Patent Description:
In United States ports alone, nearly <NUM> million cargo containers are unloaded without any physical scanning before they arrive. Once they arrive, containers may be passed through static radiation detectors. Any containers designated as high risk may be passed through further x-ray or gamma ray machines which image the containers' contents. Such static radiation detectors may detect gamma rays or neutrons emitted by sources of, for example, uranium and plutonium.

Gamma rays may be detected by observing the effects they have on matter. A gamma ray can interact with matter through several physical processes, each of which results in a transfer of energy from the gamma ray to the matter and depends on both the energy of the gamma ray and the properties of the matter with which it interacts. In Compton scattering, a gamma ray will collide with an electron and bounce off it. In photoelectric ionization, a gamma ray can push an electron to a higher energy level. As gamma-rays have so much energy, part of this energy can be transformed into matter directly by creating an electron and an anti-electron (or positron), a process known as pair production. All of these interactions cause energy to be transferred to the material, and in some types of materials this produces a signal (such as light or an electric current) that can be measured using electronic detectors. The signal can then be amplified and measured to estimate the energy and direction of the original gamma ray.

<CIT> describes a screening system for scanning a target for radioactive isotopes. The screening system comprises a gamma-ray spectrometer, comprising: a scintillation body for generating photons in response to incident gamma-rays; a photo-detector coupled to the scintillation body for detecting the photons and generating output signals therefrom; and a processor for receiving the output signals from the photo-detector and calculating energy loss spectra for incident gamma-rays and for identifying radioactive isotopes present in the target based on the energy loss spectra. The screening system further comprises a drive mechanism coupled to the gamma-ray spectrometer and arranged to move the gamma-ray spectrometer relative to the target to perform a scan.

<CIT> describes a radiation detection device comprising a data processor arranged to be communicatively coupled to a position sensor mounted on an unmanned vehicle. A solid state radiation sensor is mounted on the unmanned vehicle. The data processor is configured to receive position data from the position sensor; receive radiation data from the solid state radiation sensor; and periodically associate the position data with radiation data to form combined data.

The document "<NPL>" outlines a program whereby instruments based on a bulk detection method that uses penetrating radiation that is hazardous to enforcement personnel may be used. The document presents a scenario for a staged program to expand and enhance existing detection capability: (<NUM>) deploying existing penetrating radiation backscattering instruments via robotic machines; (<NUM>) optimizing instrument sensitivities for deployment by robotic machines; (<NUM>)augmenting the penetrating radiation backscattering approaches with complementary sensing technologies; (<NUM>) providing a picture- and graphics-oriented human interface; (<NUM>) providing database and statistical tools to assist decision making. To illustrate concretely how this system concept might be implemented a the suction-cup-footed, eddy-current- and vision-sensor-carrying robot is described for attacking the problem of inspecting passenger aircraft for pressurization-depressurization induced radial cracks around rivets and corrosion induced delamination of lap joints.

<CIT> describes a system, method, and mobile frame structure to detect radiation and identify materials associated with radiation that has been detected. The mobile frame structure comprises a first portion and a second portion configured to examine an entity to be examined therebetween and for detecting radiation and identifying materials associated with radiation that has been detected. The method comprises maneuvering at least one of the entity and the mobile frame structure such that the mobile frame structure is located over the entity to be examined for radiation sources emitting radiation from the entity; receiving from a set of radiation sensors mechanically coupled to the at least one portion of the mobile frame structure, a set of radiation data associated with the entity; and generating at least one histogram based on the set of radiation data, wherein the at least one histogram represents a spectral image of at least one radiation source associated with the entity comparing the at least one histogram to a plurality of spectral images associated with known materials. The method further comprises determining that the at least one histogram substantially matches at least one of the plurality of spectral images; determining if a material associated with the at least one of the plurality of spectral images comprises a hazardous material; and notifying personnel that the at least one radiation source comprises a hazardous material in response to determining that the material associated with the at least one of the plurality of spectral images comprises a hazardous material.

The document <CIT> describes a mobile split type inspection system and method. The scanning inspection system comprises a first radiation source, a first detection device, a first automated guided vehicle, and a second automated guided vehicle. The first detection device is provided with a detector; the first radiation source is mounted on the first automated guided vehicle; and the first detection device is mounted on the second automated guided vehicle. The first automated guided vehicle and the second automated guided vehicle can be dispatched to drive the first radiation source and the first detection device to move to preset scanning inspection positions, and a scanning channel allowing to-be-scanned articles to pass is formed between the first automated guided vehicle and the second automated guided vehicle; scanning inspection of the to-be-scanned articles is realized through relative movement between the to-be-scanned articles and the two automated guided vehicles. The scanning inspection system is flexible to move and can meet different requirements of multiple working conditions.

The present invention concerns a system for scanning shipping containers, comprising an unmanned vehicle, an article of manufacture for scanning shipping containers, and a method of scanning shipping containers according to the independent claims. The dependent claims describe embodiments of the invention.

The inventive system for scanning shipping containers comprises an unmanned vehicle and a server.

The unmanned vehicle comprises a sensor, a processor, and a memory including instructions for execution, the instructions, when executed by the processor, for causing the unmanned vehicle to:.

The server is configured to make a preliminary determination that the container may include a given material based upon the container data collected by the unmanned vehicle while moving, and based upon the preliminary determination that the container may include the given material, order another unmanned vehicle inspection of the container to gather additional information using unmanned vehicle sensors.

