Novel Radar Techniques and Applications Volume 2: Waveform Diversity and Cognitive Radar, and Target Tracking and Data Fusion
2: Fraunhofer Institute for Communication, Information Processing and Engineering (FKIE), Wachtberg, Germany
3: Ottawa Research Centre, Defence R&D Canada, Ottawa, ON, Canada
4: DIET University of Rome `La Sapienza', Rome, Italy
5: University College London, London, UK
6: Fraunhofer FKIE, Wachtberg, Germany
Novel Radar Techniques and Applications presents the state-of-the-art in advanced radar, with emphasis on ongoing novel research and development and contributions from an international team of leading radar experts. Each section gives an overview of the latest research and perspectives of the future, and includes a number of chapters dedicated to specific techniques in conjunction with existing operational, experimental or conceptual applications. This volume covers: (1) Waveform diversity and cognitive radar, including holistic design of physical radar emissions; waveform design for spectral coexistence; adaptive OFDM waveform design for spatio-temporal-sparsity exploited STAP radar; applications of noise radar; bioinspired radar techniques; concept of the intelligent radar network; clutter diversity; and cognitive radar management. (2) Target tracking and data fusion, including posterior Cramér-Rao bounds for target tracking; tracking and fusion in log-spherical state space with application to collision avoidance and kinematic ranging; multistatic tracking for passive radar applications; radar-based ground surveillance; multiplatform radar surveillance for aerial and maritime surveillance; person tracking and data fusion for UWB radar applications; and sensor management for radar networks. The companion volume 1 covers real aperture array radar, imaging radar (SAR, ISAR), and passive and multistatic radar.
Inspec keywords: sensor fusion; target tracking; radar tracking
Other keywords: waveform diversity; cognitive radar; data fusion; target tracking
Subjects: Signal processing and detection; General electrical engineering topics; Radar equipment, systems and applications; Sensing devices and transducers
- Book DOI: 10.1049/SBRA512G
- Chapter DOI: 10.1049/SBRA512G
- ISBN: 9781613532263
- e-ISBN: 9781613532287
- Page count: 553
- Format: PDF
-
Front Matter
- + Show details - Hide details
-
p.
(1)
-
Part I: Waveform diversity and cognitive radar
Introduction to waveform diversity and cognitive radar
- + Show details - Hide details
-
p.
1
–21
(21)
This chapter serves as an introduction to Part I of Volume 2 of the book, on the subjects of Waveform Diversity and Cognitive Radar. These are relatively recent concepts, and twenty years ago the terms would hardly have been recognized. Now, though, they can both be regarded as mainstream subjects in radar research, with entire sessions at radar conferences devoted to them.
1 Radar emission spectrum engineering
- + Show details - Hide details
-
p.
23
–59
(37)
The spectral containment of active radar emissions is of growing concern due to continued erosion of allocated radar spectrum and the increasing congestion driven by consumer demand for bandwidth-gluttonous wireless video applications. Strict new emission requirements are forcing the careful consideration of how to achieve radar spectral containment within the context of the ever-present pressure for enhanced sensing performance. It is thus imperative that a holistic perspective be taken that addresses the characteristics of the physical signal launched from the radar, inclusive of electromagnetics, systems engineering and signal processing attributes. This chapter introduces recent developments on the design and implementation of physical radar waveforms for spectral containment, including experimental results for various new emission schemes.
2 Adaptive OFDM waveform design for spatio-temporal-sparsity exploited STAP radar
- + Show details - Hide details
-
p.
61
–85
(25)
In this chapter, we describe a sparsity-based space-time adaptive processing (STAP) algorithm to detect a slowly moving target using an orthogonal frequency division multiplexing (OFDM) radar. The motivation of employing an OFDM signal is that it improves the target-detectability from the interfering signals by increasing the frequency diversity of the system. However, due to the addition of one extra dimension in terms of frequency, the adaptive degrees-of-freedom in an OFDM-STAP also increases. Therefore, to avoid the construction a fully adaptive OFDM-STAP, we develop a sparsity-based STAP algorithm. We observe that the interference spectrum is inherently sparse in the spatio-temporal domain, as the clutter responses occupy only a diagonal ridge on the spatio-temporal plane and the jammer signals interfere only from a few spatial directions. Hence, we exploit that sparsity to develop an efficient STAP technique that utilizes considerably lesser number of secondary data compared to the other existing STAP techniques, and produces nearly optimum STAP performance. In addition to designing the STAP filter, we optimally design the transmit OFDM signals by maximizing the output signal-to-interference-plus-noise ratio (SINR) in order to improve the STAP performance. The computation of output SINR depends on the estimated value of the interference covariance matrix, which we obtain by applying the sparse recovery algorithm. Therefore, we analytically assess the effects of the synthesized OFDM coefficients on the sparse recovery of the interference covariance matrix by computing the coherence measure of the sparse measurement matrix. Our numerical examples demonstrate the achieved STAP-performance due to sparsity-based technique and adaptive waveform design.
