Verify the operability of the UAV ADS-B receiver

Abstract

In some embodiments, techniques are provided for verifying the operability of a broadcast automatic dependent surveillance - broadcast (ADS - B) receiver included in a first unmanned aerial vehicle (UAV), which include receiving ADS - B data representing ADS - B messages broadcast by traffic within the reception range of the ADS - B receiver during a first time period, estimating a traffic environment of a service area at least partially spanning a first operating area of the first UAV during the first time period, determining, based on the estimated traffic environment, the traffic expected to be observed by the first UAV during the first time period, and verifying the operability of the ADS - B receiver of the first UAV based on a comparison between the traffic expected to be observed by the first UAV and the traffic associated with the ADS - B data received by the ADS - B receiver of the first UAV.

Description

Technical Field

[0001] The present disclosure generally relates to Automatic Dependent Surveillance - Broadcast (ADS - B) receivers, and particularly but not exclusively to the operability verification of unmanned aerial vehicle (UAV) ADS - B input receivers. Background Art

[0002] Automatic Dependent Surveillance - Broadcast (ADS - B) is a satellite - derived aircraft positioning system that combines an aircraft's positioning source, aircraft avionics, and ground infrastructure to create an accurate surveillance interface between the aircraft and air traffic control. ADS - B devices are generally classified as ADS - B Out and ADS - B In, corresponding respectively to the ability to output ADS - B data and the ability to receive ADS - B data. For example, an aircraft equipped with an ADS - B Out transponder can broadcast ADS - B data indicating the aircraft's position, altitude, and velocity vector at a higher rate than traditional radar - based surveillance systems, enabling accurate, real - time, and dynamic tracking of the broadcast aircraft.

[0003] ADS - B data can be particularly important for unmanned aerial vehicles (UAVs), which have generally become increasingly popular and offer opportunities for the transportation of goods between physical locations (e.g., from retailer to consumer). A UAV is a vehicle capable of traveling without a physically present human operator and may be capable of at least partially autonomous operation. When a UAV is operating in a remote - control mode, a pilot or driver at a remote location can control the UAV by sending commands to the UAV via a wireless link. When a UAV is operating in an autonomous mode, the unmanned vehicle typically moves based on pre - programmed navigation waypoints, dynamic automation systems, or a combination thereof. Additionally, some unmanned vehicles can operate in both remote - control and autonomous modes and, in some cases, can do so simultaneously. For example, a remote pilot or driver may wish to leave navigation to the autonomous system while manually performing another task. Brief Description of the Drawings

[0004] Non - limiting and non - exhaustive embodiments of the present invention are described with reference to the following drawings, in which like reference numerals represent like parts throughout the views unless otherwise indicated. Not all instances of an element are necessarily labeled so as not to clutter the appropriate drawing. The drawings are not necessarily drawn to scale; rather, the emphasis is on illustrating the described principles.

[0005] Figure 1An aerial map of a geographical area at a certain moment according to an embodiment of the present disclosure is shown.

[0006] Figure 2 A flowchart showing an ADS-B receiver that continuously monitors multiple UAVs via a traffic estimator and an ADS-B health monitor according to an embodiment of the present disclosure is shown.

[0007] Figure 3 A flowchart showing the verification of ADS-B operability using an ADS-B monitor according to an embodiment of the present disclosure is shown.

[0008] Figure 4 A functional block diagram of a computing system including multiple UAVs, an external computing device, and a third-party data provider according to an embodiment of the present disclosure is shown. Detailed Description

[0009] Embodiments of systems, devices, and methods for verifying the operability of an unmanned aerial vehicle (UAV) ADS-B receiver are described herein. In the following description, numerous specific details are set forth to provide a thorough understanding of the embodiments. However, those skilled in the relevant art will recognize that the techniques described herein may be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations have not been shown or described in detail to avoid obscuring certain aspects.

[0010] Certain portions of the detailed description that follows are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, considered to be a self-consistent sequence of steps leading to a desired result. These steps require physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical or magnetic signals capable of being stored, transferred, combined, compared, and otherwise manipulated. For the sake of generality, it has proven convenient at times to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, etc.

[0011] However, it should be remembered that all these and similar terms are related to appropriate physical quantities and are merely convenient labels applied to these quantities. Unless otherwise specified, it is understood from the following discussion that throughout the description, discussions using terms such as "receiving", "providing", "estimating", "determining", "verifying", "combining", "splitting", "generating", "identifying", "marking", "adjusting", "mapping", "displaying", "aborting", etc. refer to the actions and processes of a computer system or similar electronic computing device that manipulate and transform data represented as physical (e.g., electronic) quantities within the registers and memories of the computer system into other data similarly represented as physical quantities within the memories or registers of the computer system or other information storage, transmission, or display devices.

[0012] The algorithms and displays presented herein are not inherently related to any particular computer or other device. In accordance with the teachings herein, various general-purpose systems may be used with the programs, or it may prove convenient to construct more specialized devices to perform the required method steps. The structures required for various such systems will appear in the following description. In addition, no reference is made to any particular programming language to describe embodiments of the present disclosure. It will be understood that a variety of programming languages may be used to implement the teachings of the present disclosure described herein.

[0013] References to "an embodiment" or "embodiments" throughout this specification mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the invention. Thus, the appearances of the phrases "in an embodiment" or "in embodiments" throughout this specification are not necessarily all referring to the same embodiment. Moreover, in one or more embodiments, the particular features, structures, or characteristics may be combined in any suitable manner.

