Download Ghost Toolbox 1.9
Manases Yatnalkar
I was running 'Obstacle Detection' Lab on my AWR1642ODS EVM. I saw frequent ghost detections when an obstacle was at an azimuth angle less than 30 degrees , or greater than -30 degrees( 0 degree is along y axis).
Something to try is in your ArcGIS Pro project (or a new project), create a new ArcGIS Pro toolbox, copy the model from the original toolbox and paste it in the new toolbox. Open the model in edit mode, and on the ModelBuilder tab in the Run group, click Validate, then save. Then go to Tool Properties: Model and see if the ghost parameters are still there.
The toolbox also compiles existing resources developed by partners. Products include downloadable infographics, reports, and videos in languages from the COBSEA region. More tools, case studies, and resources will be added to expand the toolbox over time.
The toolbox is developed by the Coordinating Body on the Seas of East Asia (COBSEA) with contributions from partners across the region. All tools and resources are available through the East Asian Seas Regional Node of the GPML.
The toolbox includes two original good practice case studies: Net Free Seas in Thailand, and SeaNet in Indonesia, a checklist for ghost gear removal, and videos for removal, sorting, and management of ghost gear in English and languages of the COBSEA region.
Sensory bins allow a child to explore textures through play. Discover, explore, and inspire creativity with a simple sensory bin like this ghost and bean bin. In Occupational Therapy settings, therapists might use a sensory bin like this to provide a calming period in a sensory diet or lifestyle. A sensory bin provides an opportunity for the body to slow down, much like a sensory bottle. It can be relaxing for a child who is overstimulated or hypersensitive to his or her environment. Some kids do not prefer to touch materials that are sticky, squishy, scrapey, or tickly. A sensory bin can allow a child to explore these textures and more in a confined area (a box, bin, bowl, or baby pool are some ideas.) Other kids who are easily overstimulated in their environment can benefit from a sensory play like a sensory bin be exploring the textures and getting a chance to calm down. Sensory bins can be small and perfect for hand activities, or big enough to hold the whole child. Another benefit of a sensory bin in therapy and play is that you can customize them to fit the needs of your child/children: Fine motor, visual motor, tool use, bilateral hand coordination, gross motor skills, and even learning objectives can be added to sensory bins.
Next, ghost setup runs, which will provide prompts for you to configure your new publication via the ghost config command, including creating a MySQL user, initialising a database, configure nginx and sets up SSL.
If running ghost start gives an error, try use ghost run to start Ghost without using the configured process manager. This runs Ghost directly, similar to node index.js. All the output from Ghost will be written directly to your terminal, showing up any uncaught errors or other output that might not appear in log files.
Running ghost stop stops the instance of Ghost running in the current directory. Alternatively it can be passed the name of a particular ghost instance or directory. You can always discover running Ghost instances using ghost ls.
If you have a custom log configuration the ghost log command may not work for you. In particular the ghost log command requires that file logging is enabled. See the logging configuration docs for more information.
The first two-bounce path propagates from the radar (1) to a reflecting surface (3), then to the target (2) before returning to the radar (1). Because the signal received at the radar arrives from the last bounce from the true target, it generates ghost detections along the same direction as the true target. Because the path length for this propagation is longer, it appears at a farther range than the true target detections.
The second two-bounce path propagates from the radar (1) to the target (2), then to the reflecting surface (3) before returning to the radar (1). In this case, the ghost detections appear on the other side of the reflecting surface as the radar receives the reflected signal in that direction.
The three-bounce path reflects off the barrier twice. This path never propagates directly to the target or directly back to the radar. The three-bounce ghost detections appear on the other side of the reflecting surface as the radar receives the reflected signal in that direction. Additionally, it has the longest propagation path of the three-bounce paths and therefore has the longest measured range of the three paths. This path corresponds to a mirror reflection of the true target on the other side of the barrier.
This figure reproduces the analysis of the three propagation paths. The first two-bounce ghosts lie in the direction of the target at a slightly longer range than the direct-path detections. The second two-bounce and three-bounce ghosts lie in the direction of the mirrored image of the target generated by the reflection from the barrier.
