Abstract: Systems and methods can support identifying pairings and channel parameters in short-range wireless communications such as bluetooth low energy interfaces. Radio frequency sensors may be positioned within an electromagnetic environment where a master wireless device and a slave wireless device share short-range wireless communications. Signals transmitted between the master wireless device and the slave wireless device can be received by the radio frequency sensors. Inter-arrival times for packets within the received signals may be identified. Statistics of the inter-arrival times can be analyzed to identify connection intervals between the master wireless device and the slave wireless device as well as back-to-back interval exchanged within the connection intervals. Packet header contents may be used to reconcile the estimated timing parameters and time slots. Pairings between the master wireless device and the slave wireless device may be identified and tracked along with communication channel parameters.

Abstract: Systems and methods can support identifying pairings and channel parameters in short-range wireless communications such as bluetooth low energy interfaces. Radio frequency sensors may be positioned within an electromagnetic environment where a master wireless device and a slave wireless device share short-range wireless communications. Signals transmitted between the master wireless device and the slave wireless device can be received by the radio frequency sensors. Inter-arrival times for packets within the received signals may be identified. Statistics of the inter-arrival times can be analyzed to identify connection intervals between the master wireless device and the slave wireless device as well as back-to-back interval exchanged within the connection intervals. Packet header contents may be used to reconcile the estimated timing parameters and time slots. Pairings between the master wireless device and the slave wireless device may be identified and tracked along with communication channel parameters.

Abstract: Described herein are systems and methods for detecting microwave pulses. An example method may include identifying, by a first sensor, a first signal in an environment, the first signal including a signal in a microwave frequency range. The example method may also include determining first data associated with the first signal, the first data being associated with one or more parameters. The example method may also include determining that the first data exceeds a threshold. The example method may also include sending, based on the determination that the first data exceeds a threshold, an alert.

Abstract: Systems and methods can support surveying mobile wireless base stations. One or more radio frequency antennas can be positioned within an electromagnetic environment where user equipment devices are serviced by base stations. One or more radio frequency receivers can electrically couple signals from the radio frequency antennas. The signals can be scanned by the radio frequency receivers for synchronization with one or more of the base stations. Downlink channels from the identified base stations may be decoded. Performance metrics may be collected regarding the decoded downlink channels. Optimization parameters may be established to improve effective monitoring of the base stations under constraints associated with the radio frequency antennas and the radio frequency receivers.

Abstract: Systems and methods can support identifying pairings and channel parameters in short-range wireless communications such as bluetooth low energy interfaces. Radio frequency sensors may be positioned within an electromagnetic environment where a master wireless device and a slave wireless device share short-range wireless communications. Signals transmitted between the master wireless device and the slave wireless device can be received by the radio frequency sensors. Inter-arrival times for packets within the received signals may be identified. Statistics of the inter-arrival times can be analyzed to identify connection intervals between the master wireless device and the slave wireless device as well as back-to-back interval exchanged within the connection intervals. Packet header contents may be used to reconcile the estimated timing parameters and time slots. Pairings between the master wireless device and the slave wireless device may be identified and tracked along with communication channel parameters.

Abstract: Systems and methods can support identifying pairings and channel parameters in short-range wireless communications such as bluetooth low energy interfaces. Radio frequency sensors may be positioned within an electromagnetic environment where a master wireless device and a slave wireless device share short-range wireless communications. Signals transmitted between the master wireless device and the slave wireless device can be received by the radio frequency sensors. Inter-arrival times for packets within the received signals may be identified. Statistics of the inter-arrival times can be analyzed to identify connection intervals between the master wireless device and the slave wireless device as well as back-to-back interval exchanged within the connection intervals. Packet header contents may be used to reconcile the estimated timing parameters and time slots. Pairings between the master wireless device and the slave wireless device may be identified and tracked along with communication channel parameters.