3 Cognitive waveform design for spectral coexistence
- + Show details - Hide details
-
p.
87
–118
(32)
Radar signal design in a spectrally dense environment is a very challenging and topical problem due to the increasing demand of both defence surveillance/remote sensing capabilities and civilian wireless services. This chapter describes an optimization theory-based radar waveform design to deal with the spectrum congestion problem. Cognition provided by a radio environmental map paves the way for an intelligent dynamic spectrum allocation. It pushes for dynamic spectral constraints on the radar waveform which is thus the result of a constrained optimization process aimed at improving some radar performance (such as detection, classification and tracking capabilities) while ensuring spectral compatibility with the surrounding radio frequency licensed systems. Finally, some spectrally crowded illustrative scenarios are analyzed to show the effectiveness of the considered optimization theory-based approach.
4 Noise Radar Technology
- + Show details - Hide details
-
p.
119
–155
(37)
The well-known classical pulse radar has several disadvantages. The high transmitted peak power can be easily detected and warn an enemy, and the ambiguities in both range and Doppler measurements lead to problems with unambiguous localization and tracking. For a long time researchers have tended to overcome these problems and find waveforms that will free the radar from the aforementioned issues. The design of the frequency modulated continuous wave radar with linear frequency modulation was an important step; the mean power is equal to the peak power and it is much harder to detect such radar, but range and Doppler ambiguities remain due to periodicity in the waveform repetition. The next step was the introduction of the noise radar concept. At first glance it is hard to believe that a noise signal, without any clear internal structure and well-defined instantaneous frequency, can be used for radar purposes. But thanks to the development of digital correlators which are able to compute the ambiguity function in real time, it is now possible to unambiguously estimate the range and radial velocity of the target using noise illumination. But of course one must pay a price: noise radar is limited not only by ambiguities in range and Doppler, but also in dynamic range. The strong return signal from nearby targets, or clutter, can entirely mask a weak and distant target's echoes. The second drawback is that signal processing is much more complex than in classical radars and thus the radar signal processing unit for a noise radar must have much higher computational power, which is achievable only by using modern computers equipped with graphical processing (GPU) units. The potential applications for noise radar can be vast; it is possible to use it for surveillance, traffic monitoring, and early warning and imaging (SAR, ISAR) purposes as the time on target is usually very long (hundreds of milliseconds to seconds).
5 Cognitive radar management
- + Show details - Hide details
-
p.
157
–193
(37)
Cognitive radar is a radar system that acquires knowledge and understanding of its operating environment through online estimation, reasoning and learning or from databases comprising context information. Cognitive radar then exploits this acquired knowledge and understanding to enhance information extraction, data processing and radar management. In order to make progress to this goal, the topic of cognitive radar attempts to shift the cognitive processes previously performed by an operator into automated processes in the radar system. Families of cognitive processes are well defined in cognitive psychology [1], such as the perceptual processes, memory processes, languages processes and thinking processes. In this chapter, we discuss radar management techniques that enable the manifestation of one or more cognitive processes, with a particular view towards electronically steered phased array and multifunction radar systems. In particular, this chapter focuses on two cognitive processes: attention and anticipation. Attention can be manifested by effective resources management, whereby a quality of service-based task management layer connects radar control parameters to mission objectives. Anticipation can be generated using stochastic control that is non-myopic, allowing the radar system to act with a consideration of how the radar system, scenario and environment will evolve in the future.
6 Clutter diversity
- + Show details - Hide details
-
p.
195
–214
(20)
Measurements of the properties of bistatic radar clutter have shown that amplitude statistics of bistatic clutter depend on the bistatic geometry, such that the distribution of the bistatic clutter may be shorter-tailed (less `spiky') than the equivalent monostatic clutter. At the same time, the bistatic signature of targets may be significantly different from their monostatic signatures. Clutter Diversity may be defined as: `understanding and quantifying these effects, and finding out how best to exploit them', and offers a new degree of freedom in the design of radar systems. This chapter reviews the properties of clutter and of targets as a function of bistatic geometry, and explores the effects of detection performance.