[0014] Figure 1An aerial map 100 of a geographic area at an instance of time in accordance with an embodiment of the present disclosure is shown. The aerial map 100 shows a portion of a service area (e.g., a predefined geographic area) for a drone delivery service, where a plurality of UAVs 101 operate between different locations in a semi-autonomous or fully autonomous manner and provide transportation and delivery of goods. Each of the plurality of UAVs may be equipped with an ADS-B receiver that is capable of receiving ADS-B messages broadcast by nearby aircraft (e.g., manned aircraft or other aircraft equipped with an ADS-B output transponder, such as aircraft 190-A and 190-B) within the reception range of a given UAV (e.g., 170-A of the first UAV 101-A at time T1 and 170-B of the second UAV 101-B at time T1), which can be used for traffic and / or collision avoidance between a given UAV among the plurality of UAVs 101 and the aircraft 190. For example, when the ADS-B message received by one of the plurality of UAVs indicates that the aircraft 190 is less than a threshold distance from the UAV 101, the UAV 101 may adjust its flight path, land, or otherwise change its course to at least maintain the threshold distance between the UAV 101 and the aircraft 190.

[0015] An aircraft 190 with ADS-B output capability can automatically broadcast ADS-B messages (e.g., containing a unique identifier, latitude, longitude, altitude, speed, velocity vector, etc.) at a predetermined rate (e.g., once per second) and a predetermined radio frequency (e.g., 1090 MHz or 968 MHz) to notify aircraft (e.g., any one of the multiple UAVs 101) within a reception range (e.g., 200 nautical miles, but also depending on the geography of the service area) of their location information in substantially real time. For example, at time T1 in the illustrated embodiment, the first UAV 101-A is delivering a package from the launch site 110 to the driveway of Estate A along the flight path 150-A, while the second UAV 101-B is delivering a package from the launch site 110 to the backyard of Estate B along the flight path 150-B. At time T1, the first UAV 101-A can receive an ADS-B message from the aircraft 190-A but not from the aircraft 190-B because the aircraft 190-B is outside the reception range 170-A of the ADS-B receiver of the first UAV 101-A. However, the second UAV 101-B receives the ADS-B message via the ADS-B receiver used for both aircraft 190-A and 190-B because both aircraft are within the reception range 170-B of the second UAV 101-B at time T1. As described above, the ADS-B message can provide critical information related to the positions of nearby aircraft, which can be used by the multiple UAVs 101 to ensure safe operation.

[0016] In some embodiments, the ADS-B receiver of one or more of the multiple UAVs 101 can be an ADS-B input-only receiver (e.g., capable of receiving ADS-B messages but not sending ADS-B messages), or can operate in an ADS-B receive-only state (e.g., to avoid saturation of the ADS-B frequency). Since it can be expected that the ADS-B receiver will be operable throughout the entire lifespan of the multiple UAVs 101, automatic and real-time health checks may be desirable to ensure that each UAV is reliably receiving ADS-B messages from nearby traffic. Advantageously, regular health checks of the multiple UAVs enable timely identification, repair, replacement, and / or calibration of defective ADS-B receivers, antennas coupled to the ADS-B receivers, or other defective components associated with the ADS-B receivers. However, for UAVs that cannot broadcast ADS-B messages, verifying the operability of their ADS-B receivers is not straightforward because a transmit-receive self-check may not be available or desirable.

[0017] Embodiments of a real-time monitor are described herein that can cross-check operability by correlating ADS-B input data received by multiple UAVs 101 and / or using other known “true” sources of traffic reported by ADS-B within the service areas of the multiple UAVs 101, verifying the operability of the ADS-B receivers of each of the multiple UAVs 101, thereby providing a low-cost and real-time health monitoring solution for a UAV fleet. Health monitoring can be performed continuously, periodically, and at any stage of UAV operation (e.g., as a pre-flight health check, in-flight health check, and / or post-flight health check).

[0018] It should be understood that real-time health monitoring can be performed on any vehicle having an ADS-B receiver, including any number of UAVs (e.g., a first UAV 101-A and a second UAV 101-B), manned aircraft, or other vehicles operating within the service area. It should also be understood that the bird's-eye view 100 shown is scaled for discussion purposes (e.g., showing the overlapping and non-overlapping portions of the reception ranges 170 of multiple UAVs 101 relative to the geometric area and aircraft 190 shown) and should not be considered limiting. Additionally, note that real-time health monitoring is not necessarily limited to drone delivery services but can equally apply to any scenario where ADS-B operability verification is desired.

[0019] Figure 2 A flowchart 200 is shown for continuously monitoring the ADS-B receivers of multiple UAVs 201 (e.g., a first UAV-1, a second UAV-2, and up to any number N of UAVs) via a traffic estimator 220, an ADS-B health monitor 230, and optional third-party data 210, according to an embodiment of the present disclosure. The order in which some or all of the processing blocks appear in flowchart 200 should not be considered limiting. Instead, those of ordinary skill in the art benefiting from the present disclosure will understand that some processing blocks can be executed in various orders not shown, or even in parallel. Additionally, several processing blocks depict optional steps that can be omitted.

[0020] The traffic estimator 220 and the ADS-B health monitor 230 can correspond to one or more algorithms or instruction sets stored in a computer-readable or machine-readable medium, which in some embodiments can be set in the logic or circuitry of a computing system, or otherwise represented by the logic or circuitry of the computing system (see below, Figure 4 ). The computing system can be a distributed system, so the algorithms or instruction sets corresponding to the traffic estimator 220 and the ADS-B health monitor 230 can be distributed in memory or as other circuitry, external computing resources (e.g., one or more processing systems managing the multiple UAVs 201), etc., throughout the multiple UAVs 201.

