Cognitive Radar Research Articles

Wavelet-Based Waveform for Effective Sidelobe Suppression in Radar Signal

A new radar waveform based on wavelets is proposed. The waveform shows significant advantages over conventional linear frequency modulated (LFM) and Costas waveforms for effective sidelobe suppression. Another benefit comes from the wavelet packets, each of which occupies a subband in the frequency domain. Subband adaptation of the waveform in both magnitude and phase becomes flexible, responding to varying target and environmental conditions. The latter facilitates the development of powerful cognitive radars.

Ambiguity Function Shaping for Cognitive Radar Via Complex Quartic Optimization

In this paper, we propose a cognitive approach to design phase-only modulated waveforms sharing a desired range-Doppler response. The idea is to minimize the average value of the ambiguity function of the transmitted signal over some range-Doppler bins, which are identified exploiting a plurality of knowledge sources. From a technical point of view, this is tantamount to optimizing a real and homogeneous complex quartic order polynomial with a constant modulus constraint on each optimization variable. After proving some interesting properties of the considered problem, we devise a polynomial-time waveform optimization procedure based on the Maximum Block Improvement (MBI) method and the theory of conjugate-partial-symmetric/conjugate-super-symmetric fourth order tensors. At the analysis stage, we assess the performance of the proposed technique showing its capability to properly shape the range-Doppler response of the transmitted waveform.

An adaptive waveform-detection threshold joint optimization method for target tracking

The joint optimization of detection threshold and waveform parameters for target tracking which comes from the idea of cognitive radar is investigated for the modified probabilistic data association (MPDA) filter. The transmitted waveforms and detection threshold are adaptively selected to enhance the tracking performance. The modified Riccati equation is adopted to predict the error covariance which is used as the criterion function, while the optimization problem is solved through the genetic algorithm (GA). The detection probability, false alarm probability and measurement noise covariance are all considered together, which significantly improves the tracking performance of the joint detection and tracking system. Simulation results show that the proposed adaptive waveform-detection threshold joint optimization method outperforms the adaptive threshold method and the fixed parameters method, which will reduce the tracking error. The average reduction of range error between the adaptive joint method and the fixed parameters method is about 0.6 m, while that between the adaptive joint method and the adaptive threshold only method is about 0.3 m. Similar error reduction occurs for the velocity error and acceleration error.

Dynamic waveform selection for manoeuvering target tracking in clutter

In recent years, cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and become an area of vigorous research and development. This study presents a dynamic waveform selection algorithm to strive for tracking error minimisation for CR manoeuvering target tracking in clutter. Based on the concepts of resolution cell and measurement extraction cell, the statistical characteristics of radar measurements are discussed without dependence upon the Cramer-Rao lower bound of the measurement errors and the high signal-to-noise ratio assumption. A particle filter combined with probabilistic data association is used as a tracker. To quantify the utility of available waveforms, the predicted tracking mean-square error, because of its dependence on actual future measurements, is approximated efficiently via Gaussian fitting of the prior density of the target state and statistical linearisation of the measurement equation. Monte Carlo simulation results show that the proposed dynamic waveform selection algorithm can improve tracking performance considerably, especially in terms of track loss probability.

Robust Transmitted Waveform and Received Filter Design for Cognitive Radar in the Presence of Signal-Dependent Interference

The problem of robust transmitted waveform and received filter design for cognitive radar in a signal-dependent interference environment is considered. When estimate errors of the target impulse response (TIR) and clutter impulse response (CIR) exist, in order to improve the worst signal-to-clutter ratio (SCR) and signal-to-interference-and-noise ratio (SINR), a robust transmitted waveform and received filter are designed based on the minimax criterion by using the information fed back from the receiver. Using deterministic and random models, the waveform and filter design problem is divided into three optimization problems. The robust waveform and filter are then obtained by solving these problems. In the deterministic model, we prove that the robust waveform and filter impulse response can be generated by a pseudorandom code. In the random model, the robust waveform and filter impulse response can be obtained by an alternative projection algorithm. Numerical results indicate that the worst SINR of the robust waveform and filter is higher than that of the traditional waveform and filter.

