Drone technology (UAVs) is one of the most mind-blowing technologies that has graced in the last few decades. Drone have entered mainstream use in the last few years and boy have they taken the world by storm. Drones are now used for many functions including recording weather statistics, aerial photography and even for sports, i.e. drone racing. If it weren’t for drones, you wouldn’t be getting amazing aerial footage on your favorite sports.
Drones technology isn’t new to the world, but there is an aspect of it that is quite new. Drone radars. Until recently, drone radars did not exist, and there was no way of detecting drones in your area. Normal radars could not easily detect drones in the air. This meant immeasurable security threats to companies and even people.
When espionage was widely reported to the federal government with people citing drone usage, the need for developing a counter-drone system became larger. That is why anti-drone radar is being researched and developed to counter this security threat. If you google about security threats that drones pose to government agencies, the public, and even a whole nation, you will find a lot of stories about intruding drones which have gone undetected.
How Effective Is the Current Radar System in Detecting Drones?
Recently, a drone was found hovering around the white house. It could have gone undetected if it wasn’t for its unfortunate crash. This left the public questioning the ability of the secret service to detect and take down interfering or spying drones. It dawned on them that current radar systems might not be able to detect small drones.
Drone Detecting Radars
Specialized radars have been developed to deal with drone detection. These radars use a combination of techniques which put together noise detection, thermal detection, radio signal detection, and signal identification. However, research is still being done to develop more efficient drone detection radars.
This being said, small radars are far from efficient in detecting drones in the air. This is because creating a powerful radar system is very complex and costs a lot of money which many people cannot afford. There are reasons why drone radars sometimes cannot detect drones. These include;
• Thermal emissions from drones cannot be effectively detected since motors on drones are covered by plastic. Thermal emissions are one of how drones can be detected. Since birds have thermal emissions from their bodies, a radar can easily detect a bird instead of a drone which is quite misleading.
• Some drone radar detection systems use microphones to tap into drone noise patterns and therefore detect drones in the air. This has proved to be ineffective in urban areas due to the constant noise pollution that is present.
• Small drone detection systems have limited capabilities and hence cannot detect small drones.
Which Drone Detection Radars Can I Use?
There are several radar detection systems out there that can show you the presence of an unseen drone in your surroundings. These radars are new in the market since the counter drone radar system is also relatively new. They can show you the presence of small drones hence helping you figure out whether you are being spied on and also help you to avoid collisions with other drones just in case you were also flying your drone in your neighborhood.
After doing rounds on the internet, I came up with a list of the best drone radar systems that are worth trying out;
• Robin radar systems
• Kevin Hughes Drone Detection Systems
• SpotterRF Counter Drone Radar System
• Aerotenna Radar Sensor
I won’t get into details concerning each one of them so feel free to look up each of them. There are also some apps which are meant to detect drones in your surrounding, but their credibility has not been approved. This is because drone radar detection software is still new and there is a lot of room for improvement. When an update on such software comes up, you will be duly updated.
Drone mitigations are the efforts and mechanisms that are put in place to counter threats that malicious drones pose to individuals, homes, and organizations. They involve both passive and active. A lot of effort is being put to improve drone mitigation methods that are in place. There have been numerous reports of drones being used to perform several malicious activities such as deliver payloads, gain proximity to wireless networks to carry out espionage of cybercrimes and many others.
How do we go about drone mitigation? Well, the first step is by identifying the presence of a drone which was discussed previously. If you cannot detect the presence of an invading drone, it will be impossible to mitigate against any activities that they can carry out.
Drone mitigation can be done by a set of equipment that is called “defenders.” Defenders use computer technology to take down invading drones. They do so in a variety of ways which include;
• Jamming the invading drone’s signal
• Spoofing for GPS signals
• Hacking the drone software and signals
• Destroying the drone using shotguns, lasers, electromagnetic pulse or high energy microwave
• Manual takedowns i.e., using baseballs, soccer balls, etc.
• Using snaggers
All these are ways in which one can take down drones if they feel that they are invading their personal space. Some of these drone mitigation techniques are complex and expensive to use for civilians, i.e. using lasers and high energy microwaves. Larger organizations, on the other hand, are investing a lot of time and money in ensuring that drone mitigation efforts bear fruit. They are also conducting research to ensure that these mitigation techniques are effective and they will be well utilized in terms of a drone invasion.
Is Drone Mitigation Entirely Legal?
I know many people are probably asking themselves this question. Some of the drone mitigation techniques are crude and can lead to the destruction of property and possible damage to the surroundings. Let us say that your organization has spotted a drone in your airspace and you suspect that it is being used for espionage. Your company uses a jamming radio device to take it down, and you have mitigated the possible danger posed by the drone.
