As small unmanned aerial systems (UAS) are introduced into the national airspace, measures must be introduced to ensure that they do not interfere with manned aviation and other UAS. Radar provides an attractive solution because of its inherent range accuracy and because it works in diverse weather and lighting conditions. Traditional radar systems, however, are large and high power and do not meet the size, weight and power (SWaP) constraints imposed by UAS, and fully integrated automotive solution do not provide the necessary range. This thesis proposes a compact radar system that meets both the SWaP and range requirements for UAS and can act as a standalone sensor for a sense and avoid system (SAA). The system meets the field of view requirements motivated by the UAS sensing problem (120deg x 30deg) and tracks targets in range and azimuthal angle using a four element phased array receiver. The phased array receiver implements real time correlation and beamforming using a field programmable gate array (FPGA) and can track multiple targets simultaneously. Excluding antennas, the radar transceiver and signal processing platform weighs approximately 120g and is approximately the size of a whiteboard eraser (2.25in x 4in x 1in), which meets the payload requirements of many small (<25kg) UAS. To our knowledge, this is the first real time phased array radar that meets the sensing and SWaP requirements for small UAS.Our testing was done with the radar system on the ground, aimed at airborne UAS targets. Using antennas with a gain of 12 dB, and 800 milliwatts of transmitted power, the system detects UAS targets with a radar cross section of less than 0.1 square meters up to 150 meters away. The ground based system demonstrates radar detectability of extremely small UAS targets, and is scalable to further ranges by increasing antenna gain or adding additional elements. Based on our success in detecting airborne UAS, we conclude that radar remains a feasible option for a UAS collision avoidance sensor.

<p>This thesis investigates the crucial problem of collision avoidance for autonomous vehicles.  An anti-collision system for an unmanned aerial vehicle (UAV) is studied in particular. The purpose of this system is to make sure that the own vehicle avoids collision with other aircraft in mid-air. The sensor used to track any possible threat is for a UAV limited basically to a digital video camera. This sensor can only measure the direction to an intruding vehicle, not the range, and is therefore denoted an angle-only sensor. To estimate the position and velocity of the intruder a tracking system, based on an extended Kalman filter, is used. State estimates supplied by this system are very uncertain due to the difficulties of angle-only tracking. Probabilistic methods are therefore required for risk calculation. The risk assessment module is one of the essential parts of the collision avoidance system and has the purpose of continuously evaluating the risk for collision. To do this in a probabilistic way, it is necessary to assume a probability distribution for the tracking system output. A common approach is to assume normality, more out of habit than on actual grounds. This thesis investigates the normality assumption, and it is found that the tracking output rapidly converge towards a good normal distribution approximation. The thesis furthermore investigates the actual risk assessment module to find out how the collision risk should be determined. The traditional way to do this is to focus on a critical time point (time of closest point of approach, time of maximum collision risk etc.). A recently proposed alternative is to evaluate the risk over a horizon of time. The difference between these two concepts is evaluated. An approximate computational method for integrated risk, suitable for real-time implementations, is also validated. It is shown that the risk seen over a horizon of time is much more robust to estimation accuracy than the risk from a critical time point. The integrated risk also gives a more intuitively correct result, which makes it possible to implement the risk assessment module with a direct connection to specified aviation safety rules.</p>

<p>I en obemannad flygfarkost måste datorer ta över pilotens förmåga att värdera risker och undvika kollisioner. På algoritmnivå brukar man dela in problemet i tre delar: Upptäckt och estimering av inblandade farkosters positioner och hastigheter, kollisionsriskberäkning och slutligen undanmanöver.</p><p>Saabs arbete med obemannade farkoster har tidigare berört kollisionsundvikning lite ytligt men nu börjat på större allvar. Det här examensarbetet är en del i denna satsning och har resulterat i ett sätt att beräkna kollisionsrisken samt ett sätt att beräkna en undanmanöver, givet att de inblandade farkosternas positioner och hastigheter är kända. </p><p>I examensarbetet behandlas parvisa kollisionsscenarier mellan ickekommunicerande farkoster givet två olika fall. Dels där den främmande farkostens position skattats väl, dels där den främmande farkostens position skattats sämre. En enkel simuleringsmiljö har utvecklats, där två algoritmer för beräknandet av kollisionsrisken, en för varje fall, testats samtidigt som undanmanövern testats för en mängd kollisionsscenarier. Givet att den främmande farkostens position skattats väl behöver den obemannade farkosten cirka 6 s på sig för att kunna undvika en kollision. I fallet där den främmande farkostens position skattats sämre kan vi beräkna kollisionsrisken och i vissa fall sluta oss till hur farkosterna är orienterade och därigenom göra ett undanmanöverval.</p> / <p>Saabs work with unmanned aerial vehicles has only scratched the surface of collision avoidance, but is now advancing. This master thesis sheds light on some parts of the collision avoidance problem and has resulted in an innovative way to calculate the risk of collision and a way to determine an avoidance maneuver.</p><p>In this master thesis collision scenarios between non-communicating vehicles are being looked upon in pairs, given two different sets of data. Good estimates of the unknown vehicles position and unsatisfying position estimate. Through the development of a simple simulation environment, two algorithms, one for each set of data, has been tested simultaneously with tests of the collision avoidance maneuver for several collision scenarios. Given a good estimate of the unknown aerial vehicles position, the unmanned vehicle need approximately 6 seconds to act to avoid a collision. For the case with unsatisfactory estimate of the unknown vehicle the risk of collision can be calculated and in some cases the orientation of the aerial vehicles and thus a choice of avoidance maneuver can be made.</p>