Cognitive Radar Network: Cooperative Adaptive Beamsteering for Integrated Search-and-Track Application

Cognitive radar (CR) is a paradigm shift from a traditional radar system in that previous knowledge and current measurements obtained from the radar channel are used to form a probabilistic understanding of its environment. Moreover, CR incorporates this probabilistic knowledge into its task priorities to form illumination and probing strategies, thereby rendering it a closed-loop system. Depending on the hardware's capabilities and limitations, there are various degrees of freedom that a CR may utilize. Here we concentrate on spatial illumination as a resource, where adaptive beamsteering is used for search-and-track functions. We propose a multiplatform cognitive radar network (CRN) for integrated search-and-track application. Specifically, two radars cooperate in forming a dynamic spatial illumination strategy, where beamsteering is matched to the channel uncertainty to perform the search function. Once a target is detected and a track is initiated, track information is integrated into the beamsteering strategy as part of CR's task prioritization.

Signal detection for cognitive radar

The problem of signal detection for cognitive radar (CR) is considered, and a closed-loop detection system is proposed to detect the extended target in the presence of signal-dependent interference. In the system, the estimator of target impulse response (TIR) and waveform design are used to improve the detection performance of CR, and the estimate of TIR is also used for designing the matched waveform. In addition, in order to improve the reliability of detection, the two-step process which is called estimation before detection is proposed. Simulation results indicate that the proposed system performs better than the traditional radar system which used the fixed waveform.

Range-spread target detecting for cognitive radar based on track-before-detect

The problem of cognitive radar detecting for range-spread target with unknown target impulse response (TIR) is studied. A detecting algorithm which updates the estimate of TIR and transmitted waveform iteratively is proposed based on track-before-detect method. In the algorithm, the Kalman filter is used to track the range-spread target for estimating the TIR, and the signal-to-noise rate criterion is used to design the transmitted waveform. The simulation results show that if the proposed algorithm is used to detect moving target, the performance of tracking and detecting will be improved when the number of loops increases. And the estimated detecting probability will approach the true detecting probability when the tracking accuracy increases, which would improve the reliability of decision.

Transmitted Waveform Design Based on Iterative Methods

In intelligent radar, it is an important problem for the transmitted waveform to adapt to the environment in which radar works. In this paper, we propose mutual information model of adaptive waveform design, which can convert the problem of adaptive waveform design into the problem of optimization. We consider two situations of no clutter and clutter and use Newton method and interior point method to solve the optimization problem. Then we can draw the design criterion for the transmitted waveform in cognitive radar and get a greater mutual information from the simulation results. Finally, the whole paper is summarized.

Adaptive Waveform Design for Multiple Radar Tasks Based on Constant Modulus Constraint

Cognitive radar is an intelligent system, and it can adaptively transmit waveforms to the complex environment. The intelligent radar system should be able to provide different trade-offs among a variety of performance objectives. In this paper, we investigate the mutual information (MI) in signal-dependent interference and channel noise. We propose a waveform design method which can efficiently synthesize waveforms and provide a trade-off between estimation performance and detection performance. After obtaining a local optimal waveform, we apply the technique of generating a constant modulus signal with the given Fourier transform magnitude to the waveform. Finally we obtain a waveform that has constant modulus property.