While taking down the drone isn’t entirely illegal, the device and method used might be. For example, a high-frequency radio transmitter should be registered and licensed. This means that you have just committed a crime by using an unlicensed device.
Also, such devices are illegal for use by the public which further complicates the situation. Plus, jamming a radio frequency is illegal which will land you in a lot of problems if you are caught. The above-named drone mitigation techniques might be effective but illegal which makes the whole drone mitigation process complicated and sometimes impossible.
Drone id System
The FAA decided to control and regulate the American airspace in the recent past and has plans to develop a drone identification system. It wants to acquire partners who would help it to develop a realistic approach to gathering and sharing data which will help in identifying small drones in controlled airspaces. The data would include a unique identifier for a small drone, tracking information and the drone owner and remote pilot identification.
All this data will help regulate the controlled airspace and make it safer as pilots would be required to report their intentions of flying drones. It will also help to avoid tragedies in the airspace as there will be an awareness on which small drones are in the airspace.
On the other side of the world, top Chinese drone producer DJI recently launched a drone identification and monitoring system. The system is called Aerospace and is meant to increase interactions between drones while they are in flight. Aerospace uses an existing communications link between a drone and its remote controller to broadcast identification information.
DJI expects that this system is going to improve safety among drones in airspace while also laying the foundation for effective communication among drones in flight.
Drone Radar Map
We talked about some drone radars earlier in this article, and I listed several of them. These radars are made to detect small drones in the airspace specifically. There is an additional feature that these radars come with, and it is a map which shows where the detected drones are located and where they are flying to.
I suggest that you try out some of the drone radars that I listed and find out which one maps out active drones the best. You can use your drone to test this, and you can make a conclusion on their effectiveness.
There are applications on the Google Play Store and Apple store that can also provide you with a drone map for your personal use. Some of these applications are not legitimate, and some of them do not do what the developers say they do. Read all the customer reviews before downloading an app.
The accelerometer is the electromechanical device used for the measuring of the acceleration forces. The forces can be static such as gravity or a continuous force. Acceleration is thus defined generally as measuring of speed or velocity that is divided by the use of time.
A drone accelerometer is a device used for measuring proper acceleration. Coordinate acceleration is the acceleration of a body in a coordinate system that is fixed. Proper acceleration is defined as the acceleration of the body in the body’s instantaneous resting frame. Any typical quadcopter will be equipped with a unit for inertial navigation. The unit is made up of three gyroscopes, three magnetometers, and three accelerometers. For determination of altitude, it will also contain an ultrasonic sensor for proximity for measuring of altitude and is used indoors plus a barometer for outdoors. There are at times that a GPS receiver or camera will be used.
Gyro-stabilization of the drone
The main purpose of the gyroscope technology is the improvement of flight capabilities of the drone. The algorithms, software and hardware work in coordination with each other so as to improve every flight aspect including taking of turns at steep angled or perfect hovering. There are drones which have flight controllers to improve vast capabilities. The gyroscope ought to almost instantly work in relation to forces that move to oppose the drone so that it remains stabilized. The gyroscope will provide navigational information that is essential to control systems for the central flight.
About drone accelerators
MEMS accelerometers are made up of a mass system that is damped on spring and has been etched in the silicon. As an accelerometer gains momentum, the mass moves with respect to the casing. The movement will be measured by the use of electrodes. The measurement of the signal is analogue; thus there is a signal processing sequence for converting the signal to digital. The signal which is digital is then converted to the acceleration, with m/s2 as the SI unit, through the application of calibration parameters that are pre-defined.
For perfect drone flying technology of the flight controller and gyro stabilization are quite essential. The gyro stabilization provides information for flight controllers. This makes drone flying to be easy and very easy. Gyro stabilization is an important component that allows a drone is flying to be smooth including in gusts and strong winds. The smooth drone flight capabilities allow filming of aerial views that are fantastic.
Accelerometers contain a feature for power saving that is advanced thus allowing ideal choices for applications with power requirements that are ultra-low. The features include a function for auto wake-up, the mode for low-power and the FIFO buffer which will be used for data storage thereby reduction of loading of the host processor and the consumption of the system power. Exceptional stabilization of a drone in flight together with the waypoint navigation will enable a drone to produce imagery and photogrammetry maps that are 3D and of high top quality. Modern drones will use gimbals that are integrated that as well includes the technology of gyro stabilization which is inbuilt thus giving onboard sensors or camera which is a practical movement that is vibration free. It allows capturing of the perfect aerial photos and films.
Inertial sensors rely on Allan graphs for Variance for evaluation and characterization of MEMS design plus the chain for signal processing. The graphs are typically meant for the studying of the oscillators’ frequency stability. Statistical characteristics of random processes can be inferred to thus their analysis by the use of the Variance Curve. Characterization graphs for Allan Variance are used in deriving values for the data sheets (technical) of the random effects and stability of bias. Random effects are also referred to as white noise.