In some embodiments, the system may include a stationary array of sensor elements. In some embodiments, the stationary array of elements may be configured to measure background radiation data. In some embodiments, the server may be configured to identify whether the container includes a given material based upon the container data collected by the unmanned vehicle while moving, adjusted for the background radiation data collected by the stationary array of sensor elements. In some embodiments, the system may further include sensor elements arranged in a two-dimensional array. In some embodiments, the server may be further configured to identify radiation in an area of the sensor elements. In some embodiments, the server may be configured to determine, for each radiation sensor in the two-dimensional array, a direction of a source of the radiation, and identify a location of the source of the radiation from interpolation of the direction determined for each radiation sensor in the two-dimensional array. In some embodiments, the other unmanned vehicle inspection of the container may be of a longer duration than an initial unmanned vehicle inspection of the container. In some embodiments, the other unmanned vehicle inspection of the container may be closer to the container than an initial unmanned vehicle inspection of the container. In some embodiments, the server may be configured to instruct the unmanned vehicle to move along faces of the shipping container and record container data based upon a previous indication that the container may include a given material. In some embodiments, the unmanned vehicle may be further configured to record position information, and record container data as a function of the position information. In some embodiments, the server may be configured to reconstruct a position of the unmanned vehicle based upon the container data recorded as a function of the position information. In some embodiments, the server may be further configured to reconstruct the position or altitude of the unmanned vehicle based upon a (<NUM>/(r^<NUM>)) drop-off of radiation. In some embodiments, the server may be configured to identify whether the container includes a given material based upon the container data collected by the unmanned vehicle as compared to data indicating known radioactive sources. In some embodiments, the system may include another unmanned vehicle including an active radiation source. The other unmanned vehicle may be configured to move along faces of the shipping container opposite the unmanned vehicle. The unmanned vehicle may include a passive sensor configured to detect radiation from the active radiation source. The unmanned vehicle may be configured to record container data collected from the passive sensor while scanning the shipping container. The server may be configured to analyze signatures from the container data collected from the passive sensor.

Embodiments of the present disclosure may include an article of article of manufacture comprising a non-transitory memory. The memory may include instructions. The instructions, when loaded and executed by a processor, may cause the processor to perform the configuration of any of the servers, systems, or vehicles of the above embodiments.

Embodiments of the present disclosure may include methods performed by any of the servers, systems, vehicles, or processors of the above embodiments.

<FIG> is an illustration of a system <NUM> utilizing an unmanned autonomous system (UAS), according to embodiments of the invention. System <NUM> is configured to inspect one or more shipping containers. System <NUM> includes one or more UASs <NUM>. System <NUM> includes one or more host servers <NUM>. A single instance of host server <NUM> may be in communication with multiple UASs <NUM>. Any suitable number of UASs <NUM> may be used. UASs <NUM> may include, for example, an aerial vehicle or drone.

System <NUM> may be used to detect radiation sources in cargo or conveyances at consolidation points, such as ports, without requiring the shipping containers or conveyances to be routed past stationary radiation detectors or radiography machines. System <NUM> may use UASs <NUM> mounted with radiation detectors moving past cargo or conveyances. In one embodiment, background radiation levels may be measured by unobtrusive stationary detectors <NUM> at key locations in the area in which the scanning is going to occur. This information may be continuously analyzed along with the data collected from UAS <NUM>. Software in UAS <NUM> or server <NUM> may be configured to evaluate and analyze the spectral signature of any radiation detected by UAS <NUM> against a database of radioactive sources including both potentially dangerous sources (such as radiological weapons or nuclear devices) and materials that have high levels of naturally occurring radiation and pose no threat (such as ceramics).

Scanning containers or conveyances for radiation in a dynamic location such as a port using UAS <NUM> creates major challenges in terms of measuring and accounting for background radiation. Simply mounting commercially available radiation detectors on UAS <NUM> will not work for the desired monitoring purposes because the sensitivity is continuously affected by the changing radiation background. In a land, air, or seaport, the containers and conveyances are constantly moving and in different configurations. Because of the sensitivity of the activity and the high levels of accuracy that are required, system <NUM> may overcome these challenges by networking the detectors mounted on UAS <NUM> with fixed detectors <NUM> located at key locations throughout the port or other facility in which the scanning is being conducted. Furthermore, software on UAS <NUM> or server <NUM> may compare the signal collected by the detectors located on UAS <NUM> against the background radiation measured by stationary detectors <NUM> to identify anomalies. This may yield a continuously updated radiation map. Continuous communication between stationary detectors <NUM> and the detectors on UAS <NUM> may be used to quickly and accurately evaluate whether radiation signals come from background radiation or from a source located within cargo or conveyances. Furthermore, these may be used to determine whether such a radioactive source comprises a threat.

UAS <NUM> may be configured to detect radiation. System <NUM> may then be configured to locate the source of the radiation through triangulation or other reconstruction methods. Furthermore, system <NUM> may be configured to identify the nature of the radiation. In one embodiment, once an initial signal is detected, UAS <NUM> may then perform another inspection. In this inspection, UAS <NUM> may follow a predetermined pattern to determine the exact location of a radiation source and maximize the utility of the data collected about the source. In some cases, a differently configured instance of UAS <NUM> may be deployed to more efficiently identify a radiation source that has already been located by the first instance of UAS <NUM>. In a second search, UAS <NUM> may be configured to loiter for longer periods of time near the potential radiation source in order to collect as much information as possible. From a second search, system <NUM> may match a detected signal against a database of radiation signatures to more accurately identify the source of the radiation.

UAS <NUM> may be implemented in any suitable manner according to the teachings of the present disclosure. UAS <NUM> may include a UAS application <NUM>. Although described as a single application, UAS application <NUM> may be implemented by any suitable number and kind of applications, scripts, programs, executables, libraries, drivers, functions, or other instructions for execution. The instructions may be resident on one or more memories <NUM> for execution by one or more processors <NUM>. Functionality of UAS <NUM> may be performed by UAS application <NUM>. UAS may include any suitable number and kind of rotors, tracks, wheels, motors, or other components for locomotion.

Server <NUM> may be implemented in any suitable manner according to the teachings of the present disclosure. Server <NUM> may be implemented, for example, as a rack server, blade server, laptop, computer, cloud computing network, mobile device, or any other suitable electronic device. Server <NUM> may include a server application <NUM>. Although described as a single application, server application <NUM> may be implemented by any suitable number and kind of applications, scripts, programs, executables, libraries, drivers, functions, or other instructions for execution. The instructions may be resident on one or more memories <NUM> for execution by one or more processors <NUM>. Functionality of server <NUM> may be performed by server application <NUM>.

In various embodiments, operations and configurations of server application <NUM> may be performed instead by UAS application <NUM>, and vice-versa. UAS application <NUM> may be primarily responsible for controlling motion of UAS <NUM> based upon a specified flight path, and for storing position and orientation of UAS <NUM> as a function of time. Furthermore, UAS application <NUM> may be primarily responsible for operating radiation detectors aboard UAS <NUM>, recording data, performing low-level analysis of the spectral data, and reporting the data to server <NUM>. Server application <NUM> may be primarily responsible for setting flight paths for UAS <NUM>, identifying follow-up UAS inspection, integrating data from UAS <NUM> and stationary detectors <NUM> to generate and update a background map of radiation data, setting alarm conditions, identifying alarm conditions, and reporting alarms to users of system <NUM>.