Abstract: Systems and methods can support identifying threats in short-range wireless communications, such as Bluetooth, using one or more radio frequency sensors to receive signals transmitted between a master device and a slave device. Packets can be identified within the received signals and designated as originating from the master device or from the slave device. The wireless interface can be identified as synchronous or asynchronous. Lengths of data may be identified for data payloads within the packets. Total aggregate data lengths may be calculated for both the master and the slave transmissions. Time slot utilization statistics can be computed. A connection type category may be determined using these wireless connection features. The connection type may be for peripherals, streaming audio, two-way headsets, object exchange, data tethering, and so forth.

Abstract: Systems and methods can support identifying pairings and channel parameters in short-range wireless communications such as bluetooth low energy interfaces. Radio frequency sensors may be positioned within an electromagnetic environment where a master wireless device and a slave wireless device share short-range wireless communications. Signals transmitted between the master wireless device and the slave wireless device can be received by the radio frequency sensors. Inter-arrival times for packets within the received signals may be identified. Statistics of the inter-arrival times can be analyzed to identify connection intervals between the master wireless device and the slave wireless device as well as back-to-back interval exchanged within the connection intervals. Packet header contents may be used to reconcile the estimated timing parameters and time slots. Pairings between the master wireless device and the slave wireless device may be identified and tracked along with communication channel parameters.

Abstract: Systems and methods can support identifying threats in short-range wireless communications, such as Bluetooth, using one or more radio frequency sensors to receive signals transmitted between a master device and a slave device. Packets can be identified within the received signals and designated as originating from the master device or from the slave device. The wireless interface can be identified as synchronous or asynchronous. Lengths of data may be identified for data payloads within the packets. Total aggregate data lengths may be calculated for both the master and the slave transmissions. Time slot utilization statistics can be computed. A connection type category may be determined using these wireless connection features. The connection type may be for peripherals, streaming audio, two-way headsets, object exchange, data tethering, and so forth.

Abstract: Systems and methods can support improved position locating of wireless devices. A suite of machine learning models may be established. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless signals associated with communications between the user equipment devices and the base station. The suite of machine learning models may be trained using wireless signals received from user equipment devices in known positions. The trained suite of machine learning models can be applied to wireless signals received from user equipment devices in unknown positions. The trained suite of machine learning models may be used to estimate the unknown positions. The estimated positions may be refined using additional information from the user equipment devices.

Abstract: Systems and methods can support improved position locating of wireless devices. A suite of machine learning models may be established. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless signals associated with communications between the user equipment devices and the base station. The suite of machine learning models may be trained using wireless signals received from user equipment devices in known positions. The trained suite of machine learning models can be applied to wireless signals received from user equipment devices in unknown positions. The trained suite of machine learning models may be used to estimate the unknown positions. The estimated positions may be refined using additional information from the user equipment devices.

Abstract: Systems and methods can support determining a physical position of wireless devices. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless downlink signals transmitted from the base station to the user equipment devices. Network identifiers may be extracted from the received wireless downlink signals. User equipment devices may be associated with the extracted network identifiers. Radio channel allocations may be determined for the extracted network identifiers. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Signal strength indicators from the received wireless uplink signals can be associated with the extracted network identifiers. Physical positions of the user equipment devices can be determined by analyzing the associated signal strength indicators.

Abstract: Systems and methods can support identifying wireless devices from radio frequency sensors positioned within an electromagnetic environment where one or more user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Data samples can be generated from the received signals. Frequency domain samples can be computed from the data samples. The data samples can be partitioned in time and frequency to generate spectrum tiles. The spectrum tiles can be statistically aggregated in a frequency domain to reduce the quantity of data samples. A clustering algorithm may be applied to the statistically aggregated spectrum tiles. Unique instances of the user equipment devices may be identified from the clustered spectrum tiles to enumerate the instances of the user equipment devices.

Abstract: Systems and methods can support determining the physical position of a wireless device. Radio frequency sensors may be positioned within an electromagnetic environment where user equipment devices are serviced by a base station. The radio frequency sensors can receive wireless uplink signals transmitted from the user equipment devices to the base station. Data samples can be generated from the received signals. The data samples may be partitioned in time and frequency to generate spectrum tiles. The spectrum tiles may be statistically aggregated to reduce the quantity of data samples. A clustering algorithm may be applied to the statistically aggregated spectrum tiles to determine clusters of spectrum tiles. Signal parameters associated with respective clusters of spectrum tiles may be computed. Physical locations associated with user equipment devices can be estimated from the signal parameters associated with the respective clusters of spectrum tiles.