Any system integrator will pay attention to the rate of a random walk, also known as white noise, bias instability plus the velocity of a random walk for the accelerometers. The velocity of the random walk is an equivalent of integral of the random effects in the output of the accelerometer.
The velocity of the random walk: its slope will be -0.5 due to the random fluctuation in the signal having a correlation time that is shorter as compared to the sample rate. Density values with low noise are desired whenever the interest is on signals with low amplitude.
Bias instability: it’s represented using a flat portion from curve bottom. It is also known as in-run stability of bias. It indicates minimum bias whose estimation is impossible or challenging.
The rate of a random walk: it has a spectral density for power which falls as one per squared frequency. It represents fluctuations due to the bias that is long term mainly due to the effects of the temperature.
Choice of an accelerometer in a drone is dependent on the drone’s intended use. Calibrating accelerometers enable output of information that is useful. Calibration methods and algorithms are also known as a scale factor. Calibrating can be for the gain, offsetting bias, misalignment of the cross axis for 3D assembly and offsetting plus gaining of dependency of temperature.
Implementations for successful implementation employ measurements of an accelerometer for effective improvement of estimates in the orientation of the quadrotor. An aerial vehicle that is unmanned ought to explain how accelerometer data can be used appropriately. In a flight that is manually controlled data can be recorded from an accelerometer that is time stamped. Accelerator measurements are obtained from sensors onboard of the drone, and the measurements pose a capture system for motion. The error between actual and predicted measurements of the accelerometer is modeled with Gaussian, and zero mean. Accelerometer measurements relate to pitch angle and roll angle on the quadcopter.
For a quadcopter to reach a stable state, the time taken will be approximately three seconds to ten seconds. Before validity of altitude that is accelerometer based, a quadcopter will have reached a speed which will degrade the onboard data sensor or even make collisions in an environment that is likely to be cluttered. Accelerometers are used in correcting estimates in the measurement update. An accelerometer in an aerial vehicle that is unmanned will measure the scaled velocity. Rachel is used to denoting covariance of accelerator measurement.
A quadrotor will experience pitch angles of more than forty-five degrees. Gyroscope measurements will provide information to be used for fast angle changes and accurate corrections of accelerometer that will lead to a constrain drift. Altitude estimates will drift rapidly if gyroscope measurements are trusted for fast-tracking of angular changes. For drift constraining, accelerometer measurements should be sufficiently weighed. Use of an inappropriate dynamic model will result in a performance which is inferior. Modification of filters for tuning of parameters was not meant for the flight segment that also highlights the robustness of estimators that are proposed.
When per taking maneuvers that is aggressive, better performance will be achieved by tuning of complementary filters and traditional FG. Alternatively, gaining a scheduling approach or adaptive control can be implemented on filters for the improvement of the estimates for flight regimes that will be broader, but there will be an increase in complexity. The filter will use the update for accelerometer measurement and gyroscope for estimation of velocity and altitude.
Correct modeling of accelerometer measurements will have significant impacts on the altitude, velocity and position estimates for the aerial vehicles that are unmanned. Fusing an accelerometer and a sensor that is exteroceptive will increase the performance of an aerial vehicle as compared to the use of a sensor that is exteroceptive only. An accelerometer will measure specific acceleration. This is the difference found between gravitational acceleration and acceleration of an aerial vehicle that is unmanned.
Underlying assumptions to measure vector gravity have flaws when they are applied on a quadrotor. On making a design for a quadrotor estimator, assuming static equilibrium will be too restrictive. Forces that act on the quadcopter will sum to zero after long periods or at hover at an altitude that is fixed. Use of a linear gain filter that is fixed on the basis of the drag force for an enhanced model so long as there is a trade-off that exists between performance and complexity. Thus, each will provide an improvement in the altitude estimates as in comparison to the typical approaches, especially when doing maneuvers that are aggressive while ensuring the provision of information that is significant on the velocity. The velocity estimates will provide estimates for the position that is based on the dead reckoning which will lead to slow and relative divergence.
It measures the orientation of an aerial craft that is unmanned relative to the surface of the earth. Its working principle is sensing of acceleration of the gravity by the use of the same technology that is also used in the gyroscopes. Micro Systems of Electro-Mechanical interface to the electronics that allow the engineers to build stuff on a space that is extremely small.
Factors to consider when choosing a drone accelerometer
• Non-linearity; in drones it can be caused by the geometry of proof mass, mounting of capacitive measurement.
• Repeatability of bias: software mitigation means that bias of the accelerometer is done in the framework of sensor fusion.