Stationary detectors <NUM> may include any suitable number and kind of sensors, similar to sensors used in sensors <NUM> in UAS <NUM>. Stationary detectors <NUM> may be configured to continuously record spectral information as a function of time. Stationary detectors <NUM> may operate continuously and thus can be well characterized with calibration measurements. Stationary detectors <NUM> may be used to measure steady background radiation spectra rather than transient anomalies. Thus, cheaper, lower resolution detectors may be used. Stationary detectors <NUM> may include a larger volume of detectors with a lower resolution than sensors <NUM> in UAS <NUM>. Stationary detectors <NUM> may be, for example, a gamma ray detector such as a polyvinyl toluene (PVT) or sodium iodide (NaI) detector. Stationary detectors <NUM> may be communicatively coupled to server <NUM> or UAS <NUM> to provide background radiation information that may be used to adjust or interpret data detected by UAS <NUM>.

The effectiveness of the background mapping may be optionally enhanced by constructing the stationary detectors <NUM> with partitions in their sensitive volumes. For example, stationary detectors <NUM> may be formed in a 2x2x2 or 3x3x3 array of cubes, forming a larger cube. This may provide rudimentary directional information about the background radiation and add to the mapping of the radiation. For example, if a patch of concrete beneath one container stack is significantly more naturally radioactive than adjacent stacks, this directional info can be used to better localize the background radiation and thus provide higher sensitivity away from the higher background area when considering measurements made by UAS <NUM>.

Stationary detectors <NUM> may be placed in a known pattern throughout the inspection area. The location of stationary detectors <NUM> may be recorded in a coordinate system that is communicated to and common with UAS <NUM>. Data from stationary detectors <NUM> may then be interpolated as a function of position and time to create a map of the background radiation throughout the inspection area to provide high quality background data to for inspection comparisons. The density of a network of stationary detectors <NUM> may be varied based on the constraints of the inspection area, level of variability of the background radiation, and desired alarm specificity.

UAS <NUM> may be implemented, for example, as an aerial UAS or drone. UAS <NUM> may include any suitable number of rotors or propellers. For example, UAS <NUM> may include four, six, eight, or other numbers of rotors. UAS <NUM>, its sensors, and transmission and communications systems therein may employ weatherproofing to allow them to be used in inclement weather. UAS <NUM> may be operated by onboard battery or tethered to power sources to remain in use for extended periods of time. The sensors carried on UAS <NUM> may have their own batteries or be connected to the battery operating UAS <NUM>. UAS <NUM> may be configured to fly in both manually operated and autonomous modes to search cargo. The routes used to search containers may be matched to particular cargo configurations within the containers and particular configurations of containers within ports or in transit. These routes may be developed on the basis of testing. UAS <NUM> may employ specific search routes when the contents of the container are alleged to be a specific type and can be efficiently searched with specialized patterns. UAS <NUM> may be implemented in vehicles that that do not fly, which may be magnetized with tank treads, using wheels, moving on legs (robotic spiders), suction, gels, metal legs that flip over, and other means of conveyance.

UAS <NUM> includes sensors <NUM>. UAS <NUM> may include any suitable number and kind of sensors <NUM>. Some of sensors <NUM> may include sensors for guidance and locomotion. Such sensors may include cameras, altimeters, global positioning system (GPS) sensors, proximity sensors, ranger finders, other location sensors, speedometers, wind sensors, telemetry sensors, vibration sensors, and motor monitoring sensors. UAS <NUM> may include software to power motors, rotors, wheels, or other components for locomotion based upon the sensors for guidance and locomotion and upon an intended flight path. The intended flight path may be controlled by a preprogrammed flight plan to search a designated container or containers, or may be controlled manually by a user. The flight path may be set by UAS <NUM> or server <NUM>.

Sensors <NUM> may include sensors for identifying content of cargo containers. For example, sensors <NUM> may include passive radiation detectors. Sensors <NUM> may be configured to detect gamma, neutron, x-ray, or other radiation. The passive detectors may include spectroscopic detectors, scintillators, or other variations of sensors. Sensors <NUM> may further include radiation isotope identification devices or other active source radioactive material detection systems, which could allow greater accuracy in the scanning of cargo containers for radioactive materials. Furthermore, sensors <NUM> may include LiDAR, thermal imaging, infrared, penetrative cameras and other imaging, night vision, sensors for specific substances. These may include CO<NUM> sensors used to identify the presence of stowaways or human trafficking victims, chemical sensors, or sensors linked to types of contraband including narcotics, etc. Sensors <NUM> may include x-ray and gamma ray imaging cameras and sensors that can be mounted on UAS <NUM> with the ability to image within containers or vehicles. Sensors <NUM> may provide continuous readings or take measurements at defined intervals that have been identified through testing.

Sensors <NUM> may require specific mounting configurations and weight counterbalancing. Sensors <NUM> may be mounted on UAS <NUM> in any suitable manner. For example, UAS <NUM> may include sensors <NUM> mounted using carbon fiber or other ultralight materials. The mounts may include interchangeable racking so that different sensors can be used on the same UAS. UAS <NUM> may be mounted with multiple sensors or carry one at a time. The mounting may require an appropriate counterweight. The mounting may allow a radiation detector to be placed further forward or away from the main body of UAS <NUM> than other sensors or cameras on a UAS <NUM>. This configuration may increase the accuracy of the radiation detector by getting it as close to the container or cargo as is possible.

Sensors <NUM> may be configured to interface with server <NUM> through UAS application <NUM> and server application <NUM>. Server <NUM> may be configured to integrate data from multiple instances of UAS <NUM> and stationary detectors <NUM>. UAS <NUM> may implement sensors <NUM> and UAS application <NUM> with, for example, a small single board computer running the data acquisition software on board. UAS <NUM> may be configured to collect information independently of server <NUM> and store information even without a present connection to server <NUM>. UAS <NUM> may be configured to include a smaller version of a threat database, including thresholds of radiation detection. UAS <NUM> may be configured to pinpoint the exact location and altitude where a reading occurred. UAS <NUM> may be configured to report to server <NUM> in real-time, periodically, or after resumption of a communications channel. In one embodiment, UAS <NUM> may be configured to perform a second search based upon its own decisions about data collected from a first search. UAS <NUM> may include, for example, a MicroSD card for onboard data storage allowing data collection to continue in the absence of a connection to the ground based system for later comparison.

