Cognitive Radar Signal Processing

Cognitive radar is a new paradigm to conceive the next radar generation characterized by unique and amazing features inspired to mental abilities and processes related to knowledge. Introduced by Haykin [1] and Guerci [2], it is attracting huge attention within the radar community during the last few years. The key concept is that radar system performance can be enhanced through a continuous and coordinated feedback between the transmitter and receiver which implies a dynamic adaptation of the sensor’s algorithms to the operational context and environmental replies. This paper discusses the biological inspiring principles of cognitive radar and analyzes the resulting conceptual architecture. Then, three challenging radar signal processing applications, which can significantly benefit from cognition, are illustrated highlighting the potential performance benefits achievable with the awesome pro-active paradigm. STO-EN-SET-216 6 1 1. Cognitive Radar Paradigm Cognitive dynamic systems have been inspired by the unique neural computational capability of brain and the viewpoint that cognition (in particular the human one) is a form of computation. Some exemplifications within this new class, which is undoubtly among the hallmarks of the 21st century, are cognitive radar, control, radio, and some other engineering dynamic architectures. S. Haykin published two pioneering articles in the context of the cognitive radar [3], [4]. The key idea behind this new paradigm is to mimic the human brain as well as that of other mammals with echolocation capabilities (bats, dolphins, whales, etc). They continuously learn and react to the stimulations from the surrounding environment according to four basic processes: perception-action cycle, memory, attention, and intelligence. This last observation highlights the importance of specifying which are the “equivalents” of the aforementioned activities in a cognitive radar. This is thoroughly discussed in [2], [5], and [6]. The perception-action process, schematized as in Figure 1, has the fundamental task of sounding the environment. Radar transmitter through the waveform emission stimulates the background with the goal to obtain a response (i.e. a radar echo) from it. The mentioned response is perceived by the radar receiver which plays the equivalent role of the human senses. (a) General. (b) Radar. Figure 1: Schematic representation of the perception-action process. Attention requires processing the perceptor output to extract information and to selectively concentrate on some discrete aspect of information. It can require system actions for prioritizing the allocation of available resources in accordance with their importance (for instance a detection in a give range-azimuthDoppler bin usually involves a confirmation process which calls for a specific radar waveform optimized to the actual interference/clutter conditions and the Doppler bin under test [7], [8]). As to intelligence, among the aforementioned four functions, it is by far the most difficult to describe. While intelligence functionalities are based on the perception-action cycle, memory, and attention, it is the presence of a feedback at multiple levels that makes possible for the system intelligent decisions in face of inevitable uncertainties in the environment. As matter of fact, the presence of such a closed-loop feedback between the actuator (transmitter) and the perceptor (receiver) represents the main ingredient which makes unique the cognitive radar and clearly distinguishes it from the classic adaptive architecture. In this last case adaptivity is mainly confined at the receiver branch except for some static forms of transmit diversity [2] usually implemented in terms of mode selection (i.e. long-range versus short-range, search versus Cognitive Radar Signal Processing 6 2 STO-EN-SET-216 tracking). The information sharing involved in the feedback process is complemented with the use of a memory, which in the radar case is constituted by a dynamic database. It contains knowledge sources about the operating context such as: • geographic features of the illuminated area [9]: type of terrain, presence of clutter discretes, terrain elevation profiles (for instance gathered through Geographic Information Systems (GISs) or Digital Terrain Elevation Models (DTEMs)); • electromagnetic characteristics of the overlaid radiators: operating frequency, modulation and policy, activity profiles, location of transmitters (obtained through Radio Environment Maps (REMs) [10] which localize surrounding emissions in time, frequency, and space; and/or through spectrum sensing modules which continuously sound the environment and acquire fresh information on the external electromagnetic interference possibly used to update the content of the REMs); • data from other sensors [11] (Synthetic Aperture Radars (SARs), infrared devices, meteorological measurements, etc.). The overall information flow coordinates and triggers actions of the system. For example, with reference to the search process, it is exploited to devise the new transmit waveform [10], [12], to select the training data for receiver adaptation [9], [11], to censor data containing clutter discretes, to choose the most suitable detector within a battery available at the receiver. This implies a continuous adaptation of the pair perceptor-actuator ruled by the available information flow and possibly coordinated by a system manager. It is worth noting that the quoted diversity in the transmit-receive chain is actually already present in nature making the cognitive radar a bio-inspired concept. Many mammals with echolocation capabilities, in particular the bats [7, Chap. 6], in their natural behavioral phases, change the waveform in a spontaneous and systematic way, producing through tongue clicking a variety of modulated sonar signals. A nice example is the Eptesicus