Cognitive Radar: Step Toward Bridging the Gap Between Neuroscience and Engineering

In this paper, we describe a cognitive radar (CR) that mimics the visual brain. Although the visual brain and radar are different in that the visual brain does not transmit a probing signal to the environment while the active radar greatly relies on the probing signal it transmits to the environment, both of them are observers of the surrounding environment. As such, there is much that we can learn from the visual brain in building a new generation of CRs that outperform traditional radars. In this paper, we confine the discussion, in both analytic and experimental terms, to CR aimed at target tracking. From a theoretical perspective, using the posterior Cramer-Rao lower bound (PCRLB), it is shown that a cognitive tracking radar has the potential to improve tracking performance significantly. In particular, computer experiments are presented, which demonstrate that CR can indeed go beyond the theoretical limits of traditional active radars (TARs) as well as fore-active radars (FARs); the latter are radars equipped with feedback from the receiver to the transmitter. Moreover, computer experiments are presented to demonstrate another practical benefit resulting from the combined use of memory and executive attention in CR for a target-tracking application. Specifically, it is shown that with the provision of these two cognitive processes, the transition in switching from one transmit waveform to another goes forward in a smooth manner. Such a capability is beyond that of TAR or FAR.

Prolog to "Cognitive radar: Step toward bridging the gap between neuroscience and engineering"

This paper studies a reconceptualization of radar, how “cognitive radar”can mimic the visual brain. There are three classes of radar to be considered. Traditional radar operates on a feedforward basis. Foreactive radar uses a feedback link connecting the receiver to the transmitter, and it is fully adaptive. Cognitive radar builds on the first two classes by developing rules of behavior (in effect, learning from experience).

Novel System Architecture and Waveform Design for Cognitive Radar Radio Networks

A novel approach to combining communication and radar functionalities in a single waveform design for cognitive radar radio (CRR) networks is proposed. This approach aims at extracting the target parameters from the radar scene, as well as facilitating high-data-rate communications between CRR nodes, by adopting a single waveform optimization solution. The system design technique addresses the coexisting communication and radar detection problems in mission-critical services, where there is a need of integrating the knowledge about the target scene gained from distinct radar entities functioning in tandem with each other. High spatial resolution and immunity to multipath fading make ultrawideband (UWB) signals an appropriate choice for such applications. The proposed solution is achieved by applying the mutual-information-based strategy to design the sequence of UWB transmission pulses and embed into them the communication data with the pulse position modulation scheme. With subsequent iterations of the algorithm, simulation results demonstrate an improvement in extraction of the parameters from the radar scene, such as target position and impulse response, while still maintaining high-throughput radio links with low bit error rates between CRR nodes.

Illumination waveform design for non-Gaussian multi-hypothesis target classification in cognitive radar

A cognitive radar (CR) system is one that observes and learns from the environment; then uses a dynamic closed-loop feedback mechanism to adapt the illumination waveform so as to provide system performance improvements over traditional radar systems. Romero et al. recently developed a CR system that performs multiple hypothesis target classification and exploits the spectral sparsity of correlated narrowband target responses to achieve significant performance improvements over traditional radars that use wideband illumination pulses. This CR system was designed for Gaussian target responses. This research generalizes the CR system to deal effectively with arbitrary non-Gaussian distributed target responses via two key contributions: (1) an important statistical expected value operation that is usually evaluated in closed form is evaluated numerically using an ensemble averaging operation, and (2) a powerful new statistical sampling algorithm and a kernel density estimator are applied to draw complex target samples from target distributions specified by both a desired power spectral density and an arbitrary desired probability density function. Simulations using non-Gaussian targets demonstrate very effective algorithm performance.

Cognitive Dynamic Systems: Radar, Control, and Radio [Point of View

In this article on cognitive dynamic systems, the progress made and the way forward on this multidisciplinary integrative field has been reviewed. The following topics has been discussed: brief historical notes on human cognition; advances made on cognitive radar of the monostatic kind; emergence of cognitive control for the first time, building on the new concept of a two-state model and the significant progress made on cognitive radio on several fronts.

