When aUASoperates in an urban environment, it may have a set of exteroceptive sensors only available in certain locations with measurement accuracy that varies due to the chang-
ing geometry of the surrounding buildings and obstacles. While these sensors use actual
GPSorLTEsignals to calculate their measurements, they may or may not have the ability to determine and report the accuracy of these measurements to the state estimation filter.
One method to generate accuracy values for these sensors is to use empirical accuracy data
UAS has a priori knowledge of an accurate map of building locations and heights as ex- pected measurement accuracy for sensors such asGPSandLTE will vary as a function of building heights and distances apart. This assumption is reasonable given the availability
of building and map database information online today.
Relative locations are discretized into categories based on street-level position within a
city block structure (horizontal plane) and altitude with respect to urban canyon buildings
(vertical plane). Each horizontal-vertical position category is assigned specific GPS and
LTEaccuracies based on the literature. Using a feature known as Sensor Accuracy Mode (SAM), two different scenarios are explored. The first scenario presumes each sensor has
the ability to determine its own accuracy. In this scenario the propagated horizontal-vertical
position is used to look up sensor accuracy values used in both the generation of the sensor
measurements and the state estimation filter. In the second scenario the sensors do not have
the capability to generate their own accuracy values. In this case, the estimated horizontal-
vertical position is used to determine the sensor accuracy filters for the state estimation
filter with some uncertainty. However, in this second scenario, measurement generation
still uses the same filter parameters and process as in the first scenario since these values
are based on simulated ground truth data.
4.1.1
Categorizing Street-Level Position
The street-level or horizontal position categorization, or simply S L, is assigned three pos-
sible values listed below and shown in Figure6.3(b). 1. Urban Canyon: S L − 1
2. Intersection: S L − 2
3. Adjacent Open Space: S L − 3
When theUASis within an urban canyon, defined by a block with buildings on either side of street, it is in the S L − 1 category, as shown in Figure4.1(a). HereUASnavigation
relies more on VISION, LTE, andGPS when available for measurements. Upon exiting the canyon at the end of a block, theUAS enters an intersection, S L − 2 category, shown in Figure4.1(b). This environment is more open than the canyon, allowing increasedGPS
availability and accuracy, increased LTE accuracy, but no VISION availability. UAS are in the third category, S L − 3, when adjacent to an open space on one side of the street
and buildings on the other side as shown in Figure 4.1(c). This environment allows for betterGPSavailability and accuracy than S L − 1, but lowerGPSavailability and accuracy than S L − 2. In S L − 3, VISION is still available, but only gives measurements based on
the one side with adjacent buildings. Open space on both sides within the urban canyon
is not specifically given its own category as this is very similar to an intersection. When
conducting urban missions, the UASwill encounter all three categories and must be able to gain as much measurement information as possible for use in state estimation.
The inputs for the S L categorization algorithm include UAS longitudinal position, canyon number, and building information for the current canyon. With these inputs, the
algorithm uses a series of tests to determine S L category at the current time step as shown
in Figure4.2.
4.1.2
Categorizing Altitude with respect to Buildings
The altitude with respect to buildings categorization, or simply ALT , has three possible
values listed below and shown in Figure4.3. 1. Above the tops of all buildings: ALT − 1
2. Above the tops of some buildings: ALT − 2
(a) Canyon (S L − 1)
(b) Intersection (S L − 2) (c) Adjacent Open Space (S L − 3)
Figure 4.1: Street-level position categories (top view).
When theUASis above the tops of all buildings (ALT − 1), as shown in Figure4.3(a), it relies heavily on accurateGPSposition and airspeed estimates for navigation since a large view of the sky is available. As the UAS descends into a canyon, it may fly above the tops
of some shorter buildings but still be below the tops of other skyscrapers. This is known
as above the tops of some buildings (ALT − 2), as shown in Figure4.3(b). In this situation,
GPSavailability and accuracy begins to degrade as the signals no longer have line of sight to theUAS, but VISION availability increases. Once theUAS has descended fully into a canyon, it is below the tops of all buildings (ALT − 3), as shown in Figure4.3(c). Here,
1. Is theUAS longitudinal position in a canyon from the last time step AND North of the South side of the southernmost building in the current canyon AND South of the North side of the northernmost building in the current canyon?
(a) Yes: Is there open space on one side? i. Yes: S L − 3 - Adjacent Open Space ii. No: S L − 1 - Urban Canyon
(b) No: S L − 2 - Intersection
2. Is theUASin an intersection from the last time step but now adjacent to buildings? (a) Yes: Increment canyon number
i. Is there an open space on one side? A. Yes: S L − 3 - Adjacent Open Space B. No: S L − 1 - Urban Canyon
(b) No: S L − 2 - Intersection
Figure 4.2: Street Level Position Categorization Algorithm.
the UAS may completely lose GPS availability, but may have more consistent VISION availability with buildings on either side of the street now visible. Being able to navigate
safely in this category is important for theUASto execute its low-altitude urban missions. The inputs for the altitude categorization algorithm, shown in Figure 4.4, include the current S L category, UAS altitude, current canyon number, and building information for both the current canyon and the upcoming canyon. Figure 4.5 shows the UAS traveling through the intersection and its four bordering buildings (numbered 1 − 4) referenced in the
algorithm.