SUMMARY

Glossary

Gain-pattern: Directivity and gain of one antenna or array across all angle space, consisting of traits including (but not necessarily limited to) direction in Azimuth (AZ) and Elevation (EL), Beam-width in AZ EL, Side Lobes in AA EL, and efficiency).

Range Doppler-manifold: The range-and-Doppler-measurements space made by one waveform with one sequence-of-processing chain, including a set of unambiguous ranges and unambiguous Doppler velocities, and range resolution(s) and Doppler resolution(s) therein.

Beam: An instantaneous gain-pattern, combined with an instantaneous RD-manifold (waveform and processing).

Field of regard (FOR): The total angular space on which the sensor makes intentional measurements, typically expressed in AZ and EL angles. A single field of regard need not be contiguous, nor monotonic.

Field of View (FOV): The total angular space on which the sensor is capable of making intentional measurements. FOV is also known as the maximum field of regard.

The idea of intelligently tasked sensors is fundamental, and incredibly powerful. Even in sensor-tech spaces where one might suspect that such approaches are already being pursued (for example electronically scanned lidars), there is little indication that such approaches actually are being pursued. Sensors are an integral part of an enormous and diverse number of systems, and span an enormous number of different sensing modalities. Recently, sensors have become high-priority considerations for autonomous vehicles and aircraft, where common sensor modalities include cameras, lidars, ultra-sonics, and radars.

The task for any sensor is to make measurements of aspects of the real-world using one or more sensing resources, and to return data that represents ground-truth (e.g., actual velocity of a sensed object, actual range of a sensed object) as closely as possible. Some examples of the resources that sensors use to make measurements are the number of pixels for an image-capture device such as a camera, the number of beams for a lidar, and the receiving digitizer rate for an ultrasonic sensor.

In the case of autonomous vehicles, the measurement of primary interest is a three-dimensional (3D) representation of the surrounding environment including obstacles/objects (collectively “items”), plus the velocities (e.g., relative to the earth frame of reference or relative to the vehicle) at which the items are moving; the combination of the 3D representation and the item velocities is, effectively, a four-dimensional (4D) data-cube.

In many cases, the resources utilized for sensing are subject to some limits, and one or more of those resource limitations place bounds on the measurement capabilities in one or more bases. For example, the number of pixels in a camera bounds the resolution of which it is capable, and the bit-depth of the pixels bounds the contrast ratio of which it is capable.

The bounds on the measurement can negatively impact a sensor's ability to operate in an environment, and can have severe ramifications on utility. For example, a camera sensor that suffers from insufficient resolution due to a limited number of pixels may be entirely unable to detect objects that are relatively small or relatively far away. Similarly, a camera sensor with limited bit-depth may experience complete blindness when saturated with direct sunlight or glare.

In the case of a radar-based sensor, a critical resource is the radar's time-bandwidth-product (TBP), which is, effectively, equal to the total bandwidth of all of the radar's receiver channels. The TBP limits the speed (or rate) at which a radar can acquire information about the respective ranges and respective velocities of objects, and at which it can determine the objects' respective angular bearings in azimuth and elevation.

Given the goal of designing a radar system capable of making high-resolution measurements of the world in three dimensions plus velocity, a logical approach would be to maximize the TBP for the purpose of minimizing ultimate limitations on the measurement.

Unfortunately, receiver channels with large bandwidth are more difficult and costly to manufacture, and a higher number of channels drives up system cost and complexity, both directly and indirectly, through the required processing and support subsystems. Thus, a radar with a high TBP becomes costly, and has increased size, weight, and power consumption (together referred to by the acronym C-SWAP).

Other resources in a radar sensor are also subject to constraints that may impact the overall system measurement capabilities, such constraints including computational constraints, data-bandwidth constraints, and energy (power) constraints. These resources also may be driven by the total TBP.

The radar designer is thus faced with making a compromise between the desire to make high-performance measurements of the ground-truth and meeting the C-SWAP constraints imposed by the target application/market.

In the commercial application space for autonomous (land, air, and sea) vehicles, this compromise is especially uncomfortable given the simultaneous need for high-performance measurement to support autonomy and low cost, size, weight, and power to enable commercial-market penetration.

Therefore, there is a need for a sensor, a sensor subsystem, and a sensor system, that has enhanced performance while satisfying C-SWAP constraints.

Disclosed herein are embodiments that include a new class of sensor that has improved performance relative to resource constraints by, for example, intelligently morphing the aspect-ratio of its data cube. And such a sensor may also dynamically morph the aspect-ratio of its data cube.

To enhance sensor performance while maintaining C-SWAP constraints, an embodiment of an intelligent sensor includes a capability to dynamically allocate constrained resources across the measurement space of interest in response to a variety of feedback inputs, and can operate according to methods by which those resources are prioritized and distributed.