[0021] In the illustrated embodiment, during a first time period (e.g., as shown, including a particular moment), multiple UAVs 201 are being jointly operated within a service area (e.g., delivering and / or transporting goods between different locations). It should be understood that each UAV included in the multiple UAVs 201 can operate within a corresponding operating area (e.g., a first operating area of a first UAV included in the multiple UAVs 201), and these operating areas do not necessarily span the entire service area. For example, a given UAV included in the multiple UAVs may make multiple trips and / or stops, and these trips and / or stops may only cover a portion of the service area. In other words, the service area of the multiple UAVs 201 at least partially overlaps with the operating area of each of the multiple UAVs 201. Figure 1 During a first time period (e.g., any duration), nearby aircraft can broadcast ADS-B messages indicating the unique identifier, altitude, longitude, latitude, rate (e.g., velocity vector), local time, and other position, time, or status information of the nearby aircraft. Each of the multiple UAVs 201 includes a corresponding ADS-B receiver (e.g., an only ADS-B input receiver) to receive the ADS-B messages broadcast by any nearby aircraft within the receiving range of the ADS-B receiver. The received ADS-B messages can be recorded (e.g., stored in a memory) by the receiving UAV as ADS-B data, which represents the traffic viewed during the first time period. Note that each of the multiple UAVs 201 does not necessarily need to receive and record the ADS-B messages of nearby aircraft while in flight (e.g., the ADS-B messages can be recorded even when a given UAV is idle between trips or is not active in flight).

[0022]

[0023] ​As shown in the figure, each of a plurality of UAVs 201 can send ADS-B data to a traffic estimator 220 (e.g., via a wired or wireless connection). More specifically, the traffic estimator 220 receives ADS-B data that is segmented into different trajectories of observed traffic, where the trajectories represent ADS-B messages received by the plurality of UAVs 201, respectively. For example, the ADS-B data obtained by the first UAV-1 can correspond to the first trajectory among the different trajectories of observed traffic and represent the ADS-B messages broadcast by traffic within the reception range of the ADS-B receiver of the first UAV-1 during a first time period. Additional traffic data different from the ADS-B data of the first UAV-1 can also be sent to the traffic estimator 220. The additional data can correspond to additional ADS-B data (e.g., different trajectories of observed traffic obtained by the ADS-B receivers of UAV-2 to UAV-N) and / or third-party aggregated ADS-B data (e.g., ADS-B messages broadcast within a service area and aggregated by a third-party service provider, such as Flightradar24 of Flightradar24 AB, OpenSky Network of the OpenSky Network Association, etc.). In some embodiments, based only on the received ADS-B messages of the respective UAVs, each of the different trajectories can represent an approximate traffic environment of the service area during the first time period. However, even if each of the plurality of UAVs 201 has a fully functional ADS-B receiver, each of the different trajectories of observed traffic may not necessarily be the same. For example, differences in the operating areas among UAVs operating in the same service area may result in different traffic observations. Therefore, the traffic estimator 220 receives and reconciles or combines the observed traffic from the plurality of UAVs 201 and generates a unified traffic environment (e.g., a single estimate of the traffic environment).

[0024] In response to receiving observed traffic data (e.g., ADS-B data from UAV-1, additional ADS-B data from UAV-2 to N, and / or third-party aggregated ADS-B data), traffic estimator 220 estimates the traffic environment of the service areas of multiple UAVs based at least in part on the ADS-B data and additional data obtained from the first UAV-1. In some embodiments, thresholding matching, probabilistic Bayesian estimators, combinations thereof, or other means may be used to combine different trajectories of the observed traffic to generate a single estimate of the traffic environment during a first time period. In one embodiment, thresholding matching may correspond to identifying a common aircraft observed by a threshold number or percentage (e.g., greater than 50%, greater than 70%, greater than 90%, or other) of the multiple UAVs 201 at one or more moments during the first time period. For example, if the ADS-B data indicates that 7 out of 10 UAVs included in the multiple UAVs 201 observe an aircraft with a first identifier at a first moment during the first time period (e.g., the observed percentage is greater than the threshold percentage), then a single estimate of the traffic environment may determine that there is an aircraft with the first identifier at the first moment. This process may be repeated at any number of moments within the first time period by traffic estimator 220 to generate a single estimate of the traffic environment.

[0025] In the same or other embodiments, traffic estimator 220 may utilize a probabilistic Bayesian estimator to generate a single estimate. The probabilistic Bayesian estimator may correspond to a Kalman filter, an extended Kalman filter, an unscented Kalman filter, or other types of data fusion algorithms to fuse different trajectories of the ADS-B data into a single estimate of the traffic environment.

[0026] In some embodiments, traffic estimator 220 compares the estimated traffic environment (e.g., the single estimate) with third-party aggregated ADS-B data received from third party 210 to verify the accuracy of the estimated traffic environment. For example, at one or more moments within the first time period, the speed, altitude, latitude, or longitude of the aircraft within the estimated traffic environment may be compared or cross-checked with the third-party aggregated ADS-B data. If there are significant differences between the estimated traffic environment and the third-party aggregated ADS-B data, the estimated traffic environment may be marked as potentially invalid.

[0027] It should be understood that in some embodiments, an estimated traffic environment can be generated substantially in real time and continuously updated (e.g., when multiple UAVs 201 receive ADS-B messages, the observed traffic data stream can be sent to the traffic estimator 220) to achieve real-time monitoring of the ADS-B receivers of multiple UAVs 201. In other words, the estimated traffic environment can be continuously updated over time to incorporate the received ADS-B data.