Because the range and velocities of the ghost target detections are like the range and velocity of the true targets, they have kinematics that are consistent for a tracker that is configured to track the true target detections. This consistency between the kinematics of real and ghost targets results in tracks being generated for the ghost target on the other side of the barrier.
This figure shows the confirmed track positions using square markers. The tracks corresponding to static objects (for example a barrier) are not plotted. Notice that there are multiple tracks associated with the lead car. The tracks that overlay the lead car correspond to the true detection and the first two-bounce ghost. The tracks that lie off of the road on the other side of the guardrail correspond to the second two-bounce and three-bounce ghosts.
The track velocities are indicated by the length and direction of the vectors pointing away from the track position (these are small because they are relative to the ego vehicle). Ghost detections may fool a tracker because they have kinematics like the kinematics of the true targets. These ghost tracks can be problematic as they add an additional processing load to the tracker and can confuse control decisions using the target tracks.
The local maxima of the received signals correspond to the location of the target vehicle, the guardrail, and the ghost image of the target vehicle on the other side of the guardrail. Show that measurement-level detections generated by radarDataGenerator are consistent with the peaks in the range-angle map generated by the equivalent radarTransceiver.
Multipath ghost detections can be used at times to see objects in the road that would otherwise not be detected by the radar due to occlusion. One example is the detection of an occluded vehicle due to multipath off of the road surface. Use the helperGroundBounceScenarioDSD function to create a scenario where a slower moving vehicle in the same lane as the ego vehicle is occluded by another vehicle directly in front of the radar.
Reuse the radarDataGenerator to generate ghost target detections due to multipath between the vehicles and the road surface. Use the helperRoadProfiles and helperRoadPoses functions to include the road surface in the list of targets modeled in the scenario to enable multipath between the road surface and the vehicles.
In this example, you learned how ghost target detections arise from multiple reflections that can occur between the radar and a target. An automotive radar scenario was used to highlight a common case where ghost targets are generated by a guardrail in the field of view of the radar. As a result, there are four unique bounce paths which can produce these ghost detections. The kinematics of the ghost target detections are like the detections of true targets, and as a result, these ghost targets can create ghost tracks which can add additional processing load to a tracker and may confuse control algorithms using these tracks. The radarTransceiver can be used to generate higher-fidelity IQ data that is appropriate as input to detection and tracking algorithms.
While automotive radars provide robust detection performance across the diverse array of environmental conditions encountered in autonomous driving scenarios, interpreting the detections reported by the radar can prove challenging. Sensor fusion algorithms processing the radar detections will need to be able to identify the desired target detections returned along with detections arising from road (often referred to as clutter) and multipath between the various objects in the driving scenario like guardrails and other vehicles on the road. Detections generated by multiple reflections between the radar and a particular target are often referred to as ghost detections because they seem to originate in regions where no targets exist. This example shows you the impact of these multipath reflections on designing and configuring an object tracking strategy using radar detections. For more details regarding the multipath phenomenon and simulation of ghost detections, see the Simulate Radar Ghosts Due to Multipath Return example.
Similar to the tracking algorithm, you also quantitatively analyze the performance of the radar detection classification algorithm by using a confusion matrix [2]. The rows shown in the table denote the true classification information of the radar detections and the columns represent the predicted classification information. For example, the second element of the first row defines the percentage of target detections predicted as ghosts from static object reflections.
91% of the target detections are classified correctly. However, a small percentage of the target detections are misclassified as ghosts from dynamic reflections. Also, approximately 4% of ghosts from static object reflections and 22% of ghosts from dynamic object reflections are misclassified as targets and sent to the tracker for processing. A common situation when this occurs in this example is when the detections from two-bounce reflections lie inside the estimated extent of the vehicle. Further, the classification algorithm used in this example is not designed to find false alarms or clutter in the scene. Therefore, the fifth column of the confusion matrix is zero. Due to spatial distribution of the false alarms inside the field of view, the majority of false alarm detections are either classified as reflections from static objects or dynamic objects.
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