In another embodiment, where the intelligent sensor is a radar sensor including one or more transmit antennas and one or more receive antennas, the radar sensor can dynamically reconfigure a gain-pattern of each transmit and/or receive antenna. The gain-pattern may be controlled, e.g. with software, to affect parameters such as azimuth pointing angle(s), elevation pointing angle(s), beam width(s) in azimuth and elevation, side-lobe levels in azimuth and elevation, realized gain, and power transmitted. Such gain patterns can be arranged sequentially such that the combination of sequentially arranged gain patterns offers a combined total field of regard. For example, using such a radar sensor, a radar operator or controller system, e.g. implemented with circuitry, thus can configure the radar field of regard in azimuth and elevation to include or exclude particular regions of the field of view. Similarly, using such a radar sensor, a radar operator or controller system can configure the resolution in azimuth and in elevation by control over the beam widths utilized to make up that field of regard.

In another embodiment, a radar sensor includes a capability to generate a transmit signal whose parameters are a function of time (a waveform) and are generated by a signal generator (e.g. that is software controlled), and includes the ability to reconfigure, e.g. via software, the signal parameters such as amplitude, frequency, and phase. The radar sensor also includes a radar receiver and a processing system, e.g. implemented with circuitry, and the ability to reconfigure, e.g. via software, parameters of the processing system. The radar operator can thus configure the sensor's Range-Doppler manifold to include or exclude ranges and velocities of particular interest, and can configure the range and Doppler resolutions over the Range-Doppler manifold.

In another embodiment in which the above two embodiments are combined, a radar sensor includes both the ability to reconfigure, e.g. via software, components of the gain-pattern of the transmit and/or receive antennas, such as directivity, beam-width, and side-lobes, and the ability to reconfigure, e.g. via software, the transmit-signal parameters such as amplitude, frequency, and phase, and the receiver processing. The radar operator or controller system, e.g. implemented with circuitry, thus can configure the system field of regard to include or exclude particular azimuth and elevation fields of regard, ranges, and velocities of interest, thus forming a 4D hypervolume of interest. Furthermore, the radar operator or controller system can configure the azimuth, elevation, range, and velocity resolution(s) within that hypervolume of interest, including configuring distinct resolutions configured over one or more sub-hypervolumes.

Another embodiment of a radar subsystem includes at least one antenna and a control circuit. The at least one antenna is configured to radiate at least one first transmit beam and to form at least one first receive beam. And the control circuit is configured to steer the at least one first transmit beam and the at least one first receive beam over a first field of regard during a first time period, and to steer the at least one first transmit beam and the at least one first receive beam over a second field of regard during a second time period.

Using a radar sensor that includes one or more of the above-described embodiments, a radar operator or controller system can allocate, intelligently, the constrained resources of the radar sensor to meet the needs of different platforms, engagements, and environments, and can perform this allocation dynamically in real-time and in response to changing conditions. That is, such a system can include one or more sensor feedback loops that allow the radar operator or controller system to reconfigure the radar sensor in response to information that the sensors obtain and provide to the system.

The aforementioned techniques illustrated for a radar sensor may be utilized with other sensors, such as, for example, sonic and lidar sensors. A radar system of subsystem can use various sources of data and/or information to direct this intelligence, including data which comes directly from the radar sensor, data which comes from other sensors such as cameras and lidars, and data which comes from preexisting information such as maps.

An intelligent radar system or subsystem can allocate resources more efficiently in order to avoid the wasting of resources by, for example, measuring empty areas/regions such as sky or vacant fields. That is, an intelligent radar system or subsystem can be used to allocate resources more efficiently by not wasting resources on sensing in regions of lower priority, such as off-road regions for an autonomous car.

An intelligent radar system or subsystem can contain a firewall (a “blocked-off” region of the field of regard) and can avoid regions of known or detected interference or degraded measurement, such as regions of strong clutter.

An intelligent radar system or subsystem can prioritize objects or regions in a field of view, to increase measurement resolution (in angular, range, and/or Doppler domains), to increase the update rate on objects, or to increase the measurement range or Doppler extent on certain areas/regions (e.g. tracking long-range radar down a freeway).

Given the latency and communication overhead to/from any sensor, a radar operator or controller circuit may have (at least effectively) concerns associated with requirements for controlling such an intelligent sensor. However, the radar operator or controller circuit may also be able to re-allocate the radar resources very quickly, to respond to emergency situations or to rapidly changing environments. The designer of an intelligent sensor is thus motivated to minimize the communication and control overhead, while simultaneously improving the responsiveness (in terms of response time and situational flexibility). One embodiment that simultaneously accomplishes both of these objectives is the use of internal control that is free-running on the sensor side, but that accepts and responds quickly to prioritized adjustments to that tasking from an external control source.