Sensors <NUM> for detection of radiation sources may be implemented independently of sensors <NUM> for guidance and physical control of UAS <NUM>. Sensors <NUM> may perform communication with server <NUM> that is independent of the rest of UAS <NUM>. Thus, sensors <NUM> may provide complete access to the data produced by detectors. Moreover, radiation detection may function independently from the rest of UAS <NUM>.

UAS <NUM> may include an antennae or other communication mechanisms to server <NUM>. The communication may include any suitable communications technique or protocol. Preferably, wireless communications may be used. Data collected by sensors <NUM> on UAS <NUM> may be transmitted through any suitable mechanism to server <NUM> including Bluetooth, cellular networks, Wi-Fi, microwave transmission, through a tethered cable, and other mechanisms and protocols. Data stored on UAS <NUM> may be transmitted to server <NUM> periodically or in real-time. The transmissions may be continuous or may involve some determined interval based on testing. Information may be relayed between UASs <NUM>. The relays may be fixed in stationary locations around the area in which UAS <NUM> is being used or on vehicles that can be moved around to follow UAS <NUM>. Transmissions may involve unique coding to ensure authenticity and defeat remote hacking. Data storage may be based on UAS <NUM>, server <NUM>, or other suitable locations.

Different instances of UAS <NUM> may be implemented with different packages of sensors <NUM>. For example, an instance of UAS <NUM> performing initial scans of a container may have less sensitive detectors than an instance of UAS <NUM> that may perform a follow-up scan of the container, given an initial detection of radiation. The second instance of UAS <NUM> may include more sensitive radiation detectors that are better able to precisely determine the nature of a radioactive source, or have a greater active detector volume. For example, the second instance of UAS <NUM> may include arrays of cadmium zinc telluride crystal detectors, larger CsI detectors, or NaI detectors. These might provide greater active detection volume but would limit the operational time of UAS <NUM> due to the power requirements of carrying the greater detector weight. Such a second instance of UAS <NUM> might be deployed automatically or manually in response to a potential primary alarm.

UAS <NUM> or server <NUM> may be configured to securely transmit alerts and other suitable information to customs authorities, port operators, and other relevant entities. The alerts may be made based upon detection of a particular type, quantity, or other characteristic of cargo, such as contraband. Alerts may be sent to US or other customs authorities, port owners and terminal operators, shippers, freight forwarders, relevant shipping companies (especially during on-ship applications), importers, or other relevant entities within the supply chain as appropriate.

System <NUM> is used to inspect cargo shipping containers. While the term "container" may be used to describe nearly any sort of mechanism for shipping goods, system <NUM> may be adapted to search or inspect standardized shipping containers designed and built for intermodal freight transport. Such containers may be loaded and used in ships, railroads, or trucks without unloading and reloading their contents. Furthermore, such containers form the basis for standardized global and regional freight transport. The containers may be known as container, cargo container, freight container, ISO container, shipping container, sea container, ocean container, container van box, sea can, or c can. The containers may be identified according to the ISO <NUM> standard. The containers may be implemented as steel boxes and in standard sizes such as twenty or forty feet, six or twelve-meter standard length with heights of <NUM> feet <NUM> inches (<NUM> meters) or <NUM> feet <NUM> inches (<NUM> meters). The containers may be frequently stacked together in all three directions-height, length, and width. The containers may include doors that open only at one end of the container.

System <NUM> may be configured to solve security problems for containers. Such security problems have become more problematic in recent years, particularly for maritime cargo. System <NUM> may overcome lax security standards for containerized cargo. System <NUM> may enable expanded screening of-for example-the nearly <NUM> million cargo containers that are unloaded in U. ports every year, allowing screening before they are shipped, while they are in transit, and continuously after they arrive in the destination port. Other solutions, such as physical scanning and inspection, are limited in their ability to actually inspect every container, particularly when containers or their doors within or on top of a stack are physically inaccessible. Ports are limited in their ability to perform radiation, x-ray, or gamma ray inspection of containers. Stationary radiation screening in particular has significant limitations, as even if it detects the materials that would go into a radiological dispersion devices ("dirty bombs"), it would be unlikely to detect the fissile material of a nuclear weapon, particularly if the weapon were shielded. Furthermore, stationary radiation detection leads to an enormous number of false positives from naturally radioactive but benign goods like kitty litter and ceramics.

Other solutions involve more rigorous standardization of improved scanning equipment and scanning a higher percentage of cargo at ports around the world. Moreover, other solutions also include the use of sensors and security devices that would be placed on every container and then tracked for any kinds of alerts. Some such proposals include replacing every steel container in the global supply chain with carbon fiber replacements with integrated electronics. There are approximately <NUM> million intermodal containers moving through the global supply chain, and any system that relies on retrofitting all of them with individual sensors and security devices would be a huge undertaking requiring significant coordination and agreement on standards. A mass production model of a smart container has not yet been developed and such containers might be more expensive than existing steel models. Some countries, such as the U. , have taken yet another approach to station inspection officers at foreign ports to inspect containers before they reach the U. However, again, physical inspections of goods are time consuming and disruptive (a single container can take several officers half a workday to complete), and this approach requires cooperation at foreign port authorities. In addition, coordination is difficult because tracking of contents of containers is imprecise. The oversight of the global supply chain resides in various private and public-sector entities. For example, the exporter and importer for a shipment might know exactly what is inside a container, but the shipper may know only basic information from the manifest about the types of goods being carried. Conversely, while the shipper or terminal operator may know exactly where a container is, none of the other entities may have even basic information about where it is located aside from an arrival date at a specific location.

In contrast, embodiments of the present disclosure include system <NUM> that will significantly increase the security of containerized cargo without requiring changes to existing logistical processes. Furthermore, system <NUM> may significantly increase the security of containerized cargo without requiring retrofitting or replacement of the containers that are currently moving through the global supply chain.