[0028] As shown, the traffic estimator 220 sends the estimated traffic environment to the ADS-B health monitor 230, which is configured to verify the operability of the ADS-B receivers of multiple UAVs 201 based at least in part on the observed traffic of a given UAV (e.g., the ADS-B data of the first UAV-1) and the expected observed traffic determined based on the estimated traffic environment. For example, when the first UAV-1 traverses the service area along a flight path during a first time period, any aircraft having a position within the reception range of the ADS-B receiver included in the estimated traffic environment can be expected to be observed by the first UAV-1. Then, the expected observed traffic of the first UAV can be compared with the actual observed traffic according to the ADS-B data received by the first UAV-1 during the first time period to verify whether the ADS-B receiver of the first UAV-1 is operating nominally. In some embodiments, the ADS-B health monitor 230 will provide continuous updates to the multiple UAVs 201, thereby indicating the operating status (e.g., nominal or sub-nominal) of the ADS-B receivers of the multiple UAVs 201. For example, if the ADS-B receiver of any one of the multiple UAVs 201 is not operating nominally (e.g., sub-nominally), then the ADS-B health monitor 230 can send a signal indicating the operability status of the ADS-B receiver.

[0029] Figure 3 A flowchart 300 for verifying the operability of an ADS-B receiver with an ADS-B monitor according to an embodiment of the present disclosure is shown. The flowchart 300 is a possible implementation of the process executed by Figure 2 the traffic estimator 220 and the ADS-B health monitor 230 of the flowchart 200 shown. For example, Figure 3 processing blocks 305 and 310 can correspond to Figure 2 the traffic estimator 220, and Figure 3 processing blocks 315 - 350 can correspond to Figure 2 the ADS-B health monitor 230. Returning to Figure 3, the order of some or all of the processing blocks that appear in flowchart 300 should not be considered restrictive. Instead, those of ordinary skill in the art who benefit from this disclosure will understand that some processing blocks can be executed in various orders not shown, or even in parallel. Additionally, several processing blocks depict optional steps that can be omitted.

[0030] Block 305 shows receiving traffic data representing observations of traffic within a service area during a first time period. The observed traffic data is segmented by a data source into different trajectories. In some embodiments, multiple data sources can correspond to ADS-B data obtained by an ADS-B receiver of a respective unmanned aerial vehicle (UAV) included in a plurality of UAVs. For example, a first trajectory included in different trajectories can correspond to ADS-B data obtained by an ADS-B receiver of a first UAV included in a plurality of UAVs. Additional ADS-B data obtained from multiple ADS-B receivers associated with respective UAVs included in a plurality of UAVs can also be included in the observed traffic data and segmented accordingly. In one embodiment, third-party aggregated ADS-B data can also be received from a third-party source.

[0031] Block 310 shows estimating the traffic environment of the service area during the first time period based on the observed traffic data by combining the different trajectories into a single estimate of the traffic environment. In one embodiment, the estimated traffic environment at least partially spans the first operating area of the first UAV. In the same or other embodiments, the estimated traffic environment is estimated at least in part based on ADS-B obtained by the first UAV and additional traffic data different from the ADS-B data obtained from the first UAV (e.g., additional ADS-B data obtained from multiple UAVs, third-party data, and combinations thereof). In some embodiments, at least one of a probabilistic Bayesian estimator or threshold matching is used to combine or fuse the observed traffic segmented into different trajectories to generate a single estimate of the traffic environment. In some embodiments, the probabilistic Bayesian estimator is a Kalman filter, an extended Kalman filter, or an unscented Kalman filter.

[0032] Blocks 315 and 345 show monitoring a loop of the ADS-B receivers of each UAV included in a plurality of UAVs. Processing blocks disposed between blocks 315 and 345 can include steps for an ADS-B health monitor to verify the operability of the ADS-B receivers included in the monitored UAVs.

[0033] Box 320 shows the ADS-B data received during a first time period by an ADS-B receiver of a UAV (e.g., a first UAV included in a plurality of UAVs). The ADS-B data represents the ADS-B messages broadcast by traffic within the reception range of the ADS-B receiver during the first time period. However, it should be understood that in some cases, the ADS-B data obtained by the ADS-B receiver of the UAV may be a null value or otherwise indicate a lack of observed traffic during the first time period (e.g., in a situation where there is actually no observed traffic, the ADS-B receiver is operating sub-nominally or otherwise).

[0034] Box 325 shows determining the expected observed traffic of a UAV (e.g., the first UAV) during the first time period by identifying one or more aircraft that the UAV is expected to observe during the first time period based on an estimated traffic environment, the flight path of the UAV, the reception range of the ADS-B receiver of the UAV, and combinations thereof. For example, at a first moment, the flight path of the UAV can indicate the position of the UAV. Then, based on the estimated traffic environment, any aircraft located within the reception range of the ADS-B receiver relative to the position of the UAV at the first moment can be expected to be observed by the UAV. In some embodiments, the process for identifying one or more aircraft expected to be observed can be repeated at multiple moments to determine the expected observed traffic of the UAV over the entire duration of the first time period.

