A variety of methods and devices for a low-probability of intercept, low probability of denial (LPI/LPD) method of providing RF signals in denied spaces are disclosed. A phased array antenna converts an omnidirectional communication system into a highly directional system. This factor coupled with the precise timing between the transmit and receive sets, establishes a precise distance measurement between the two sets. Using three or more transceivers enables the composition of an ad hoc network of nodes that can be used to establish an available mesh of position and timing that can be accessed by operators within the radio boundaries of the mesh. Portable transceivers reestablish position, navigation and timing (PNT), thereby forming an ad-hoc network. Where the ad-hoc network PNT mesh can intersect with a GPS signal that is outside of the denied environment, the ad-hoc network mesh can detect the GPS position and timing.

An imaging system to reconstruct a reflectivity image of a scene including an object moving with the scene. A tracking system to track a deforming object to estimate an object deformation for each time step. Sensors acquire snapshots of the scene, each acquired snapshot of the object includes measurements in the object deformation for that time step, to produce a set of object measurements with deformed shapes over the time steps. Compute a correction to estimates of object deformation for each time step, with matching measurements of the corrected object deformation for each time step to measurements in the acquired snapshot of object for that time step. Select a corrected deformation over other corrected deformations for each time step, according to a distance between the corrected deformation and the estimate of the deformation, to obtain a final estimate of the deformation of the deformable object moving in the scene.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

Various embodiments wirelessly detect micro gestures using multiple antenna of a gesture sensor device. At times, the gesture sensor device transmits multiple outgoing radio frequency (RF) signals, each outgoing RF signal transmitted via a respective antenna of the gesture sensor device. The outgoing RF signals are configured to help capture information that can be used to identify micro-gestures performed by a hand. The gesture sensor device captures incoming RF signals generated by the outgoing RF signals reflecting off of the hand, and then analyzes the incoming RF signals to identify the micro-gesture.

This document describes techniques for fine-motion virtual-reality or augmented-reality control using radar. These techniques enable small motions and displacements to be tracked, even in the millimeter or sub-millimeter scale, for user control actions even when those actions are small, fast, or obscured due to darkness or varying light. Further, these techniques enable fine resolution and real-time control, unlike conventional RF-tracking or optical-tracking techniques.

In some implementations, a server may receive sensor data from a plurality of detectors, including receiving first sensor data from a first detector and second sensor data from a second detector. The server may use the sensor data to retrain a detector-based machine learning algorithm to create an updated detector-based machine learning algorithm. The server may send the updated detector-based machine learning algorithm to the first detector and the second detector. For example, the updated machine learning algorithm may be trained to identify at least one pest that the machine learning algorithm is incapable of identifying.

In some implementations, a computing device may receive a notification from a pest detector. In response, the computing device may initiate execution of a software application and display a user interface. The user interface may enable playback of the notification, such as displaying a photograph of a pest or playing back audio of noise made by the pest. The user interface may display a floor plan of a building along with a location of the pest detector and additional locations of additional pest detectors located in the building superimposed on the floor plan. The user interface may enable selecting a particular building of multiple buildings, a particular floor of multiple floors, or a particular room of multiple rooms. The user interface may display where pests have been detected superimposed on the floor plan.

In some implementations, a pest detector may receive sensor data from a sensor. The pest detector may determine, using a machine learning algorithm, that the sensor data indicates a presence of a first type of pest. The pest detector may send a notification message, including at least a portion of the sensor data, to a computing device and visually indicate that the first type of pest was detected using an indicator. The pest detector may receive an update to the machine learning algorithm from a server and install the update to create an updated machine learning algorithm. The pest detector may receive second sensor data and determine, using the updated machine learning algorithm, that the second sensor data indicates the presence of a second type of pest that is not recognized by the machine learning algorithm.

Various embodiments dynamically learn user-customizable input gestures. A user can transition a radar-based gesture detection system into a gesture-learning mode. In turn, the radar-based gesture detection system emits a radar field configured to detect a gesture new to the radar-based gesture detection system. The radar-based gesture detection system receives incoming radio frequency (RF) signals generated by the outgoing RF signal reflecting off the gesture, and analyzes the incoming RF signals to learn one or more identifying characteristics about the gesture. Upon learning the identifying characteristics, the radar-based gesture detection system reconfigures a corresponding input identification system to detect the gesture when the one or more identifying characteristics are next identified, and transitions out of the gesture-learning mode.

This document describes apparatuses and techniques for radar-enabled sensor fusion. In some aspects, a radar field is provided and reflection signals that correspond to a target in the radar field are received. The reflection signals are transformed to provide radar data, from which a radar feature indicating a physical characteristic of the target is extracted. Based on the radar features, a sensor is activated to provide supplemental sensor data associated with the physical characteristic. The radar feature is then augmented with the supplemental sensor data to enhance the radar feature, such as by increasing an accuracy or resolution of the radar feature. By so doing, performance of sensor-based applications, which rely on the enhanced radar features, can be improved.