A series of advanced electronically scanned phased array (AESA) mmWave radars are designed with a multi-mode multi-mission software-defined radar (SDR) capability. These research radars address a variety of markets including advanced driver assistance systems (ADAS), fixed or mobile ground deployed small unmanned aerial vehicles (sUAVs) drone detection and tracking systems, sUAV air-to-air and air-to-ground radars and sUAV deployed airborne synthetic aperture radar (SAR). The radars are designed to facilitate radar research and development from early stage concept-of-operations through requirements definition and validation to system design, verification and deployment.

The radars are designed and manufactured by aiRadar Inc., using highly integrated, state-of-the-art RFICs from Sivers Semiconductors AB for the transmit/receive modules (TRMs). All are multi-mode multi-mission. They can switch seamlessly between a sector scanner covering 90 degrees in azimuth with better than 0.5 degrees angular resolution with triple baseline interferometric positioning in elevation, to a sUAV deployed single pass interferometric SAR (InSAR) with range and azimuth resolution better than 5 cm generating digital surface models (DSMs) with up to 16 channels of along track interferometry for high-resolution unambiguous moving target indication (MTI).

The InSAR configuration provides a multi-aperture SAR capability with displaced phase center antenna (DPCA) micronavigation. The radars range in size from the smallest model, with a mass of 3,850 grams comprising three transmit (Tx) and three receive (Rx) 64-element arrays, to the largest model, with a mass of less than 10 kg comprising 1,536 active elements in an identical array layout but with 256-element arrays.

The target customers are commercial, military and academic researchers who seek to advance the state-of-the-art in radar using a ruggedized reconfigurable instrument rated to IP-67 and Mil-Std-810. Deploying these research radars with a simple but powerful compiled radar programming language (aiRPL), which executes on a field-programmable gate array based multi-mode radar processing unit (aiRPU), eliminates the risks associated with developing AESA radar systems based on analysis and simulation, either computer simulation or over-the-air validation using radar target simulators.

An ADAS developer might wish to perform an operational real-world comparison of various AESA configurations with different array sizes and with virtual or real array elements. These research radars permit, for example, the direct co-located and co-temporal comparison of a two Tx and four Rx MIMO array to a 12 Tx and 16 Rx MIMO array. Similarly, the 12 Tx and 16 Rx MIMO array (with 192 virtual channels) may be compared to a 256 Tx and 512 Rx array (with 512 real channels arranged as a long or medium baseline elevation interferometer). A simple script in aiRPL manages the complexity, enabling these three (or more radar configurations) to be cycled on a PRI-by-PRI basis, providing an objective comparison of radar performance under the same operating conditions.

Once the requirements and AESA configurations are validated for a specific application, the commercial, military or academic radar developer may proceed, based on the risk assessment, the economics or the urgency of time-to-market, with an in-house radar design or an aiRadar customized application specific radar. This can be done with or without the licensing of the aiRadar programming language compiler and the radar processing unit IP Core.

The aiRPU IP Core provides real-time bidirectional interfaces, up to 48 Gbps, to the lowest level in-phase and quadrature raw radar data channels, and to the aiRPU IP Core. This interface is provided for researchers and developers of cognitive adaptive (CA) radar allowing an external artificial intelligence (AI) processor, perhaps based on GPU arrays, to modify any or all of the radar configurations from transmit pulses to beamforming/steering directions on a PRI-to-PRI bases.

An example application is adaptive pulse code modulation (PCM) for ADAS in the inevitably congested radar environment that will exist as more radars are deployed in ever more advanced systems. The CA loop facilitates the analysis of received signals to determine if an interfering source (another vehicle) is present and select the PCM codes to reject that interference. This CA loop has applications in low probability of intercept (LPI) radars for military applications as well. A key feature of the CA physical and API interfaces is that the algorithms in the CA loop remain the exclusive intellectual property of their developers.

To facilitate the granting of experimental and research licenses, the first offering of these research radars has a center frequency at 66 GHz, where there is little commercial activity at this stage. The research radars are architected in such a manner that the digital control and RF interfaces to the TRMs enable hardware reconfiguration to 24 GHz with existing Sivers Semiconductors technology or reconfiguration to 76 to 81 GHz with future Sivers Semiconductors technology. The generalized TRM interfaces anticipate the emergence of new allocated mmWave frequency bands, should they arise.


Given the ability of radar sensors to operate in conditions such as rain, fog and snow, which impairs or disables the operation of LiDAR sensors and visual cameras, it is inevitable that radar will become a fundamental element of ADAS.

Most radars currently deployed in automotive applications have very coarse resolution. While lower resolution radars may detect an object, a motorcycle, person or a truck, the object is represented by little more than a “blob.” The task of object recognition is largely offloaded to an AI/machine earning (ML) algorithm, where advances in AI hardware and software algorithms are tasked with providing that one crucial step closer to a fully autonomous, safe vehicle.

There may be several reasons for this allocation of functionality and performance, but one likely contender may be that the requirements definition and validation, followed by the design and manufacture of advanced modern radar with complex AESA antennas is difficult. This difficulty translates into technical, performance, schedule and cost risk. Availability of low-cost and low risk advanced AESAs may enable changes to this allocation and, perhaps, advances in autonomy levels.

In addition to the design and manufacture of complex advanced AESA radars, verification and ongoing product assurance is not trivial and requires well-defined metrics. A simple requirement such as integrated sidelobe ratio (ISLR) impacts the angular resolution of two targets and the angular measurement accuracy of a single target, as well as having a significant impact on image quality. This lack of resolution and image quality may have a very negative impact on the AI/ML interpretation of the scene.


A growing number of radar applications have emerged recently where current radars perform poorly or are not suitable. These applications include ground deployed (and human portable) real aperture radar for detecting and monitoring small drones which pose security and military threats as well as small UAV-deployed high-resolution imaging with SAR and/or real aperture radar (RAR).

A good example is InSAR deployed at the site of a flooding disaster where the desired product is high-resolution scenes superimposed on DSMs, captured in real time as riverbanks and slopes subside, with the identification of objects of interest with overlays of velocity vectors (MTI) attached to those objects.

Military applications are highlighted by the ongoing conflict in the Ukraine. Hostile drones are extremely dangerous: locating forces, providing intel on those forces, directing artillery fire with devastating accuracy and assessing damage. While the open literature shows many sUAV detection systems, these do not appear to have been effectively deployed as evidenced by multiple videos from loitering drones eliciting no evasive responses when artillery is spotted onto a target or when improvised weapons, such as modified rocket propelled grenades are dropped vertically onto targets from sUAVs.

SAR offers extremely high-resolution but requires motion, while RAR provides excellent image quality from a stationary position. A sUAV deployed SAR with 3D InSAR DSMs might be the preferred instrument for pre and post operation high-resolution threat and damage assessment, while an sUAV deployed RAR with an AESA may be better suited for real-time target spotting.


Addressing multiple applications, multiple modes and multiple missions with a SDR on a single hardware platform with a common interface as an economic and affordable solution is challenging. An early decision to implement a hybrid beamforming architecture, with analog beamforming at the level of 16 antenna elements and digital beamforming at a higher level, reduced the number of ADCs and data rates.

An investigation of available multi-channel mmWave technologies suitable for this hybrid architecture led to Sivers Semiconductors AB. Sivers Semiconductors develops, among other things, MMICs, modules and subsystems based on advanced semiconductor technology for WiGig mmWave networks.