[0035] Box 330 shows calculating one or more comparison metrics that compare the expected observed traffic (e.g., determined from processing block 325) and the traffic associated with the ADS-B data obtained from the ADS-B receiver of the UAV. In one embodiment, the one or more comparison metrics include determining the difference between the total number of aircraft expected to be observed by the first UAV based on the identified one or more aircraft and the actual total number of aircraft observed based on the traffic associated with the UAV's ADS-B data. In the same or another embodiment, the one or more comparison metrics include determining the difference between the expected observed duration of each of the one or more aircraft identified from the estimated traffic environment and the actual observed duration of the one or more aircraft determined from the UAV's ADS-B data.

[0036] The frame 335 shows a comparison (e.g., one or more comparison metrics) between the traffic of the expected observations based on the UAV and the traffic associated with the ADS-B data received by the ADS-B receiver of the UAV to verify the operability of the ADS-B receiver (e.g., the first UAV). More specifically, depending on whether one or more comparison metrics are within a threshold range, the ADS-B receiver of the UAV can be marked as nominal or sub-nominal. In one embodiment, a first aircraft expected to be observed by the UAV at a first moment is identified based on the estimated traffic environment (e.g., as shown in block 325). In the same embodiment, when the ADS-B data obtained by the UAV does not include an ADS-B message corresponding to the first aircraft at the first moment, the ADS-B receiver can be marked as sub-nominal. Conversely, when the ADS-B data obtained by the UAV does include an ADS-B message corresponding to the first aircraft at the first moment, the ADS-B receiver can be marked as nominal.

[0037] In the same or other embodiments, when at least one of one or more comparison metrics is within a threshold range, the ADS-B receiver of the UAV is marked as nominal, or conversely, when at least one of one or more comparison metrics is outside the threshold range, the ADS-B receiver of the UAV is marked as sub-nominal. In some embodiments where there is more than one of one or more comparison metrics, a single instance of one of the one or more comparison metrics being outside the threshold range may cause the ADS-B receiver of the UAV to be marked as sub-nominal. In some embodiments, the threshold range of the difference between the expected observation duration of each of one or more aircraft identified from the estimated traffic environment and the actual observation duration of one or more aircraft determined from the ADS-B data corresponds to a percentage difference (e.g., for each observed aircraft, the UAV actual observation duration is at least 50%, 70%, 90%, 95% or a different percentage of the expected observation duration). In the same or other embodiments, the threshold range of the difference between the total number of aircraft expected to be observed by the UAV and the actual total number of observed aircraft is a threshold percentage (e.g., at least 50%, 70%, 90%, 95% or a different percentage of the aircraft expected to be observed by the UAV are actually observed by the UAV during a first time period). In some embodiments, the percentage difference of the threshold range may have an upper limit (e.g., not greater than 100%).

[0038] Block 340 illustrates determining actions to take to address whether the UAV's ADS-B receiver is nominal or sub-nominal. In some embodiments, the actions taken to address a nominal UAV ADS-B receiver may continue on schedule (e.g., delivering a package, etc.). In the same or other embodiments, verification of a nominal ADS-B may result in scheduling the next health check of the ADS-B receiver at a particular time or when an event occurs (e.g., package delivery, UAV landing, etc.). In some embodiments, the actions taken to address a sub-nominal UAV ADS-B receiver may include adjusting the UAV's flight path. The UAV's flight path may be adjusted to ensure a threshold distance is maintained from the identified aircraft, adding a docking to a launch site or repair center for maintenance, and / or immediately landing the UAV. In some embodiments, when the UAV's ADS-B receiver is marked as sub-nominal, the UAV may still complete the delivery of the package before being rerouted for repair.

[0039] Block 345 illustrates the end of the terminal of the loop that began at block 315. It should be understood that UAVs included in multiple UAVs may verify the operability of their ADS-B receivers sequentially and / or simultaneously. It should also be understood that in some embodiments, the ADS-B receivers of multiple UAVs may be continuously monitored in real time, and as the multiple UAVs receive ADS-B data, the estimated traffic environment is updated.

[0040] Block 350 illustrates the verified ADS-B receiver operability for each of the multiple UAVs after completing the loop associated with blocks 315 through 345. It should be understood that in some embodiments, block 350 may proceed to block 305 (e.g., obtain additional observed traffic data, update the estimated traffic environment, and continue to monitor the ADS-B receivers of the multiple UAVs).

[0041] Figure 4 A functional block diagram of a computing system 400 according to an embodiment of the present disclosure is shown. The computing system 400 includes multiple UAVs 401 (e.g., 401-1, 401-2, 401-3, …, 401-N), an external computing device 461, and an optional third-party data provider 495. The computing system 400 is one possible system that may implement Figure 2 the flowchart 200 shown and Figure 3 any one of the flowchart 300 shown to implement ADS-B receiver operability verification.

[0042] In Figure 4In the described embodiments, each of the plurality of UAVs 401 (e.g., the first UAV 401-1) includes a power supply system 403, a communication system 405, a control circuit 407, a propulsion unit 409 (e.g., one or more propellers, engines, etc., for positioning the UAV 401), an image sensor 411 (e.g., one or more CMOS or other types of image sensors and corresponding lenses for capturing images of the service area), other sensors 413 (e.g., an inertial measurement unit for determining the pose information of the UAV, LIDAR camera, radar, etc.), a data storage device 415, and a payload 417 (e.g., for collecting and / or receiving packages, goods, etc.). The power supply system 403 includes a charging circuit 419 and a battery 421. The communication system 405 includes a GNSS receiver 423 and an antenna 425, as well as an ADS-B receiver 427 (e.g., an ADS-B input receiver only). The control circuit 407 includes a controller 429 and a machine-readable storage medium 433. The controller 429 includes one or more processors 431 (e.g., a dedicated processor, a field-programmable gate array, a central processing unit, a graphics processing unit, a tensor processing unit, and / or a combination thereof). The machine-readable storage medium 433 includes program instructions 435 and may include additional instructions corresponding to a traffic estimator 437 and an ADS-B health monitor 439. The data storage device 415 includes an estimated traffic environment 441 (e.g., received from an external computing device 461, generated by the execution of the traffic estimator 437, or a combination thereof) and ADS-B data 443 (e.g., recorded ADS-B messages obtained by the ADS-B receiver 427 and / or additional ADS-B data received from other UAVs included in the plurality of UAVs 401). Each component of the UAV 401 may be coupled (e.g., electrically coupled) to each other via an interconnect 450.