Abstract: Systems and methods can support a sensor mesh and signal transmission architecture for electromagnetic signature analysis and threat detection. Sensor antennas may be deployed within an electromagnetic environment. A configurable antenna feed network can couple radio frequency signals from the antennas to both software-defined radio receivers and hardware-defined radio receivers. A raw signal analysis engine associated with the software-defined radio receiver can receive digital samples of the radio frequency signals, identify signal features within the digital samples, and generate signal feature vectors from the identified signal features. A signal feature network can receive the signal feature. A signal aggregation and analysis engine can receive the signal feature vectors from the signal feature network, aggregate the signal feature vectors, process the signal feature vectors, and identify wireless attacks according to the signal features within the signal feature vectors.

Abstract: Systems and methods can support identifying radio transmissions associated with autonomous or remote-controlled vehicles. Radio frequency signals may be received using one or more sensors, wherein the sensors comprise radio receivers. Radio frequency fingerprints may be identified within one or more of the radio frequency signals, wherein the radio frequency fingerprints comprise radio signal characteristics or radio hardware identifiers. A stored radio frequency fingerprint may be determined as matching the received radio frequency fingerprint. A motion characteristic may be computed. The received radio frequency fingerprint may be associated with an autonomous or remote-controlled vehicle based upon the stored radio frequency fingerprint or the motion characteristic. Information regarding the identified autonomous or remote-controlled vehicle may be presenting to one or more operator interfaces.

Abstract: Systems and methods can support threat detection using electromagnetic signatures. One or more sensors comprising radio receivers may receive radio frequency signals within an electromagnetic environment. Radio frequency signatures may be identified from one or more of the radio frequency signals. A baseline electromagnetic environment may be established from the radio frequency signatures. The radio frequency signatures may be monitored over time to detect variations from the baseline electromagnetic environment. Variations in the electromagnetic environment may be evaluated against stored threat signatures. Operator interfaces may present indications of threats determined from evaluating the variations in the electromagnetic environment.

Abstract: Systems and methods can support a computational signal processing architecture for electromagnetic signature analysis and threat detection. A plurality of sensor antennas can couple a radio frequency signal into a radio receiver. The radio receiver can generate digital samples of the signal. A raw signal analysis engine can identify signal features within the digital samples, generate signal feature vectors from the identified signal features, decode signal content from the signal feature vectors, and transmit the signal feature vectors into a signal feature network. The signal feature vectors may be aggregated from the signal feature network into a signal aggregation and analysis engine. The signal aggregation and analysis engine can refine feature vectors through processing such as identifying wireless attacks according to the signal features within the signal feature vectors. One or more operator interfaces and one or more analysis databases may support these operations.

Abstract: Systems and methods can support coprocessing radio signals and video to identify and locate a radio transmitter. Positions and orientations for cameras and RF sensors may be maintained. An RF signature associated with the radio transmitter may be received from the RF sensors to determine an RF persona. A first physical location for the radio transmitter may be estimated according to a physical radio propagation model operating on RF signals. A video stream from one or more of the cameras may be received. An individual may be identified in the video stream using computer vision techniques. A second physical location for the radio transmitter may be estimated from the video stream. Relationships may be established between the first physical location and the second physical location and between the RF persona and the identified individual. The relationships may be presented to an operator interface.

Abstract: Systems and methods can support detecting and identifying threats associated with wireless devices. A radio receiver can collect radio frequency signals from one or more sensor antennas positioned within an electromagnetic environment. The receiver can generate data samples representing at least a portion of the radio frequency signals. Feature vectors can be generated comprising at least a portion of the data samples and attribute information. The attribute information can describe one or more features of a communicated signal within the radio frequency signals. Content of the feature vectors may be compared against signatures of known signals to identify radio frequency signals associated with a wireless attack. Content of the feature vectors may be compared against templates of known attacks to classify the identified wireless attacks. Threat information associated with the wireless attacks may be presented to one or more operator interfaces.