12:08+00:00https://debrief.github.io/releases/technical-demonstration/introduction-of-scripting-in-debrief<p>After a couple of months of development, we're preparing the release of the new Debrief scripting ability</p>
<p>This new capability is intended to enable Debrief Power Users to take on more advanced data manipulation tasks,
including:
<ul>
<li>Apply bulk changes to Debrief objects</li>
<li>Import unexpected data-types from file</li>
<li>Conduct ad-hoc calculations on Debrief data</li>
</ul>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/vJ1dbZbbI5k" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe>
</div>
</p>ianmayoAfter a couple of months of development, we're preparing the release of the new Debrief scripting ability This new capability is intended to enable Debrief Power Users to take on more advanced data manipulation tasks, including: Apply bulk changes to Debrief objects Import unexpected data-types from file Conduct ad-hoc calculations on Debrief dataNew Feature Release: Paste Rep from Clipboard2018-09-27T12:12:08+00:002018-09-27T12:12:08+00:00https://debrief.github.io/releases/technical-demonstration/new-feature-release-paste-rep-from-clipboard<p>We're coming to the end of another new feature.</p>
<p>Most of the time Debrief data comes in digital form, captured on another system. But, occasionally it arrives in text form and has to be transcribed. Some users do this within Debrief, others perform the task in a text editor such as Notepad, then save the content and open in Debrief.</p>
<p>This process has been made quicker by allowing analysts to write content in Notepad, then copy/paste it into Debrief's outline view.</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/tDvfxrHpXYg" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe>
</div>ianmayoWe're coming to the end of another new feature. Most of the time Debrief data comes in digital form, captured on another system. But, occasionally it arrives in text form and has to be transcribed. Some users do this within Debrief, others perform the task in a text editor such as Notepad, then save the content and open in Debrief. This process has been made quicker by allowing analysts to write content in Notepad, then copy/paste it into Debrief's outline view.New Feature: SVG Symbols2018-09-21T12:10:08+00:002018-09-21T12:10:08+00:00https://debrief.github.io/releases/technical-demonstration/new-feature-svg-symbols<p>Symbols are used to add value to analysis plots, through giving a visual indication of the vehicle type.</p>
<p>In the past, the symbols have been hard-coded in Debrief, but now Debrief supports the provision of vectored symbols in the industry standard SVG format.</p>
<p>The first set of symbols is shown below.</p>
<p>Please feel free to request any other commonly required symbols, together with a pointer to an example of what it should look like, and we'll get our designer onto it.</p>
<p> </p>
<p><img class="spotlight" src="/assets/images/new-feature-svg-symbols-green.png" alt="new feature: svg symbols" aria-busy="true" /></p>
<p> </p>
<p><img class="spotlight" src="/assets/images/new-feature-svg-symbols-red.png" alt="New Feature: SVG Symbols" aria-busy="false" /></p>ianmayoSymbols are used to add value to analysis plots, through giving a visual indication of the vehicle type. In the past, the symbols have been hard-coded in Debrief, but now Debrief supports the provision of vectored symbols in the industry standard SVG format. The first set of symbols is shown below. Please feel free to request any other commonly required symbols, together with a pointer to an example of what it should look like, and we'll get our designer onto it. New Release: Sprint 4: Export to PPT2018-08-03T12:08:13+00:002018-08-03T12:08:13+00:00https://debrief.github.io/releases/technical-demonstration/new-release-sprint-4-export-to-ppt<p>With the conclusion of Sprint 4, the first major work package ("Epic" in Scrum language) is coming to completion, Export to PPT. In a multi-threaded package of work different members of the team have:</p>
<ul>
<li>designed PowerPoint master slides</li>
<li>developed Python code to iteratively work through the various processes involved in transforming Debrief screen pixels into MS PowerPoint entities</li>
<li>Design a tidy, sensible UX that leads the user through the export process</li>
<li>Transform the working Python into Java, then wrap that code in unit and integration tests</li>
<li>Develop the new UI elements, and integrate the new Java export code</li>
</ul>
<p>The implementation has 1/2 dozen minor features to be added/refined, which will slip the final release into next week - but a screencast of the working implementation is shown below:</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe width="100%" height="315" src="https://www.youtube.com/embed/7YfkHyQ42ws" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>ianmayoWith the conclusion of Sprint 4, the first major work package ("Epic" in Scrum language) is coming to completion, Export to PPT. In a multi-threaded package of work different members of the team have: designed PowerPoint master slides developed Python code to iteratively work through the various processes involved in transforming Debrief screen pixels into MS PowerPoint entities Design a tidy, sensible UX that leads the user through the export process Transform the working Python into Java, then wrap that code in unit and integration tests Develop the new UI elements, and integrate the new Java export code The implementation has 1/2 dozen minor features to be added/refined, which will slip the final release into next week - but a screencast of the working implementation is shown below:New Feature Release: Better multi-static analysis capabilities2018-08-03T12:05:04+00:002018-08-03T12:05:04+00:00https://debrief.github.io/releases/technical-demonstration/new-feature-release-better-multi-static-analysis-capabilities<p>To support the development of multi-static analysis capabilities, we've added the ability to introduce 3 actors in the frequency optimisation process: transmitter, subject, and receiver.</p>