Furthermore, system <NUM> may employ sensors at positions not used in the past for cargo inspection. By placing sensors on UAS <NUM>, container data may be analyzed as arising from different positions, altitudes, and precise locations unobtainable through conventional methods. In conventional methods, such as where a container is moved past a stationary sensor, the precise location of a detected source within the container is not obtained. Furthermore, such a method requires moving a heavy container past a sensor. Instead, system <NUM> may rapidly search many containers by placing the sensor on UAS <NUM>. However, placement of sensors <NUM> on UAS <NUM>, rather than on static locations whereby a container is moved past a sensor, incurs technical challenges. For example, searching a container with sensors on UAS <NUM> may incur background noise or detect background radiation that is variable and may cause many false-positive readings. Such background radiation may be simply tared or zeroed out when a container is moved past a static sensor, since the static sensor remains in the same background environment at all times. Users of such conventional methods would thus not expect success when using sensors on UAS <NUM>. Accordingly, embodiments of the present disclosure, by using an unconventional approach to placing sensors <NUM> on UAS <NUM>, may further include solutions to the technical problems generated by the placement of such sensors. For example, use of small, mobile, and high-sensitivity detectors on UAS <NUM> rather than large fixed detector installations may require distinguishing measurements from sensors <NUM> from the natural background and, more importantly, transient phenomena (i.e., non-threatening radiation sources in cargo). System <NUM> may include a library of known sources of normally occurring radioactive materials that do not constitute threats in addition to the library of radioactive threats.

In some embodiments, UAS <NUM> may penetrate the sides of containers or vehicles and can insert cameras or other sensors inside the area to be searched. Such a UAS <NUM> may be used to penetrate the side wall or roof of a suspicious container with a small hole that would allow a camera or other sensor to be snaked into the container and see whether the contents matched what was described on entry documents and take other readings. As an example, if a passive radiation hit were found on a container on a ship that was supposed to be carrying textiles, UAS <NUM> could penetrate the side wall of the container and insert a camera that would allow the relevant authorities to see whether there was something dramatically different inside, such as a radiological weapon, without having to open or unpack the container.

UAS <NUM> may be used in various applications. In ports, UAS <NUM> may be used to search or scan containers: alongside container stacks using search algorithms; along the ends of containers using search algorithms; in stationary locations where cargo is being moved past for logistical purposes such as being loaded onto a ship; following a pattern to use active radiation sources and passive radiation detectors to inspect containers; landed or attached to the side of containers, penetrate the wall, and then insert sensors or cameras; or between container stacks using search algorithms. On ships, UAS <NUM> may also be used to search between container stacks using search algorithms or manually operated to search specific containers. At intermodal transportation centers, the UAS <NUM> may also be used to search containers using search algorithms. At factories, docks, or other locations where cargo is loaded, UAS <NUM> may search the cargo before it is loaded into the containers. Next to train tracks, UAS <NUM> may search containers while trains move by. In places adjacent to trucks carrying the containers, UAS <NUM> may search containers as they pass by. In other places, UAS <NUM> may be able to scan contents of a building, or other type of vehicles, such as aircraft or private boats, wherein the contents are unknown.

UAS <NUM> may search containers or groups of containers based upon search patterns. The patterns may be performed autonomously by UAS <NUM> in that the search patterns may be executed by UAS <NUM> rather than manually by a human operator of UAS <NUM>. In some embodiments, a human operator may select a search algorithm to be performed by UAS <NUM> for a given container or a group of containers. In another embodiment, UAS <NUM> may be configured to select a search algorithm based upon an identification of a given container, layouts or stacks of containers, port or other geographical location, expected contents of a container, data from a previous scan or inspection of a container, or other suitable criteria. The search pattern may be based upon the type of sensor that is to be used. Any suitable number and kind of searches may be performed sequentially for a container or a group of containers. The search pattern or algorithm may be based upon testing of containers.

<FIG> illustrate example search patterns of operation of UAS <NUM>, according to embodiments of the present disclosure. The searches may be performed on a longest side of a container, but may also be performed on a bottom, top, or end of a container. A search may be entered for a given container at a most proximate place to UAS <NUM>. The searches of <FIG> include an initial search. A second, more detailed search is conducted if the initial search provides information that a threat is found in a container.

<FIG> illustrates an example search pattern performed by UAS <NUM> including scanning individual containers in a vertical pattern from the side, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed by UAS <NUM> including scanning individual containers horizontally from the side, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed by UAS <NUM> including scanning ends of multiple containers in a stack, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed by UAS <NUM> including scanning in a vertical pattern while spending more scan time on a lower half of a container, according to embodiments of the present disclosure. Such techniques may be used on cargo that is expected to, or may have been previously determined to be, in the lower part of the container.

<FIG> illustrates another example search pattern performed by UAS <NUM> including scanning in a horizontal pattern while spending more scan time on a lower half of a container, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed by UAS <NUM> including searching contents of a container while stopping in intervals for a specified period of time, according to embodiments of the present disclosure. For example, every N feet during a scan performed according to any of the other example searches of the present disclosure, the UAS may pause, for example, for <NUM> seconds.

<FIG> illustrates an example search pattern performed UAS <NUM> including searching multiple faces of a container. The scan may include an entire container scan. The scan may search two faces, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed UAS <NUM> including searching the door of a container, according to embodiments of the present disclosure.

<FIG> illustrates an example search pattern performed UAS <NUM> including searching a single side of a container, according to embodiments of the present disclosure.

<FIG> illustrates configuration and operation of system <NUM> to detect threats, according to embodiments of the present disclosure.

System <NUM> may include a threat database <NUM>. Threat database <NUM> may be implemented in any suitable manner, such as by a relational database, look-up table, or other database. Threat database <NUM> may include descriptions and characteristics of various dangerous materials, such as radioactive substances. Moreover, threat database <NUM> may include descriptions and characteristics of dangerous materials as they would be perceived while shielded or located inside other materials. For example, threat database <NUM> may include characteristics of a radiological dispersion device containing a particular type of radioactive material located within a certain width of metal shielding, further located within a steel shipping container. Conversely, threat database <NUM> may include descriptions and characteristics of materials that do not constitute a threat that nevertheless may give off a radioactive signature. For example, certain kinds of pottery may give off radioactive signatures. However, these signatures may indicate that the pottery is not a threat, despite giving off radioactive signals. The signatures and threshold levels of matching the signatures may be set by users and developed through situational testing. In addition, elements of threat database <NUM> may be developed with Monte Carlo simulations of radiation emissions and the signals that would be identified by different sensors at different amounts of loiter time.