7 Biologically inspired processing of target echoes
- + Show details - Hide details
-
p.
215
–232
(18)
Echolocating bats have evolved an impressive ability to detect and discriminate targets in highly challenging environments. It is believed that over 50 million years of evolution have contributed to optimize their echolocation system so that highlevel performance could be achieved within their operating environment. The way bats interrogate the surroundings present differences, as well as similarities, with respect to typical signal processing techniques used in synthetic sensors. In identifying and investigating these differences, useful lessons can be made available to engineers that can potentially be used to improve radar and sonar systems. In this chapter, we review some of the strategies that bats are believed to employ to detect and classify moving and static targets and present a comparison with the radar and sonar counterparts. We introduce a baseband receiver based on an existing model of the bat auditory system and apply it to baseband synthetic ultrasound signals to investigate target detection and resolution performance.
8 The concept of the intelligent radar network
- + Show details - Hide details
-
p.
233
–252
(20)
Future radar systems are likely to be distributed, intelligent, multistatic and spectrally efficient, taking into account many of the concepts developed in this section of the book, and offering greater flexibility, greater robustness and lower cost than conventional single-platform monostatic approaches. This chapter describes the `intelligent radar network' and some of the developments that will be necessary for its realization. In particular, the resource management of a radar network, the means of communication between the nodes of the network, and geolocation and synchronization between the nodes of the network all represent significant challenges.
-
Part II: Target tracking and data fusion
Introductory remarks on tracking-and fusion-driven radar systems technology
- + Show details - Hide details
-
p.
253
–261
(9)
Future radar and radiofrequency systems will provide not only kinematic measurements and classification spectra at high update rates with much improved qualities in terms of accuracy, reliability, resolution, robustness, etc., but will also offer new types of classification attributes, mission-relevant environmental information and complimentary information provided by other sensors via sensor data fusion. Moreover, these rich informational sources for producing real-time situation pictures will be collected by an optimized use of all available sensing, communications and platform-related resources.
9 Posterior Cramér-Rao bounds for target tracking
- + Show details - Hide details
-
p.
263
–302
(40)
In this chapter, we present a review of recent developments in the calculation of estimation error performance bounds for target tracking. We concentrate on the posterior Cramér-Rao bound (PCRB), which is computationally the simplest of a general class of lower bounds. We present full details of an efficient recursive formula for the PCRB for the general non-linear filtering problem, and of PCRB methodologies in cluttered environments (i.e. in which there can be missed detections and spurious false measurements). In such environments, the measurement origin uncertainty is shown to manifest itself as an information reduction factor that degrades tracking performance according to the severity of the clutter. A tutorial of the key PCRB methodologies in cluttered environments is provided, and via simulations, PCRBs are calculated for a scenario in which a single target is tracked using measurements generated by a stationary radar. The PCRBs are compared to the performance of an extended Kalman filter, and the results demonstrate the efficacy of the PCRB as an efficient theoretical predictor of the capability of the tracker. We also present a discussion of applications that would benefit greatly from the development of a PCRB methodology. These applications include sensor scheduling in passive coherent location networks; and performance assessment of algorithms designed for image fusion, data assimilation for meteorology/oceanography, simultaneous localization and mapping and quantum estimation.
10 Tracking and fusion in log-spherical state space with application to collision avoidance and kinematic ranging
- + Show details - Hide details
-
p.
303
–333
(31)
This chapter is devoted to a special state representation for target tracking. The considered coordinates possess, in comparison with classical Cartesian ones, distinct advantages in particular in applications where angles are the only measurements available like, e.g. for jammed radar. In those applications, measurements are not Cartesian-complete and the range of a moving object under track is not observable unless the sensor platform performs manoeuvres. This chapter presents basic relations and properties of log-spherical coordinates. In particular, it is shown how those coordinates decouple the remaining coordinates from the unobservable range in angular-only tracking. The chapter discusses recursive filter algorithms and corresponding performance bounds. As an application example, it uses data fusion in a collision avoidance system based on a suite of sensors. The final topic is the so-called kinematic ranging, i.e. the extraction of range information from angular-only measurements by suitably chosen manoeuvres of the sensor platform. Presentation in this chapter covers both mathematical derivations as well as numerical simulation results.
11 Multistatic tracking for passive radar applications
- + Show details - Hide details
-
p.
