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Today’s radars face an ever increasingly complex operational environment, intensified by the numerous types of mission/modes, number and type of targets, non-homogenous clutter and active interferers in the scene. The EM cluttered environment is a growing problem for ground- based and airborne radar systems.
This problem is becoming critical as the available frequency spectrum shrinks due to growing wireless communication device usage and spectrum management regulations. The commercial use of spectrum is scaling exponentially as cellular providers begin to roll out 5G, automotive manufacturers push V2X communication, and the Internet of Things drives wireless connectivity into a myriad of devices. Future systems require the ability to
anticipate the behavior of emitters in the operational environment and to adapt their transmissions in a cognitive fashion based upon the spectrum availability
“This problem is further exacerbated by the growing number of targets that the radar must detect. More capable radar systems are needed that can adapt to multiple targets while utilizing unoccupied frequency bands. Finally, radar, communication, and other electronic systems must be capable of operating without interfering with each other,” says ARL report by Anthony Martone and others.
Constantly changing environments represent an enormous challenge for modern driver assistance systems. To meet these challenges, software controlled automotive radars offer entirely new opportunities. They are compact, low cost, and also extremely versatile and highly reconfigurable.
Finally EW threats to radars have also become more complex and deadly due to electronic battlefield and development of advanced electronic warfare systems. Next-generation electronic warfare (EW) technology is being developed that will quickly detect, locate, and identify emitters of radio frequency signals over all threat bands and from all directions.
Thus, the ability to adapt ones transmit waveform, to optimally suit the needs for a particular radar tasking and environment, becomes mandatory. Cognitive and adaptive radars shall have capability to identify possible radio and sensor jamming threats and then transmit without affecting friendly signals. They shall be capable of sensing the environment and adapting transmissions and signal processing to maximize performance and mitigate interference effects in an increasingly cluttered EM environment. They shall also adapt to multiple targets of interest; and other radar, communication, and electronic systems that must operate without interfering with each other
The recent advances in hardware capability to generate arbitrary (phase and amplitude) design waveforms, high computation resources like FPGAs, Giga samples per second A/D and D/A convertors and machine learning algorithms are other drivers of cognitive and adaptive radars.
Cognitive and Adaptive Radars
A possible solution to this problem are Cognitive radars, the systems based on a perception-action cycle that sense the environment and learn from it important information on the target and its background, then adapt the transmitted waveform to optimally satisfy the needs of their mission according to a desired goal.
Cognitive Radars are capable of interacting intelligently with its environment by adapting both its transmit and receive functions based on contextual awareness and expert reasoning so as to maximizes their output SINR, SCR for Optimal target identification.
Cognitive radar is a radar system which selects its transmitted waveform to adapt to the radar environment by using feedback structure from the receiver to the transmitter. Cognitive radar system is capable of optimizing performance using (1) intelligent signal processing that learns from the environment; (2) receiver-to-transmitter feedback; and (3) preservation of information (i.e., memory).
Adaptive radar systems for ADAS
Fraunhofer FHR presented an adaptive radar at the recent European Microwave in Madrid. The demonstrator measures changing distances while optimising the use of resources. By applying cognitive methods, they can be used to develop radars that intelligently and automatically adapt their parameters to the individual situations during operation.
Driver assistance systems have to ensure reliable operation in a range of different traffic conditions. In city traffic, for instance, they have to detect a large number of different targets in the presence of a very heterogeneous background. On the highway, on the other hand, they have to recognise targets at high speeds and in large distances. Automotive radars have to be able to adapt to these changing conditions in order to accurately determine short and long distances, relative speeds, and target positions in each situation while recognising several types of targets in diverse environments.
To achieve this, cognitive radars intelligently adapt their operational parameters such as the channel selection, the bandwidth, and the carrier frequency as well as the duration and the number of measurements to the different situations and tasks. One major challenge associated with the spatial resolution is the channel selection for the position estimation. Here, the accuracy depends on the length of the antenna array. Accuracy increases with the number of antenna elements, i.e. with the length of the array. This, however, requires more transmit and receive channels with an adequate spacing, which leads to higher costs and a large volume of data that has to be processed in real time.