Threat database <NUM> may include or account for various variables. In one embodiment, threat database <NUM> may include signatures for different radiation sources. These radiation sources may include radiological dispersion devices, fissile materials, and nuclear weapons. In another embodiment, threat database <NUM> may include signatures for various shielding and cargo configurations within containers or conveyances. For example, threat database <NUM> may include signatures for different shielding thicknesses of lead or steel. In yet another embodiment, threat database <NUM> may include signatures for various container configurations and stack depths. In still another embodiment, threat database <NUM> may include signatures for different conveyances, such as small boats and general aviation aircraft, with both shielded and unshielded radiation sources. In another embodiment, threat database <NUM> may include signatures for varying speeds, distances, search routes, and loiter times for different detector configurations of UAS <NUM>. For example, signatures for uranium for positioning of UAS <NUM> at different external distances from the container and loiter times of UAS <NUM> may be included in threat database <NUM>.

In one embodiment, system <NUM> may measure environmental radiation as a function of time and location during inspection of a container. This environmental radiation may be used to form a background spectrum map <NUM>. Data collected by UAS <NUM> may be used in view of background spectrum map <NUM>. In particular, data collected by UAS <NUM> may be normalized by background spectrum map <NUM> before being compared to elements of threat database <NUM>.

Server <NUM> may be configured to generate and update background spectrum map <NUM> from data from stationary detectors <NUM>. Stationary detectors <NUM> may operate autonomously, recording spectra as a function of time. These spectra are then reported to server <NUM>. The spectra may include data fields such as the spectra as a function of time (discretized in time intervals), the position of the detector, and any available directional information regarding the detected radiation. Each of stationary detectors <NUM> may report its response function to varying energies of radiation on regular intervals. These data are reported to the central system. Server <NUM> may monitor data from stationary detectors <NUM> to look for changes in the background radiation levels and types. Changes in the background radiation levels and types may indicate that either a background detector is malfunctioning or a radioactive source had been brought near the detector. In the latter case, UAS <NUM> may be deployed to check for a possible alarm source. Stationary detectors <NUM> may provide significant improvements over previously used background monitoring, such as a fingerprint for an empty container. This may occur due to the longer integration time of the elements of system <NUM>, rather than a calibration of a container which occurs at some intervals, and the ability of system <NUM> to map the background both spatially and temporally to suppress dependence on environmental factors.

If stationary detectors <NUM> are configured in a self-shielding array, the principal direction of each energy region of the recorded background spectrum may be recorded to provide more information to the background mapping routine. The direction of each energy component may be reconstructed using the relative spectrum counts at each energy in the different volumes of the detector array, according to measured or simulated attenuation of the detector materials. The directionality information may be generated along with an uncertainty (i.e., a cone in which the background radiation source is determined to be within).

Information from stationary detectors <NUM> may be used to estimate the background spectrum at all points throughout the inspection area to generate background spectrum map <NUM>. Depending on the complexity of the spatial distribution of the background radiation, the number and quality of sensors in the network, and the desired or necessary quality of background spectrum map <NUM> (which may be affected by the available inspection times using the UAS and the level of background radiation), several methods may be used to interpolate data from stationary detectors <NUM>. These may include, for example, 2D bilinear and spline methods, and kriging.

Background spectrum map <NUM> may include dimensions or versions for specific isotopes detected in the background environment. Background spectrum map <NUM> may be stored with a lookup key of position. Thus, background spectrum map <NUM> may yield expected background spectra as a function of position, which may be queried by server <NUM> or UAS <NUM>. Additionally, in the case of aerial UAS operation, the two-dimensional mapping in background spectrum map <NUM> may be used to estimate background radiation throughout the entire three-dimensional geometric space geometrically using the (<NUM>/(r^<NUM>)) fall-off of radioactive source strength and by estimating the attenuation of any intervening material. The strength of detected radiation as a function of position may be fit assuming a point source to the (<NUM>/(r^<NUM>)) fall-off function, using a technique such as least-squares fitting, or the data may be used for a maximum likelihood analysis in cases where a point source poorly describes the data to determine the spatial extent of the detected source. The attenuation estimation may be conducted as a function of energy, using either a simulation of the intervening materials using software or by using a rough estimate from known gamma ray attenuation databases.

In some cases, stationary detectors <NUM> may be unavailable. This may arise from considerations such as cost or expedience of system deployment. In such cases, system <NUM> may be implemented and deployed in a mode in which sensors <NUM> on UAS <NUM> provide their own background monitoring. In this scenario, UAS <NUM> may be deployed in a regular pattern throughout the inspection area while transient events-such as the movement of containers or vehicles-are not occurring. This recorded data may be transmitted to server <NUM>, which may process the data using the mapping procedure to generate background spectrum map <NUM>. Such background mapping might need to be conducted on a regular basis to account for seasonal effects, weather effects, and other environmental changes in the inspection area.

During an initial search of a container, UAS <NUM> may gather data. The data may be gathered according to, for example, search patterns shown above. For detection, different materials may require different times of measuring for radioactive sources. Thus, UAS <NUM> may perform a search pattern with a suitable amount of time to detect a given material with sufficient resolution (the ability to tell particles of different energies apart) and statistical power (the ability to detect enough particles to be probabilistically confident that a radioactive source is or is not present, which is related to the size, type, and counting time of the detector and the source strength). UAS <NUM> may perform search patterns of various durations depending upon the types of materials that are desired to be detectable. In one embodiment, the data may be analyzed by UAS <NUM>. In another embodiment, the data may be sent to server <NUM>, which may analyze the data. The data may be analyzed by evaluating signatures of the data with signatures in threat database <NUM>.

If an initial search of the container has yielded a probability within a designated threshold that the data matches a threat listed in threat database <NUM>, a second search is initiated by a different UAS <NUM>. The second search may be longer in duration.

Data recorded by UAS <NUM> may include the data from sensors <NUM> as a function of time, location, and speed. The location and speed may be recorded to determine flight paths actually taken as well as density of measurements. Density of measurements may affect the sensitivity of system <NUM> to radioactive materials. Denser, slower flight paths increase sensitivity while increasing inspection time. However, faster flight paths decrease sensitivity but allow faster, more efficient inspection. Consequently, an initial search may be performed using a less dense flight path while a second search may be performed using a denser flight path.