Waveform design and high-resolution imaging of cognitive radar based on compressive sensing

We introduce the compressive sensing (CS) theory for waveform design of cognitive radar, and then propose an algorithm for the high-resolution radar signal waveform and its corresponding imaging method based on the sparse orthogonal frequency division multiplexing-linear frequency modulation (OFDM-LFM) signal. We first present the principle of spectrum synthesis and high-resolution imaging based on OFDM-LFM signals. Then, we propose the spectrum-sparse waveform design criterion and the reconstruction algorithm for a highresolution range profile (HRRP) based on CS. Based on this, we analyze in detail the relationship between the scattering characteristics of the target and the parameters of the designed signal, and we construct the feedback of the target characteristics on the waveforms. Therefore, the “cognitive” function of radar can be achieved by adaptively adjusting the waveform with the target characteristics. Simulations are given to validate the effectiveness of the proposed algorithm.

Scheduling and Power Allocation in a Cognitive Radar Network for Multiple-Target Tracking

We propose a cognitive radar network (CRN) system for the joint estimation of the target state comprising the positions and velocities of multiple targets, and the channel state comprising the propagation conditions of an urban transmission channel. We develop a measurement model for the received signal by considering a finite-dimensional representation of the time-varying system function which characterizes the urban transmission channel. We employ sequential Bayesian filtering at the receiver to estimate the target and the channel state. We propose a hybrid Bayesian filter that operates by partitioning the state space into smaller subspaces and thereby reducing the complexity involved with high-dimensional state space. The feedback loop that embodies the radar environment and the receiver enables the transmitter to employ approximate greedy programming to find a suitable subset of antennas to be employed in each tracking interval, as well as the power transmitted by these antennas. We compute the posterior Cramer-Rao bound (PCRB) on the estimates of the target state and the channel state and use it as an optimization criterion for the antenna selection and power allocation algorithms. We use several numerical examples to demonstrate the performance of the proposed system.

Cognitive Dynamic Systems

The main topics covered in this paper address the following four issues: 1) Distinction between how adaptation and cognition are viewed with respect to each other, 2) With human cognition viewed as the framework for cognition, the following cognitive processes are identified: the perception-action cycle, memory, attention, intelligence, and language. With language being outside the scope of the paper, detailed accounts of the other four cognitive processes are discussed, 3) Cognitive radar is singled out as an example application of cognitive dynamic systems that “mimics” the visual brain; experimental results on tracking are presented using simulations, which clearly demonstrate the information-processing power of cognition, and 4) Two other example applications of cognitive dynamic systems, namely, cognitive radio and cognitive control, are briefly described.

Joint Multi-Target Identification and Classification in Cognitive Radar Sensor Networks

We investigate the problem of jointly classifying and identifying multiple targets in radar sensor networks where the maximum number of categories and the maximum number of targets in each category are obtained a priori based on statistical data. However, the actual number of targets in each category and the actual number of target categories being present at any given time are assumed unknown. It is assumed that a given target only belongs to one category and one identification number. The target signals are moreover modeled as zero-mean complex Gaussian processes. In this paper, we propose a joint multi-target identification and classification (JMIC) algorithm for radar surveillance using the cognitive radar network. The existing target categories are first classified and then the targets in each category are accordingly identified. Simulation results are presented to evaluate the feasibility and effectiveness of the proposed JMIC algorithm in a query surveillance region.

Control theoretic approach to tracking radar: First step towards cognition

In Haykin (2006) [8], the idea of Cognitive Radar was described for the first time. Four essential points were emphasized in that seminal paper: Bayesian filtering in the receiver, dynamic programming in the transmitter, memory, and global feedback to facilitate computational intelligence. This paper provides a first step towards designing a cognitive radar for tracking applications by presenting a fore-active tracking radar; a radar that utilizes its previous measurements and actions to optimize its transmitted waveform (Haykin, 2011 [11]). In our design, the emphasis is being placed on the cubature Kalman filter to approximate the Bayesian filter in the receiver, approximate dynamic programming for transmit-waveform selection in the transmitter, and global feedback embodying the transmitter, the radar environment, and the receiver all under one overall feedback loop. Simulation results, based on the tracking of an object falling in space, are presented, which substantiate practical validity of the superior performance of a fore-active tracking radar over a traditional active radar with fixed waveform.