[0043] The power supply system 403 provides an operating voltage to the communication system 405, the control circuit 407, the propulsion unit 409, the image sensor 411, the other sensors 413, the data storage device 415, and any other components of the UAV 401. The power supply system 403 includes a charging circuit 419 and a battery 421 (e.g., an alkaline battery, a lithium-ion battery, etc.) to power the various components of the UAV 401. The battery 421 may be directly charged with the charging circuit 419 (e.g., via an external power source), inductively charged (e.g., via the antenna 425 used as an energy harvesting antenna), and / or may be replaced within the UAV 401 when the charge is depleted.

[0044] Communication system 405 provides communication hardware and protocols for wireless communication with an external computing device 461 (e.g., via antenna 405) and sensing geospatial positioning satellites to determine the coordinates and altitude of UAV 401 (e.g., via GPS, GLONASS, Galileo, Beidou, or any other global navigation satellite system). Representative wireless communication protocols include, but are not limited to, Wi-Fi, Bluetooth, LTE, 5G, etc. The ADS-B receiver 427 may be coupled to antenna 425 or include a separate antenna to receive ADS-B messages broadcast by aircraft within the receiving range of the ADS-B receiver 427.

[0045] Control circuit 407 includes a controller 429, a traffic estimator 437, and an ADS-B health monitor 439 coupled to a machine-readable storage medium 433 (including program instructions 435). When the controller 429 executes the program instructions 435, the traffic estimator 437, and / or the ADS-B health monitor 439, the system 400 is configured to perform operations. For example, the program instructions 435 may orchestrate the operation of the components of UAV 401 to deliver a package. According to embodiments of the present disclosure, in some embodiments, the execution of the traffic estimator 437 may cause the system 400 to estimate the traffic environment of the service area, and the execution of the ADS-B health status monitor 439 may cause the system 400 to verify the operability of the ADS-B receiver 427. It should be understood that the control circuit 407 may not show all the logic modules, program instructions, etc., all of which may be implemented using software / firmware running on a general-purpose microprocessor, hardware (e.g., an application-specific integrated circuit), or a combination of both.

[0046] In some embodiments, the UAV 401 may be wirelessly (e.g., via communication link 499) coupled to an external computing device 461 to provide external computing capabilities via a processor 463 and access a data storage device 469, which may include an estimated traffic environment 471 of the service area, ADS-B data 473 received from multiple UAVs 401, and aggregated third-party ADS-B data 475 received from a third-party data provider 495. The external computing device 461 includes an antenna 465 for communicating with multiple UAVs 401. The processor 463 orchestrates the operation of the external computing device 461 based on program instructions 477, a traffic estimator 479, and an ADS-B health monitor 481 included in a machine-readable storage medium 467. For example, according to embodiments of the present disclosure, the external computing device 461 may be configured to receive ADS-B data 443 from each of the multiple UAVs 401 and generate an estimated traffic environment 471. In some embodiments, the external computing device 461 may also be configured to send the estimated traffic environment 471 as an estimated traffic environment 441 to the multiple UAVs 401 (e.g., such that the multiple UAVs may autonomously perform an ADS-B receiver health check via the ADS-B health monitor 439). In the same or other embodiments, the execution of the ADS-B health status monitor 481 may cause the external computing device 461 to monitor the ADS-B receivers 427 of each of the multiple UAVs 401 and periodically send a verification signal to each of the multiple UAVs to indicate whether the ADS-B receiver 427 is operating in a nominal or sub-nominal state.

[0047] It should be understood that the data storage device 415, the machine-readable storage medium 433, the machine-readable storage medium 467, and the data storage device 469 are non-transitory machine-readable storage media, which may include, but are not limited to, any volatile (e.g., RAM) or non-volatile (e.g., ROM) storage systems readable by components of the system 400. It should also be understood that the system 400 may not show all of the logic modules, program instructions, etc. All of these may be implemented in software / firmware executed on a general-purpose microprocessor, hardware (e.g., an application-specific integrated circuit), or a combination of both.

[0048] It should be understood that the “unmanned” aircraft or UAV referred to herein is equally applicable to autonomous and semi-autonomous aircraft. In a fully autonomous implementation, all functions of the aircraft are automated; for example, pre-programmed or controlled via real-time computer functions that respond to inputs from various sensors and / or predetermined information. In a semi-autonomous implementation, some functions of the aircraft can be controlled by a human operator while other functions are performed autonomously. Additionally, in some embodiments, the UAV can be configured to allow a remote operator to take over functions that would otherwise be autonomously controlled by the UAV. For example, in some embodiments, the functions can include operating a mechanical system for picking up an object, a gimbal and camera for taking aerial photographs, etc. Further, a given type of function can be remotely controlled at one level of abstraction while being performed autonomously at another level of abstraction. For example, a remote operator can control high-level navigation decisions of the UAV, such as specifying that the UAV should travel from one location to another (e.g., from a warehouse in the suburbs to a delivery address in a nearby city), while the navigation system of the UAV autonomously controls more fine-grained navigation decisions, such as the specific route to take between the two locations, the specific flight control inputs to achieve that route and avoid obstacles while navigating that route, etc.