This thesis investigates the crucial problem of collision avoidance for autonomous vehicles.  An anti-collision system for an unmanned aerial vehicle (UAV) is studied in particular. The purpose of this system is to make sure that the own vehicle avoids collision with other aircraft in mid-air. The sensor used to track any possible threat is for a UAV limited basically to a digital video camera. This sensor can only measure the direction to an intruding vehicle, not the range, and is therefore denoted an angle-only sensor. To estimate the position and velocity of the intruder a tracking system, based on an extended Kalman filter, is used. State estimates supplied by this system are very uncertain due to the difficulties of angle-only tracking. Probabilistic methods are therefore required for risk calculation. The risk assessment module is one of the essential parts of the collision avoidance system and has the purpose of continuously evaluating the risk for collision. To do this in a probabilistic way, it is necessary to assume a probability distribution for the tracking system output. A common approach is to assume normality, more out of habit than on actual grounds. This thesis investigates the normality assumption, and it is found that the tracking output rapidly converge towards a good normal distribution approximation. The thesis furthermore investigates the actual risk assessment module to find out how the collision risk should be determined. The traditional way to do this is to focus on a critical time point (time of closest point of approach, time of maximum collision risk etc.). A recently proposed alternative is to evaluate the risk over a horizon of time. The difference between these two concepts is evaluated. An approximate computational method for integrated risk, suitable for real-time implementations, is also validated. It is shown that the risk seen over a horizon of time is much more robust to estimation accuracy than the risk from a critical time point. The integrated risk also gives a more intuitively correct result, which makes it possible to implement the risk assessment module with a direct connection to specified aviation safety rules.

I en obemannad flygfarkost måste datorer ta över pilotens förmåga att värdera risker och undvika kollisioner. På algoritmnivå brukar man dela in problemet i tre delar: Upptäckt och estimering av inblandade farkosters positioner och hastigheter, kollisionsriskberäkning och slutligen undanmanöver. Saabs arbete med obemannade farkoster har tidigare berört kollisionsundvikning lite ytligt men nu börjat på större allvar. Det här examensarbetet är en del i denna satsning och har resulterat i ett sätt att beräkna kollisionsrisken samt ett sätt att beräkna en undanmanöver, givet att de inblandade farkosternas positioner och hastigheter är kända. I examensarbetet behandlas parvisa kollisionsscenarier mellan ickekommunicerande farkoster givet två olika fall. Dels där den främmande farkostens position skattats väl, dels där den främmande farkostens position skattats sämre. En enkel simuleringsmiljö har utvecklats, där två algoritmer för beräknandet av kollisionsrisken, en för varje fall, testats samtidigt som undanmanövern testats för en mängd kollisionsscenarier. Givet att den främmande farkostens position skattats väl behöver den obemannade farkosten cirka 6 s på sig för att kunna undvika en kollision. I fallet där den främmande farkostens position skattats sämre kan vi beräkna kollisionsrisken och i vissa fall sluta oss till hur farkosterna är orienterade och därigenom göra ett undanmanöverval. / Saabs work with unmanned aerial vehicles has only scratched the surface of collision avoidance, but is now advancing. This master thesis sheds light on some parts of the collision avoidance problem and has resulted in an innovative way to calculate the risk of collision and a way to determine an avoidance maneuver. In this master thesis collision scenarios between non-communicating vehicles are being looked upon in pairs, given two different sets of data. Good estimates of the unknown vehicles position and unsatisfying position estimate. Through the development of a simple simulation environment, two algorithms, one for each set of data, has been tested simultaneously with tests of the collision avoidance maneuver for several collision scenarios. Given a good estimate of the unknown aerial vehicles position, the unmanned vehicle need approximately 6 seconds to act to avoid a collision. For the case with unsatisfactory estimate of the unknown vehicle the risk of collision can be calculated and in some cases the orientation of the aerial vehicles and thus a choice of avoidance maneuver can be made.

