Cognitive Radar Network Research Articles

Timely Target Tracking: Distributed Updating in Cognitive Radar Networks

Timely Target Tracking: Distributed Updating in Cognitive Radar Networks

Hybrid Cognition for Target Tracking in Cognitive Radar Networks

This work investigates online learning techniques for a cognitive radar network utilizing feedback from a central coordinator. The available spectrum is divided into channels, and each radar node must transmit in one channel per time step. The network attempts to optimize radar tracking accuracy by learning the optimal channel selection for spectrum sharing and radar performance. We define optimal selection for such a network in relation to the radar observation quality obtainable in a given channel. This is a difficult problem since the network must seek the optimal assignment from nodes to channels, rather than just seek the best overall channel. Since the presence of primary users appears as interference, the approach also improves spectrum sharing performance. In other words, maximizing radar performance also minimizes interference to primary users. Each node is able to learn the quality of several available channels through repeated sensing. We define hybrid cognition as the condition where both the independent radar nodes as well as the central coordinator are modeled as cognitive agents, with restrictions on the amount of information that can be exchanged between the radars and the coordinator. Importantly, each part of the network acts as an online learner, observing the environment to inform future actions. We show that in interference-limited spectrum, where the signal-to-interference-plus-noise ratio varies by channel and over time for a target with fixed radar cross section, a cognitive radar network is able to use information from the central coordinator in order to reduce the amount of time necessary to learn the optimal channel selection. We also show that even limited use of a central coordinator can eliminate collisions, which occur when two nodes select the same channel. We provide several reward functions which capture different aspects of the dynamic radar scenario and describe the online machine learning algorithms which are applicable to this structure. In addition, we study varying levels of feedback, where central coordinator update rates vary. We compare our algorithms against baselines and demonstrate dramatic improvements in convergence time over the prior art. A network using hybrid cognition is able to use a minimal amount of feedback to achieve much faster convergence times and therefore lower tracking error.

Distributed Online Learning for Coexistence in Cognitive Radar Networks

This work addresses the coexistence problem for radar networks. Specifically, we model a network of cooperative, independent, and non-communicating radar nodes which must share resources within the network as well as with non-cooperative nearby emitters. We approach this problem using online Machine Learning (ML) techniques. Online learning approaches are specifically preferred due to the fact that each radar node has no prior knowledge of the environment nor of the positions of the other radar nodes, and due to the sequential nature of the problem. For this task we specifically select the multi-player multi-armed bandit (MMAB) model, which poses the problem as a sequential game, where each radar node in a network makes independent selections of center frequency and waveform with the same goal of improving tracking performance for the network as a whole. For accurate tracking, each radar node communicates observations to a fusion center on set intervals. The fusion center has knowledge of the radar node placement, but cannot communicate to the individual nodes fast enough for waveform control. Every radar node in the network must learn the behavior of the environment, which includes rewards, interferer behavior, and target behavior. Each independent and identical node must choose one of many waveforms to transmit in each Pulse Repetition Interval (PRI) while avoiding <i>collisions</i> with other nodes and interference from the environment. The goal for the network as a whole is to minimize target tracking error, which relies on obtaining high SINR in each time step. Our contributions include a mathematical description of the MMAB framework adapted to the radar network scenario. We conclude with a simulation study of several different network configurations. Experimental results show that iterative, online learning using MMAB outperforms the more traditional sense-and-avoid (SAA) and fixed-allocation approaches.

Advance stagger scheduling of pulses in cognitive radar networks

Advance stagger scheduling of pulses in cognitive radar networks

Joint threshold optimization and power allocation of cognitive radar network for target tracking in clutter

Networked radar systems have the ability to enhance the tracking performance by working in a cognitive manner. In the cognitive radar network, resource allocation technique can be employed to improve the system performance. In this paper, a joint threshold optimization and power allocation (JTOPA) strategy for target tracking by radar network in clutter is proposed containing two main stages. At the detection stage, we introduce the Bayesian tracker-aware detector to adaptively adjust the threshold based on the predicted target state information. At the transmitting stage, the limited power resource is optimally assigned to each radar node. By incorporating the improved information reduction factor (IIRF), the Bayesian Cramér-Rao lower bound (BCRLB) is utilized as the optimization criterion for the JTOPA strategy. The JTOPA is a two-variable nonconvex optimization problem. We develop a spectral projected gradient based method to solve the problem. Simulations show that the proposed JTOPA strategy effectively improves the tracking performance compared with the uniform threshold setting and power allocation in the cognitive radar network.

