An essential part of satellite camera mission planning is the satellite image downlink scheduling problem (SIDSP). Customer requests are only complete when they finally receive associated satellite images. SIDSP remains an overloaded scheduling issue because of rising client demands and constrained ground resources. In this article, we examine SIDSP and present the serial scheduling approach to solving it. The concept is to first determine a variation of downlink requests before creating a plan according to sorted requests. To allocate the transmission time frame for each planned request in accordance with a specific request variation, experts suggest the schedule generation algorithm (SGA). Then, a downlink request sequence can be improved with a hybrid genetic algorithm (HGA) and neighbourhood search to maximize utility function. This might sound too complicated for many since they might think a satellite camera works on its own. However, things are not like this at all. Amongst others, there’s a satellite camera image downlink system in place.
Collecting satellite camera images and downloading or downlinking them are tasks involved in mission planning activities of Earth monitoring satellites. Effective satellite camera images and downlinking timing are essential for proper operations. Heuristic algorithms are frequently used to handle picture-capturing and downlinking processes. There are two camera systems that work together to handle these algorithms and processes. The one we will talk about here is the satellite image downlink system. Image downlinking can be done in two different ways:
● pass-through mode
● store-forward mode
A camera picture is downlinked in pass-through mode while captured. This can be accomplished only when a region to be photographed falls within the ground station’s viewing filter.
Store-forward mode is all about images taken in the past and stored in the satellite camera recorder. These images are downlinked when an opportunity presents itself.
The onboard communication system and the base reception system make up most of the satellite image downlink system. Camera transmitters and receivers are part of the onboard broadcast device. Transceivers and transmitters are part of the Earth’s reception device. The goal of a satellite camera picture transmission operation is to deliver compressed or original camera data on a schedule. This data should arrive at a ground location via satellite-ground data connections while maintaining specific bit error and code rates. Satellite image downlink scheduling can operate in one of two ways:
● each antenna can function autonomously and transmit a distinct image to one or more ground stations at once.
● during the satellite camera’s passing, images that have been observed in the on-board cache are stored, and then the data is sent to the base location.
Two of the major timing issues with satellite camera mission planning are image transmission and image capture or acquisition. A satellite that circles the Earth to take pictures and a group of fixed ground sites that receive images for processing working together can lead to the SIDSP mentioned earlier. Thus, a plan for image acquisition must be created in advance. It should also be mentioned that there are several requirements for picture downlinking. But it might not be all the time feasible to send all accessible images. As a result, queries might be rejected. Unscheduled images are those camera images not downlinked. SIDSP is concerned with determining a picture transmission plan to optimize a suitable utility function for a camera and the entire downlinking system. The camera utility function takes into account the quantity of planned downlinks, their priorities, and how early downlinking can start in relation to the timeframe. Only when a satellite equipped with an advanced camera is travelling over a station’s view shield, which is a coverage region for ground stations, a transmission action can be performed. Each base station now has more than two channels for getting a downlinked camera image, and satellites use two receivers for downlinking. However, when there’s a SIDSP, camera images don’t downlink on time. How is this problem fixed, then? Let’s see.
The method of finding an answer to SDISP in a satellite camera space vehicle’s downlink scheduling system is founded on a neighborhood-based area inquiry. Below is an explanation of how to develop an algorithm that fixes SDISP. First, here are the stages that could be followed:
● Pre-processing.
● Processing of urgent requests.
● Processing non-urgent requests.
Further, there are two options for solving the low-density data problem:
● machine-scheduled
● human-rescheduled
The method used for task planning produces machine-planned answers. Such a solution might not be practical, so a human-rescheduled solution modifies machine-scheduled solutions to fix errors and consider new factors that operators were aware of. Such information might not be exact. A human operator might plan a task even if it exceeds a specified camera visibility mask by a little because this method employs sharp visibility mask limits.
It doesn’t matter how advanced satellite cameras might be; SDISP remains an issue imagers will face. The capabilities of a space camera no longer matter without an effective algorithm that fixes the SDISP. Luckily, this algorithm is implemented and works. It would be interesting to extend this approach to multi-satellite problems and consider how they were solved for different satellite camera systems. Perhaps in another article of this kind, so keep an eye on what’s being published.