Unmanned aerial systems (UAS) are an influential technology which can enhance life in multiple ways. However, they must be able to sense and operate safely with manned aircraft. Radar is an attractive sensor for UAS because of its all-weather performance. It is challenging, though, to meet the size, weight, and power (SWaP) limitations of UAS and especially small-UAS while still maintaining the needed sensing capability. A working FMCW radar prototype has been created which meets the SWaP requirement of small-UAS. A simulation model for the radar was developed to test the processing algorithms of the radar and proved to be advantageous in that purpose. An automatic target detection algorithm was also successfully developed to allow the radar to identify targets of interest in a cluttered and dynamic environment. Fixed-wing airborne tests have been performed with the radar which show that the radar meets the SWaP requirements of small-UAS. They also show the prototype requires a higher sensitivity to detect other small-UAS. A successful redesign of the radar's receivers was done to make the radar more sensitive.

No description available.

Unmanned aircraft systems (UAS) represent the future of modern aviation. Over the past 10 years their use abroad by the military has become commonplace for surveillance and combat. Unfortunately, their use at home has been far more restrictive. Due to safety and regulatory concerns, UAS are prohibited from flying in the National Airspace System without special authorization from the FAA. One main reason for this is the lack of an on-board pilot to "see and avoid" other air traffic and thereby maintain the safety of the skies. Development of a comparable capability, known as "Sense and Avoid" (SAA), has therefore become a major area of focus. This research focuses on the SAA problem as it applies specifically to small UAS. Given the size, weight, and power constraints on these aircraft, current approaches fail to provide a viable option. To aid in the development of a SAA system for small UAS, various simulation and hardware tools are discussed. The modifications to the MAGICC Lab's simulation environment to provide support for multiple agents is outlined. The use of C-MEX s-Functions to improve simulation performance and code portability is also presented. For hardware tests, two RC airframes were constructed and retrofitted with autopilots to allow autonomous flight. The development of a program to interface with the ground control software and run the collision avoidance algorithms is discussed as well. Intruder sensing is accomplished using a low-power, low-resolution radar for detection and an Extended Kalman Filter (EKF) for tracking. The radar provides good measurements for range and closing speed, but bearing measurements are poor due to the low-resolution. A novel method for improving the bearing approximation using the raw radar returns is developed and tested. A four-state EKF used to track the intruder's position and trajectory is derived and used to provide estimates to the collision avoidance planner. Simulation results and results from flight tests using a simulated radar are both presented. To effectively plan collision avoidance paths a tree-branching path planner is developed. Techniques for predicting the intruder position and creating safe, collision-free paths using the estimates provided by the EKF are presented. A method for calculating the cost of flying each path is developed to allow the selection of the best candidate path. As multiple duplicate paths can be created using the branching planner, a strategy to remove these paths and greatly increase computation speed is discussed. Both simulation and hardware results are presented for validation.

Location information is vital for any type of aircraft and even more crucial for Unmanned Aerial Systems (UAS). GPS is a readily available solution but signals can easily be jammed or lost. In this thesis, radar is explored as a backup system for self-localization when GPS signals are not available. The method proposed requires that an area be pre mapped by collecting radar data with known latitude and longitude coordinates. New radar data is then collected and compared to previously stored values. Channel matrices are stored at each point and are used as the basis for location comparisons. Various methods of matrix comparison are used and both simulation as well as experimental results are shown. The main results of this thesis show that position can be determined using channel matrices if the sensor is within a certain radius of previously stored locations. This radius is on the order of a wavelength or less. Using correlation matrix comparisons the radius of localization is broadened. A novel method using multiple channel and multiple frequency data proves to be successful and determines the position of an octorotor UAS with a mean position error of less than three meters. The design of a low-cost, compact, and light-weight FMCW radar for two applications is also presented. The first application is a novel radar based positioning system that utilizes multiple channel and multiple frequency information to determine position. The second application is a UAS sense and avoid system using a monopulse configuration. Without connectors or antennas, the radar weighs 45.7 grams, is 7.5 cm x 5 cm x 3 cm in size, and costs less than $100 when built in quantities of 100 or more (excludes antennas and connectors). It is tested using delay lines and corner reflectors and accurately determines the distance to close range targets.

No description available.