Fraunhofer FHR has developed a MIMO radar that adaptively detects the radar scene and uses complex algorithms to accurately predict the radar target’s new position based on previous measurements. With these one step ahead predictions, the controller in the system adaptively selects only the four to six receiver and transmitter channels necessary for the next measurement from the MIMO array’s 32 virtual channels. Thus, the position can be accurately determined, even with a relatively small and cheaper system and a lower real time data volume. The results of each new measurement flow into the calculations for the next measurement according to the closed loop principle. This is how the radar system learns to continuously improve its adaptive strategy depending on the individual situations and to create an optimised image of the radar scene using less hardware and computational resources.
Cognitive passive radars
A Passive Radar (PR) system is a bistatic radar that makes use of emissions from a non co-operative transmitter of opportunity, such as broadcast, communications, or radio-navigation transmitters rather than a dedicated, co-operative radar transmitter. Such systems have a number of potential advantages over conventional active
systems. The receiver is passive and so potentially undetectable. Many illumination sources can be used, and many of them are high power and favorably sited.
PR receiver systems can often be rather simple and low cost, and there is no need for any license for the transmitter. Moreover, in recent years, multistatic PR systems have become very attractive for harbor protection and coastal
surveillance, offering a number of advantages in terms of eco-compatibility and sustainability. In fact, they can be installed even in protected and populated areas, reasonably without providing additional electromagnetic (EM) pollution. In this sense, the use of PRs for low/medium range applications can be viewed as a strategy for a smart
use of the spectrum resources .
Passive radars are quite often grouped in networks to extend coverage and improve detection, tracking, and identification of targets entering the region under surveillance. This can be done easily even with a single receive node exploiting the different sources of illumination available in the surveillance area and the spatial diversity provided by different channels of observation. Clearly, passive radars cannot change in a cognitive way their transmitted waveform, because they do not transmit but fully rely on the sources of opportunity available in the
surveillance area. However, if the receive node is able to handle multiple signals (FM, UMTS, DVB-T etc.) , it can decide in a cognitive manner which channel or set of channels to use for detecting, tracking, and classifying the targets, based on the acquired information on targets themselves and knowledge of source characteristics
and transmitter-target-receiver geometry, in the same way as an active radar chooses the transmit waveform on-the-fly.
CERDEC’s Adaptable, secure radar technology
The US Army Materiel Command’s Communications-Electronics Research, Development and Engineering Center (CERDEC) is working on developing adaptable, secure radar technology. It has developed the advanced pulse compression noise (APCN), the new noise-encrypted radar waveform that can be programmed in real-time to optimise radar performance.
CERDEC I2WD Radar Division research scientist Dr Mark Govoni said: “Encrypting our radar waveforms limits the likelihood for adversaries to intercept and exploit our emissions. Programming the waveform in real-time takes this capability even further, and ensures operational effectiveness.” The new technology aims to preserve radar system performance during attacks and in high-traffic radio frequency environments.
CERDEC Intelligence and Information Warfare Directorate director Dr Paul Zablocky said: “The battlespace is continually evolving, and with that, comes the need to change the way we think about radar design. “Techniques such as real-time re-programmable waveform synthesis and low probability of intercept / low probability of detection (LPI/LPD) provide added capability that will address the emerging electromagnetic spectrum challenges our soldiers are likely to face in the future.”
Cognitive EW to Counter Cognitive Radars
The future adaptive and cognitive radars will likely present an even greater challenge as they will be capable of sensing the environment and adapting transmissions and signal processing to maximize performance and mitigate interference effects.
DARPA has launched Adaptive Radar Countermeasures (ARC) program is to enable U.S. airborne EW systems to automatically generate effective countermeasures against new, unknown and adaptive radars in real-time in the field. ARC technology will: Isolate unknown radar signals in the presence of other hostile, friendly and neutral signals, deduce the threat posed by that radar, synthesize and transmit countermeasure signals to achieve a desired effect on the threat radar and assess the effectiveness of countermeasures based on over-the-air observable threat behaviors.
Conclusion
The emerging ability for radar and EW systems to adapt rapidly to their environments is creating an electronic cat-and-mouse game that plays out at ever-increasing speed, as radar systems seek to adapt more quickly than the EW systems that oppose them, and likewise as EW systems seek to jam ever more quickly adaptable radar, says John Keller