[0049] The above processes are described in terms of computer software and hardware. The described techniques can constitute machine-executable instructions embodied in a tangible or non-transitory machine (e.g., a computer) readable storage medium, which when executed by the machine will cause the machine to perform the described operations. Additionally, the processes can be embodied in hardware, such as a dedicated integrated circuit (“ASIC”) or others.

[0050] A tangible machine readable storage medium includes any mechanism that provides (i.e., stores) information in a non-transitory form accessible by a machine (e.g., a computer, a network device, a personal digital assistant, a manufacturing tool, any device having a set of one or more processors, etc.). For example, a machine readable storage medium includes recordable / non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash devices, etc.).

[0051] The above description of the illustrated embodiments of the invention, including the description in the abstract, is not intended to be exhaustive or to limit the invention to the specific forms disclosed. While specific embodiments and examples of the invention have been described herein for purposes of illustration, those skilled in the relevant art will recognize that various modifications are possible within the scope of the invention.

[0052] Based on the above detailed description, the present invention can be modified in these ways. The terms used in the appended claims should not be construed as limiting the present invention to the specific embodiments disclosed in the specification. On the contrary, the scope of the present invention will be determined entirely by the appended claims, which will be interpreted in accordance with established principles of claim interpretation.

Claims

1. A non - transitory computer - readable medium storing logic thereon, the logic, when executed by one or more processors of a computing system, causes the computing system to perform actions for verifying the operability of a broadcast automatic dependent surveillance - broadcast (ADS - B) receiver, the actions including: Receiving, by the computing system, ADS - B data obtained by an ADS - B receiver included in a first unmanned aerial vehicle (UAV), the ADS - B data representing ADS - B messages broadcast by traffic within the reception range of the ADS - B receiver during a first time period; Estimating, by the computing system, a traffic environment of a service area that at least partially spans a first operating area of the first UAV during the first time period, wherein the traffic environment is estimated at least in part based on the ADS - B data obtained by the first UAV and additional traffic data different from the ADS - B data; Determining, by the computing system, the traffic expected to be observed by the first UAV during the first time period based on the estimated traffic environment; and Verifying, by the computing system, the operability of the ADS - B receiver of the first UAV based on a comparison between the traffic expected to be observed by the first UAV and the traffic associated with the ADS - B data received by the ADS - B receiver of the first UAV.

2. The non - transitory computer - readable medium according to claim 1, wherein, the ADS - B receiver is an ADS - B only input receiver, and wherein the first UAV does not include a transponder with ADS - B output capability.

3. The non - transitory computer - readable medium according to claim 1, wherein, the ADS - B message includes at least one of a unique identifier, latitude, longitude, altitude, or rate of one or more aircraft included in the traffic at one or more moments within the first time period.

4. The non - transitory computer - readable medium according to claim 1, wherein, the additional traffic data corresponds to additional ADS - B data obtained by a plurality of ADS - B receivers, each ADS - B receiver associated with a respective UAV included in a plurality of UAVs operating within the service area during the first time period.

5. The non - transitory computer - readable medium according to claim 4, wherein, the additional ADS - B data of the plurality of UAVs is segmented into different trajectories of the traffic observed during the first time period, each different trajectory associated with a respective one of the plurality of UAVs.

6. The non - transitory computer - readable medium according to claim 5, wherein, the different trajectories further include a first trajectory of the traffic observed and associated with the ADS - B data of the first UAV.

7. The non - transitory computer - readable medium according to claim 5, wherein, estimating the traffic environment further includes: Combining each different trajectory of the observed traffic into a single estimate of the traffic environment.

8. The non - transitory computer - readable medium according to claim 7, wherein, using at least one of a probabilistic Bayesian estimator or threshold matching to combine the different trajectories of the observed traffic to generate a single estimate of the traffic environment.

9. The non-transitory computer-readable medium according to claim 8, wherein, the probabilistic Bayesian estimator is a Kalman filter, an extended Kalman filter, or an unscented Kalman filter.

10. The non-transitory computer-readable medium according to claim 1, wherein, the comparison corresponds to determining one or more comparison metrics that compare the expected observed traffic and the traffic associated with the ADS-B data, and wherein the operability of the ADS-B receiver of the first UAV is verified as nominal when at least one of the one or more comparison metrics is within a threshold range.

11. The non-transitory computer-readable medium according to claim 10, wherein, determining the expected observed traffic of the first UAV includes: identifying, at one or more moments within a first time period, one or more aircraft included in the estimated traffic environment within the reception range of the ADS-B receiver of the first UAV, at least in part based on the flight path of the first UAV during the first time period.

12. The non-transitory computer-readable medium according to claim 11, wherein, the difference between the total number of aircraft expected to be observed by the first UAV based on the identified one or more aircraft and the actual total number of aircraft observed based on the traffic observations associated with the ADS-B data is included in the one or more comparison metrics.

13. The non-transitory computer-readable medium according to claim 11, wherein, the difference between the expected observed duration of each of the one or more aircraft identified from the estimated traffic environment and the actual observed duration of the one or more aircraft determined from the ADS-B data is included in the one or more comparison metrics.

14. The non-transitory computer-readable medium according to claim 1, wherein, verifying the operability of the ADS-B receiver of the first UAV is determined substantially in real time.