<p>See more in the video below:</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/yt5CZmiUOqM" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe>
</div>ianmayoTo support the development of multi-static analysis capabilities, we've added the ability to introduce 3 actors in the frequency optimisation process: transmitter, subject, and receiver. See more in the video below:Demo: Create New Narrative Entries from UI2018-07-30T12:02:46+00:002018-07-30T12:02:46+00:00https://debrief.github.io/debrief-help/technical-demonstration/demo-create-new-narrative-entries-from-ui<p> The main way for narrative data to get into Debrief is by the translation of system recording into .REP format.</p>
<p>But, on occasion a Debrief analyst wishes to enter one or more narrative entries directly into Debrief.</p>
<p>Historically this has always been possible right-clicking on the Narratives folder in the Outline View, or by right-clicking into empty space if there isn't (yet) a Narratives folder.</p>
<p>From today it's also possible to add a new narrative entry directly from the Narrative Viewer. See the following screencast for more detail.</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/evFCfol-guA" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>ianmayo'The main way for narrative data to get into Debrief is by the translation of system recording into .REP format. But, on occasion a Debrief analyst wishes to enter one or more narrative entries directly into Debrief. Historically this has always been possible right-clicking on the Narratives folder in the Outline View, or by right-clicking into empty space if there isn't (yet) a Narratives folder. From today it's also possible to add a new narrative entry directly from the Narrative Viewer. See the following screencast for more detail.New Feature in Development: Support for multiple primary sensors2018-07-25T12:00:56+00:002018-07-25T12:00:56+00:00https://debrief.github.io/new-feature-in-development/technical-demonstration/new-feature-in-development-support-for-multiple-primary-sensors<p>Analysts have always been able to use Target Motion Analysis (TMA) algorithms to predict missing track data using passive sensor data.</p>
<p>Traditionally, this has only allowed use of a single sensor. Now analysts would like to triangulate a track using multiple sensors.</p>
<p>This trial release will let you do just that, to perform manual TMA.</p>
<p>Use scenario: Say a platform has hull-mounted and towed sensors. This video shows how analysts can use both data points in Debrief’s existing manual TMA facilities, with the embryonic Semi-Automatic TMA Construction to create a TMA solution that’s very close to the truth.</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/-7eh5RMTqdo" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>ianmayoAnalysts have always been able to use Target Motion Analysis (TMA) algorithms to predict missing track data using passive sensor data. Traditionally, this has only allowed use of a single sensor. Now analysts would like to triangulate a track using multiple sensors. This trial release will let you do just that, to perform manual TMA. Use scenario: Say a platform has hull-mounted and towed sensors. This video shows how analysts can use both data points in Debrief’s existing manual TMA facilities, with the embryonic Semi-Automatic TMA Construction to create a TMA solution that’s very close to the truth.New Feature in Development: Manual TMA using sensor measurements from more than one platform2018-07-24T11:58:23+00:002018-07-24T11:58:23+00:00https://debrief.github.io/new-feature-in-development/technical-demonstration/new-feature-in-development-manual-tma-using-sensor-measurements-from-more-than-one-platform<p> Part of the package of new features to support multi-statics is to allow manual TMA to be conducted using sensor measurements from more than one platform.</p>
<p>This video shows how sensor data from two floating sensor buoys can be used to triangulate a straight leg of positional data.</p>
<p>The changes to support this have affected the deep internals of Debrief's TMA processing, so we've got a few more days of integration testing to do before it's ready for prime-time, but we're on track for it to be available next week.</p>
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item" width="560" height="315" src="https://www.youtube.com/embed/-03Iccf-1iY" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>
</div>ianmayoPart of the package of new features to support multi-statics is to allow manual TMA to be conducted using sensor measurements from more than one platform. This video shows how sensor data from two floating sensor buoys can be used to triangulate a straight leg of positional data. The changes to support this have affected the deep internals of Debrief's TMA processing, so we've got a few more days of integration testing to do before it's ready for prime-time, but we're on track for it to be available next week.