Data recorded by UAS <NUM>, stationary detectors <NUM>, and server <NUM> may include raw spectral, position, and time data used internally in system <NUM> to produce alarm decisions. The data may also include alarm decisions reported to the user. Furthermore, the data may include a log of the system's activities, as detected radiation and corrective actions.

Data captured by sensors <NUM> may be processed by UAS <NUM> to produce a calibrated energy spectrum. This spectrum may be stored as a histogram. The histogram may be stored as associated with the average time and position at which it was recorded. The intervals for the generation of each histogram may be set manually or dynamically based on the inspection conditions. Data analysis performed by server <NUM> or UAS <NUM> may include a list of significant particle energies detected along with the rate of detection, location, etc. The data analysis may include the system's estimate of what type of material gave rise to the detected particle energies. The data analysis may further include an estimate of the probability that the detections exceed the background level. The data analysis may also include an estimate of whether the detections match a known, safe source identified in threat database <NUM> rather than an unknown source or a known, dangerous source.

As an example of an estimation of whether safe or dangerous source, if system <NUM> detects with high confidence the presence of a <NUM> MeV gamma ray that is a unique signature of U-<NUM>, system <NUM> would generate an alarm. Conversely, if system <NUM> detects with high confidence high levels of the <NUM> MeV gamma ray from K-<NUM>, this may simply indicate the presence of a load of potassium salt. Signatures for each type of material may be included within threat database <NUM>.

Users may set thresholds for system <NUM> for alarms. Particular sources of radiation may be selected for monitoring, as well as associated parameters such as source strength.

Analysis of data captured by sensors <NUM> may include determining if a non-background radiation source is present and, if so, determining the nature of the radiation source. Determining whether a non-background source is present may be achieved by comparing the spectra recorded by UAS <NUM> to the recorded background of background spectrum map <NUM>. Determining the nature of the radiation source may be achieved by comparing the spectra to known radiation sources and their expected appearance to the system as recorded in threat database <NUM>.

First, the spectra recorded by sensors <NUM> may be decomposed according to determine the energies of the source particles that lead to the detection. Each type of radioactive material is associated with characteristic radiation energies and particle types, and thus the decomposed spectrum may be used to assess the probability that a given material is present. Second, each known possible radiation source may be compared to the decomposed spectral signature. The possible radiation source may be identified locally in UAS <NUM> or as recorded in threat database <NUM>. Any potential matches may be reported. If a source is detected, system <NUM> may localize the source as described below and proceed to determine whether the source should be reported as an alarm.

For each detector, including sensors <NUM> and stationary detectors <NUM>, a characterized detector response may be used to extract the source energies that contributed to the detected spectrum using known techniques. This process may include methods such as Bayesian unfolding, D' Agostini iteration, or Tikhonov regularization. From this unfolding, the energies of the source particles may be reconstructed, which provides a unique signature of most radioactive materials. With the detected radiation from UAS <NUM> and stationary detectors <NUM> characterized, the analysis may proceed by direct comparison of the decomposed spectral energies in the background (extracted from the background map at the position of the inspection) and data from UAS <NUM>. This may be performed by a standard statistical comparison of counts at each detected energy to determine the excess of the detected radiation above the known background. Alternatively, a convolution of data from both stationary detectors <NUM> and sensors <NUM> may be used to produce the background spectrum map <NUM>. This instance of background spectrum map <NUM> may be subtracted from measurements by sensors <NUM> to leave only counts from transient radiation sources. This approach may be advantageous since the background radiation is also measured in UAS <NUM> whenever a transient source is not present, which provides a basis to mesh the two signals. With the spectrum subtracted from data measured by sensors <NUM>, the unfolding analysis may proceed more smoothly by eliminating the background or ignorable contributions to the spectrum from consideration. In any case, the result of considering this step may be to produce a function at each position along the inspection path that encodes the significance of above-background radiation levels at each detectable energy for each detectable particle type.

In addition, to reconstruction of characteristic specific energies, system <NUM> may discern information about the shielding of the source from detection due to other materials in the area if the source energy is determined.

When any significant source is detected, system <NUM> may determine the location of that source. Source localization may be achieved by first associating the spectral data and the identifiable components of the detected radiation with the position of the inspecting UAS <NUM> in the inspection area. This may be performed by server <NUM>. Server <NUM> may generate a data map from data detected by UAS <NUM>. Such a data map may be similar in structure to background spectrum map <NUM>. Interpolation for such a data map interpolation might not be needed since UAS <NUM> moves continuously throughout the inspection area. The path of UAS <NUM> may be discretized in time. The discretization may be performed in units to be determined by testing, and may be adjusted for different applications, and the integrated spectrum and average UAS position over that interval is determined. Each integrated spectrum may be analyzed to extract likely sources of radiation detected at each location on the path.

Using the points in space and their associated detected radiation sources, system <NUM> may determine the point in space at which the radiation source resides in two ways. First, system <NUM> may perform a reconstruction that assumes that each detected radiation type is due to a single point source. Then, system <NUM> may find the best position in space to account for the detected radiation along the path, assuming a (<NUM>/(r^<NUM>)) fall-off in the detected radiation. Uniform shielding may be assumed. A standard least-squares fitting algorithm may be used. Second, system <NUM> may use a maximum likelihood distribution approach. The maximum likelihood distribution approach may require greater computing resources, data quality, and programming effort, but may provide the capability of better localizing sources that are distributed in space or shielded in a non-uniform fashion.

Based on the detected decomposed energies, system <NUM> may then attempt to match those energies with known possible radiation sources. This may be performed by matching the specific detected energy (along with an uncertainty generated in the unfolding procedure) to a list of materials that generate radiation of a type or energy consistent with the detection. Such a list may be included in, for example, threat database <NUM>, or information derived therefrom. If multiple detectable energies of radiation are associated with a given material type (such as the two distinct gamma rays of Co-<NUM>), detection of this material type might require probabilistically matching the entire radioactive spectrum of that material. For each material, the effective shielded strength of the radiation is determined, if available. The shielded strength, along with the known natural radioactivity of the specific material, can be used to constrain a determined amount of material present given a known amount of shielding.