335
–372
(38)
Bistatic and passive radar systems enjoy various advantages, which have been discussed in detail in Part III of Volume 1 of this book. Multistatic configurations, where multiple transmitters (Txs) and/or multiple receivers (Rxs) are located separately, are of particular importance in this context. They provide detection of a target from different aspect angles. Furthermore, the fusion of the measurements from different Tx/Rx pairs can be used to overcome the low quality of a single measurement. Associating measurements of the same target from different Tx/Rx combinations (i.e. multi-sensor association), as well as improving the target state estimate over time belongs to the tasks of target tracking. In this chapter, the tracking task is discussed for different passive radar systems. Solutions based on multiple hypothesis-tracking techniques are proposed and tested.
12 Radar-based ground surveillance
- + Show details - Hide details
-
p.
373
–403
(31)
Radar-based ground surveillance provided by airborne platforms or based on distributed land-based installations is an essential ingredient for modern activity-based intelligence. Therefore, ground moving target indication radar detects objects within wide areas on land and sea and reports them through extracted radar plots. Often these radar plots end up in a big data problem due to the high number of involved objects and the long duration of typical surveillance missions. Also, the object trajectories reported by radar plots may be interrupted due to terrain masking and radar blindness within the Doppler notch. Multi-object tracking techniques create continuous object trajectories by considering the radar plots collected by multiple platforms together with topography and infrastructure. Higher level aggregation methods, like convoy detection, group tracking and traffic flow estimation, are additional methods of data analytics. Applied on the radar-based ground picture they contribute to the overall situation assessment and are suitable for focussing the attention of the users. Besides these detection and tracking aspects, object classification and identification is necessary to complete the situational ground picture. Radar contributes e.g. with synthetic aperture radar or high-range resolution. Finally, data fusion is used to combine the radar picture with additional data coming from other sensors or transponder systems.
13 Radar multi-platform system for air surveillance
- + Show details - Hide details
-
p.
405
–427
(23)
The chapter is about air surveillance system composed of several platforms equipped with primary and secondary radars. Currently, air situational awareness is obtained by data fusion of platform tracks exchanged via normalized Tactical Data Links. The performances of air surveillance can be significantly improved, taking into account the game changing due to telecommunication progress: High Data Rate network is now available on almost all the multiplatform systems, even those composed of mobile platforms as ships and aircrafts. Via HDR network, platforms can share plots (raw detections) of all their radars and a common improved air picture can be elaborated by plot data fusion on each platform. The first section presents the objectives of multiplatform air surveillance system in civil and military domains. The second section describes the theoretical multi radar performance gains under the hypothesis that the HDR network is perfect (no loss, no delay) and that the radar plots of all the platforms are exchanged. The third section describes the evolution of architectures for civil and military multiplatform systems over a 40 years period. For the upgraded multiplatform architecture, the fourth section presents its external interface and its main functions. The fifth section provides some examples of western multiplatform systems and gives some results of performance gains obtained by a multiplatform system in development. Finally, multiplatform systems' future challenges are discussed in the sixth section.
14 People tracking and data fusion for UWB radar applications
- + Show details - Hide details
-
p.
429
–455
(27)
Localization of people that do not carry active tags is needed in security as well as in rescue applications. Ultra-wideband (UWB) technology is promising due to its high ranging resolution capability, robustness against multipath interference and obstacle penetration among others. In this chapter, an approach for detection, localization and tracking of people using either a single UWB sensor or a distributed network of UWB sensor nodes is described. The background behind UWB sensing, a description of UWB sensor nodes and a concept for a distributed sensor network is presented. The basic principle of person detection based on the changes the person induces on the channel impulse response is explained. Two approaches for localization based on range-only observations that can be applied in singlesensor or multiple-sensor scenarios or in the presence of a single person or multiple people are presented. The concept and each step of the approach are illustrated using data obtained in a measurement campaign at TU Ilmenau from an office environment.
15 Sensor management for radar networks
- + Show details - Hide details
-
p.
457
–488
(32)
Advancements in communication and information-processing technologies are driving an interest in networked radar systems, which are capable of compensating for the weaker attributes of the individual radars in the network. The role of a network of radar systems is to perform joint assessment of a surveillance region by fusing data generated at the individual radar nodes. To achieve best possible performance for the network, it is necessary to optimally configure the network and allocate its finite resources, such as radar time/energy budget, bandwidth, communication capacity and processing capacity. This chapter describes sensor and resources management techniques that can be applied to radar networks, focussing on radar network measurement scheduling and networked radar quality of service-based resources management.
-
Back Matter
- + Show details - Hide details
-
p.
(1)