15. The non-transitory computer-readable medium according to claim 1, wherein, the actions further include: receiving, by the computing system, the ADS-B data and additional traffic data substantially in real time; identifying, by the computing system, a first aircraft expected to be observed by the first UAV at a first moment based on the estimated traffic environment; and marking the ADS-B receiver of the first UAV as sub-nominal when the ADS-B data does not include an ADS-B message corresponding to the first aircraft at the first moment; and determining an action to be taken to address the ADS-B receiver of the first UAV being sub-nominal.

16. The non-transitory computer-readable medium according to claim 1, wherein, the actions further include: comparing the estimated traffic environment with third-party aggregated ADS-B data to verify the accuracy of the estimated traffic environment.

17. The non-transitory computer-readable medium according to claim 1, wherein, the actions further include: when the expected observed traffic of the first UAV is different from the traffic associated with the ADS-B data obtained by the first UAV, determining, by the computing system, an action to be performed by the first UAV; and instructing the first UAV to perform the action.

18. The non-transitory computer-readable medium according to claim 17, wherein, the actions include at least one of the following: adjusting a flight path of a first UAV, immediately landing the first UAV, or adding a docking for the first UAV to a launch site or a maintenance center for maintenance.

19. A method for verifying the operability of a broadcast automatic dependent surveillance - broadcast (ADS - B) receiver, the method comprising: receiving ADS - B data obtained by an ADS - B receiver included in a first unmanned aerial vehicle (UAV), the ADS - B data representing ADS - B messages broadcast by traffic within the reception range of the ADS - B receiver during a first time period; estimating a traffic environment of a service area that at least partially spans a first operating area of the first UAV during the first time period, wherein the traffic environment is estimated at least in part based on the ADS - B data obtained by the first UAV and additional traffic data different from the ADS - B data; determining, based on the estimated traffic environment, the traffic expected to be observed by the first UAV during the first time period; and verifying the operability of the ADS - B receiver of the first UAV based on a comparison between the traffic expected to be observed by the first UAV and the traffic associated with the ADS - B data received by the ADS - B receiver of the first UAV.

20. The method according to claim 19, wherein, the additional traffic data corresponds to additional ADS - B data obtained by a plurality of ADS - B receivers, each ADS - B receiver being associated with a respective UAV included in a plurality of UAVs operating within the service area during the first time period.

21. The method according to claim 20, wherein, the additional ADS - B data of the plurality of UAVs is segmented into different trajectories of the traffic observed during the first time period, each different trajectory being associated with a respective one of the plurality of UAVs.

22. The method according to claim 21, wherein, estimating the traffic environment further includes: combining each different trajectory of the observed traffic into a single estimate of the traffic environment.

23. The method according to claim 22, wherein, using at least one of a probabilistic Bayesian estimator or threshold matching to combine the different trajectories of the observed traffic to generate a single estimate of the traffic environment.

24. A system for verifying the operability of a broadcast automatic dependent surveillance - broadcast (ADS - B) receiver, comprising: a plurality of unmanned aerial vehicles (UAVs) configured to operate within a service area during a first time period, wherein each of the plurality of UAVs includes an ADS - B receiver to receive ADS - B data representing ADS - B messages broadcast by traffic within the reception range of the ADS - B receiver when the plurality of UAVs are in operation; A traffic estimator configured to jointly receive ADS-B data of multiple UAVs as common ADS-B data and estimate a traffic environment for at least a first time period that at least partially spans a first operating area of a first UAV included in the multiple UAVs, wherein the estimated traffic environment is at least partially based on the common ADS-B data; and An ADS-B health monitor configured to verify the operability of an ADS-B receiver of a first UAV included in the multiple UAVs based on a comparison between the expected observed traffic of the first UAV determined from the estimated traffic environment and the traffic associated with the ADS-B data received by the ADS-B receiver of the first UAV.

25. The system according to claim 24, wherein, the ADS-B receiver of each of the multiple UAVs is an ADS-B only input receiver, and wherein each of the multiple UAVs does not include a transponder with ADS-B output capability.

26. The system according to claim 24, wherein, the common ADS-B data corresponds to observed traffic within a service area during a first time period that is segmented into different trajectories, each different trajectory being associated with a respective one of the multiple UAVs, and wherein the traffic estimator is further configured to estimate the traffic environment by combining the different trajectories into a single estimate of the traffic environment using at least one of a probabilistic Bayesian estimator or threshold matching.

27. The system according to claim 24, wherein, the ADS-B health monitor is further configured to perform the comparison by determining one or more comparison metrics that compare the expected observed traffic of the first UAV during a first time period and the traffic associated with the ADS-B data obtained by the first UAV during the first time period, and wherein when at least one of the one or more comparison metrics is within a threshold range, the operability of the ADS-B receiver of the first UAV is verified as nominal.

28. The system according to claim 27, wherein, the ADS-B health monitor is further configured to determine the expected observed traffic of the first UAV during the first time period by at least partially based on the flight path of the first UAV during the first time period, identifying one or more aircraft within the reception range of the ADS-B receiver of the first UAV and included in the estimated traffic environment at one or more moments within the first time period.

29. The system according to claim 28, wherein, the one or more comparison metrics include at least one of the following: the difference between the total number of aircraft expected to be observed by the first UAV based on the identified one or more aircraft and the actual total number of aircraft observed based on the traffic associated with the ADS-B data obtained by the first UAV; or the difference between the expected observed duration of each of the one or more aircraft identified from the estimated traffic environment and the actual observed duration of the one or more aircraft determined from the ADS-B data obtained by the first UAV.

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