Target tracking via recursive Bayesian state estimation in cognitive radar networks

To cope with complicated environments and stealthier targets, incorporating intelligence and cognition cycles into target tracking is of great importance in modern sensor network management. With remarkable advances in sensor techniques and deployable platforms, a sensing system has freedom to select a subset of available radars, plan their trajectories, and transmit designed waveforms. In this paper, we propose a general framework for single target tracking in cognitive networks of radars, including consideration of waveform design, path planning, and radar selection, which are separately but not jointly taken into account in existing work. The tracking procedure, built on the theories of dynamic graphical models (DGM) and recursive Bayesian state estimation (RBSE), is formulated as two iterative steps: (i) solving a combinatorial optimization problem to select the optimal subset of radars, waveforms, and locations for the next tracking instant, and (ii) acquiring the recursive Bayesian state estimation to accurately track the target. Further, an illustrative example introduces a specific scenario in 2-D space. Simulation results based on the scenario demonstrate that the proposed framework can accurately track the target under the management of the network of radars.

Spectrally-Constrained Sequences: Bounds and Constructions

We investigate spectrally-constrained sequences (SCSs), which are applicable to the communication and radar systems operating over non-contiguous carriers or frequency slots. Typical examples of such systems are overlay cognitive radio or cognitive radar networks. First, we derive the periodic- and aperiodic-correlation lower bounds for single-channel SCSs and multi-channel SCSs by convex optimization in the frequency domain. Each of these bounds reduces to a Welch bound when the number of forbidden carriers is set to zero. We then propose systematic constructions of optimal unimodular single-channel SCSs with the aid of cyclic difference sets and the theory of maximal-length shift register sequences.

Research on an improved parallel algorithm based on user’s requirement in cognitive radar network

Abstract In cognitive radar network, a parallel algorithm can allocate idle spectrum to cognitive radars, but it often ignores the user’s requirement. According to the graph theory, the topology of the network is divided into two different groups. In the process of distributing spectrum, each group summarizes the distributed information after completing a distribution. When a user’s demand is satisfied, every group is informed to not allocate the spectrum for the user anymore and deletes it from the topology at the same time. As a result, by deleting the node which the user’s requirement has already been met, the other nodes which are interfered with the node on the same channel can participate into spectrum allocation. The improved algorithm can ensure high spectrum utilization taking a little time that is spent on the spectrum allocation. The simulation result shows that the spectrum utilization of the improved algorithm is higher than that of the traditional parallel algorithm, and the user’s satisfaction increases greatly. Therefore, this paper aims to attempt to explore an improved parallel algorithm to resolve the problem by considering user’s requirement.

Signal Design for Context Aware Distributed Radar Sensing Networks Based on Wavelets

In the field of radar sensing, a paradigm shift is taking place from the traditional radar system to distributed sensing/radar networks. Comparing with the single radar system distributed radar networks have a superior performance in detection and estimation of parameters of target. They also enhance the imaging and classification performance of sensing systems. As in distributed radar networks several radars operate at the same frequency band in a network and share the spectrum they may interfere each other, and accordingly the radar-to-radar interference becomes a major problem to be addressed. Therefore, the context aware design of radar signals for radar sensing networks to cope with the interference is pronounced. On the other hand Wavelet packet transform has recently emerged as a novel signal design technique with attractions such as good time–frequency resolution, low sidelobes, and the reconfigurability capability. The wavelet approach is advantageous for the signal design of cognitive radar networks mainly because of its flexibility, lower sensitivity to distortion and interference, and better utilization of spectrum. For minimum interference to the adjacent radar bands, the wavelets should be maximally frequency selective. Commonly known wavelets are not frequency selective in nature and hence result in poor spectrum sharing performance. To alleviate this problem, we design a family of wavelets that are maximally frequency selective in nature. To this end, the design constraints are first enlisted. Then the problem, originally non-convex, is reformulated into a convex optimization problem and solved using Semi Definite Programming tools. Through simulation studies the benefits of the newly designed wavelets for a distributed radar sensing network are demonstrated.

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.

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.

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.

Extended Target Recognition in Cognitive Radar Networks

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Biologically Inspired Target Recognition in Radar Sensor Networks

One of the great mysteries of the brain is cognitive control. How can the interactions between millions of neurons result in behavior that is coordinated and appears willful and voluntary? There is consensus that it depends on the prefrontal cortex (PFC). Many PFC areas receive converging inputs from at least two sensory modalities. Inspired by human's innate ability to process and integrate information from disparate, network-based sources, we apply human-inspired information integration mechanisms to target detection in cognitive radar sensor network. Humans' information integration mechanisms have been modelled using maximum-likelihood estimation (MLE) or soft-max approaches. In this paper, we apply these two algorithms to cognitive radar sensor networks target detection. Discrete-cosine-transform (DCT) is used to process the integrated data from MLE or soft-max. We apply fuzzy logic system (FLS) to automatic target detection based on the AC power values from DCT. Simulation results show that our MLE-DCT-FLS and soft-max-DCT-FLS approaches perform very well in the radar sensor network target detection, whereas the existing 2D construction algorithm does not work in this study.