The detected materials may be compared against entries in threat database <NUM>. A given sample detected by UAS <NUM> may include multiple materials. The logical combination of such multiple materials may yield an alert that would not exist for individual determinations of the materials existing separately. For instance, a mix of detected materials consistent with a uranium nuclear weapon may be considered more threatening than detection of a single material present in such a device, which would be reflected in the alarm report. Alternatively, a mix of radioactive materials that is common to granite (a common source of non-threatening radioactivity) would be considered less threatening and may be excluded from alarm reports. This may reduce the number of false positives requiring intervention that are reported to the user.

Upon a radiation source detection event, the position of UAS <NUM> at such an event may be reconstructed by analysis of the data that was generated as a function of position and speed. The position may be passed to the instance of UAS <NUM> to conduct the second search. With this information, UAS <NUM> may be directed to approach the source as closely as possible. By approaching the source closely, data on a known source may be gathered more rapidly to permit clearer identification of the source and whether it constitutes a threat.

System <NUM> may produce an alarm when radioactive material is present in an inspection area that meets a user's definition of a threat. Before comparing data collected from UAS <NUM> to information from threat database <NUM>, the data may be integrated, compared, or otherwise adjusted with background sources of radiation to conduct a statistics-based analysis to determine the probability of the presence of a threat. These probabilities may be weighed against user thresholds and definitions, and in the event of a detection in excess of these thresholds, the user may be notified of the nature of the threat so as to take action.

Radioactive materials emit particles that may be detected using radiation detectors such as sensors <NUM> of UAS <NUM>. The energies and types of these particles may provide specific information about the type of material that emitted them. For example, sensors <NUM> of UAS <NUM> may be configured to detect gamma ray (photons with energies from <NUM> to approximately <NUM> MeV) or neutron radiation. UAS <NUM> or server <NUM> may determine whether radiation gathered in a first, second, or other search of a container is anomalous relative to known sources of radiation identified in threat database <NUM>, analogous to known threats identified in threat database <NUM>, or anomalous relative to naturally occurring radiation. For example, normally occurring sources of radiation include the <NUM> MeV gamma ray from the isotope K, which occurs in cement, biological material, and many other sources. Another normally occurring source of radiation may include the <NUM> MeV gamma ray T1-<NUM>, a naturally occurring decay product of uranium, which may be present in significant amounts in a shipment of granite.

Conversely, radioactive materials that would be identified as a threat may include radiological dispersion devices, radiological dispersion devices, nuclear weapons, or the materials required for such weapons. These may each emit a characteristic radiation spectrum, such as a distribution of particles as a function of energy. These may uniquely identify each material-if sufficiently and appropriately measured, as provided by system <NUM>.

As an example of radioactive signals that may include both dangerous sources and safe sources from the background, UAS <NUM> may include thermal neutron detectors in sensors <NUM>. Such sensors may be capable of detecting the neutrons produced by the spontaneous fission of certain nuclear materials, such as the strong neutron emission rates of Pu-<NUM>. However, naturally occurring neutrons may constitute background events. These background events may be defined by the rate of high energy cosmic rays, which may be characterized by independent measurements.

Operation of system <NUM> may be conducted in a fully autonomous operation mode. The fully autonomous operation mode may include sending a UAS <NUM> along a preset path with respect to a container, dynamically reacting to obstacles, dynamically reacting to potential detections of radiation sources, processing the data, and comparing the process data to the user's defined alarm thresholds to make alarm decisions. In this mode, the data may be analyzed continuously to generate alarms, or the data collected on UAS <NUM> may be stored and reported to the server <NUM> at a later time. The latter option may be useful in situations with poor connectivity or in cases where very long inspection times of fixed arrangements are possible, such as a shipping yard of containers waiting overnight.

Users may optionally control UAS <NUM> in particular circumstances for greater flexibility. For instance, UAS <NUM> may be controlled or flown manually, a user may inspect data and manually trigger an alarm, or the user may direct the UAS manually simply for the purposes of collecting data rather than making alarm decisions, such as in testing, calibration, or debugging efforts.

In addition, system <NUM> may operate in an automated response to other alarm sources. For example, other systems in a location such as a radiation portal monitor or a quay crane-mounted systems may generate an alarm. Since typically such other systems only have a few seconds to measure radiation from a source, they generally require a follow-up measurement to collect sufficient information to make an alarm decision. System <NUM> may perform such a follow-up measurement by providing the capability for UAS <NUM> to carry out the secondary inspection. Inspection may be made of containers near the object or objects generating the alarm. The alarm may provide position information, spectral data, for which UAS <NUM> may begin its search.

Following the spectral comparison described in the previous section, if the radiation levels and types meet the conditions set by the user an alarm report is generated. The conditions set by the user consist of threshold values in the probability that detected radiation is inconsistent with the known background radiation, the probability that the detected radiation is consistent with a known naturally occurring radioactive material (NORM) source (which may be paired with known manifest information when available), and the set of radioactive materials that the user is interested in detecting. Precise values of these thresholds will depend on the end user's needs based on factors such as desired false positive and false negative rates, level and knowledge of the background radiation (which improves with time due to the background network, when available), and outside/intelligence information regarding the nature of potential threats.

In the event of an alarm, a user of system <NUM> may be provided the nature of the alarm. This may include the probable radioactive material detected, the similarity to known normally occurring radiation sources, and spatial localization of the material. Once an alarm decision is made and reported to a user, system <NUM> may finalize the alarm and UAS <NUM> may proceed on a flight pattern to inspect other portions of the area of interest. In other cases, UAS <NUM> may remain in the vicinity of the alarm to collect more data increase the accuracy of the data and refine the alarm report. UAS <NUM> may proceed on a preexisting flight path after making a detection while system <NUM> automatically dispatches another instance of UAS <NUM> to inspect the alarm area in more detail.

Claim 1:
A system for scanning shipping containers, comprising an unmanned vehicle (<NUM>), the unmanned vehicle (<NUM>) having:
a sensor (<NUM>);
a processor (<NUM>);
a memory (<NUM>) including instructions for execution, the instructions, when executed by the processor (<NUM>), for causing the unmanned vehicle (<NUM>) to:
move along faces of a shipping container; and
record container data collected from the sensor (<NUM>) while scanning the shipping container; characterized in that
the system comprises a server (<NUM>) configured to:
make a preliminary determination that the container may include a given material based upon the container data collected by the unmanned vehicle (<NUM>) while moving; and
based upon the preliminary determination that the container may include the given material, order another unmanned vehicle inspection of the container to gather additional information using unmanned vehicle sensors.