Nilssonii bat [7, Chap. 6]. While attempting to feed on prey, it changes the Pulse Repetition Time (PRT) and the waveform shape between the approach phase and the terminal phase. In fact, studying the wideband ambiguity function during the search phase, the bat is capable of resolving both in range and Doppler, then during the terminal phase, it improves the range resolution but the signal becomes quite Doppler tolerant. Interestingly, many preys also developed cognitive actions in terms of evasive behaviors [13] to counteract the bats’ sonar and to confuse it with false multiple echoes (a technique which clearly resembles Electronic CounterMeasures (ECMs) to deceive a radar). Summarizing, the block scheme of a cognitive radar is displayed in Figure 2, which, as expected, highlights the much higher hardware complexity with respect to the classic adaptive radar architecture of Figure 3. This poses many technological challenges connected with the implementation of the new cognitive radar paradigm. Some of them can be afforded with the advent of Redundant Array of Independent Disks (RAIDs), phased array with several transmit-receive modules, Multiple-Input Multiple-Output (MIMO) hardware capabilities, multi-polarization equipments, and Application Specific Integrated Circuit (ASIC) logics, etc. Algorithmic challenges are also present in order to exploit as efficiently as possible both the feedback information and the a-priori knowledge. A multitude of algorithms should be possibly run contemporaneously, pushing for the use of parallel computing architectures and programming. A nice and simple example of a cognitive radar tracker is shown in [14]; this is probably among the first formulations of a fore-active radar, namely a first step towards radar cognition. The measurement noise depends on the action of the transmitter. That action is controlled by a transmit waveform parameter (i.e. pulse duration and chirp rate). The optimal signal selection to sound the scene is established through a feedback between the receiver and the transmitter. By doing so, a closed-loop around the environment Cognitive Radar Signal Processing STO-EN-SET-216 6 3 Figure 2: Block scheme of a cognitive radar. Figure 3: Classic adaptive radar architecture. is formed, whereby it becomes possible for the transmitter to exercise indirect control on the receiver via the environment. In the remaining part of this paper a selected list of references is first provided and then three challenging signal processing applications involving cognitive radar are presented. They can lead to a significant potential performance improvement over a classic radar system thanks to the presence of pro-activity and the interaction with a dynamic environmental database. 2. Selected Reference List on Cognitive Radar In the following a selected reference list on cognitive radar is provided and discussed. In [15] a detailed study of a general radar system that mimics the way the visual brain observes its surrounding environment is addressed. Specifically, a cognitive radar jointly exploiting a perception-action cycle paradigm and some stored information is described. The performance gains pursued are assessed for tracking applications, Cognitive Radar Signal Processing 6 4 STO-EN-SET-216 using the posterior Cramer-Rao lower bound (PCRLB) as figure of merit to dynamically adapt the transmit waveform. In [16], following the architecture in [15], a cognitive radar tracker based on the Maximum a Posteriori Penalty Function (MAP-PF) tracking methodology is described. A cognitive Pulse Repetition Frequency (PRF) adaptation is employed to enable interesting performance enhancement. In [17], resorting to a Partially Observable Markov Decision Process (POMDP) model, a new cognitive target tracker is devised. The proposed algorithm properly chooses the radar measurement times to ensure robust performance while a target becomes temporarily unobservable. By doing so a lower probability of track loss than some counterparts is achieved. Reference [18] applies flow field theory to echolocation sensors. More precisely, it highlights that echoic flow provides key information for vehicular radar systems to create an accurate and instantaneous perception allowing automatic adjustments to be made in order to maintain safe separation. Practical implementation issues on cognitive radar are addressed in [19]. Therein the authors demonstrate that cognitive capabilities can be readily added to modern digital radars equipped with flexible signal/data processors and arbitrary waveform synthetizers. Building upon this framework, in [20], the authors focus on a cognitive perception/action cycle for a notional fighter radar with an adaptive Active Electronic Scanning Antenna (AESA). In [21], a cognitive radar detector is conceived exploiting previously acquired information to estimate the statistical characteristics of the operating environment. Precisely, machine learning approaches are adopted to adaptively determining the optimal detection threshold within the low sample support regime. The conducted performance analysis highlights the effectiveness of the devised algorithm to accurately estimate the threshold. Another issue of interest which can be potentially benefit from cognition regards the coexistence of radar and communication systems. In this context, some works have been published, such as [22], where the authors introduce an approach to design shared spectrum access operations for the joint coexistence of radar and communication networks. Furthermore, in [23], an alternative approach is considered which also improves the spectral efficiency. Last but not least, cognitive radar networks are attracting some attention in the field of environmental surveillance in the presence of non-cooperative targets [24]. The review of features, benefits, and challenges resulting in the use of such networks is also described in [25].