A Priority-Based Scheduling Scheme for Search, Track, and Communications in MPARS

The modern battlefield scenario is strongly influenced by the innovative capabilities of the multifunction phased array radars (MPARs) which can perform sequentially or in parallel a plethora of sensing and communication activities. As a matter of fact, the MPAR can functionally cluster its phased array into bespoke sub-apertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each sub-aperture and the grand challenge is the definition of strategies for an optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this paper. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz. volume search, cued search, update and confirmation tracking) and then utilize the communication (COM) looks so as to fill the empty intra-slot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in term of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets

The Constant Information Radar

abstract: The constant information radar, or CIR, is a tracking radar that modulates target revisit time by maintaining a fixed mutual information measure. For highly dynamic targets that deviate significantly from the path predicted by the tracking motion model, the CIR adjusts by illuminating the target more frequently than it would for well-modeled targets. If SNR is low, the radar delays revisit to the target until the state entropy overcomes noise uncertainty. As a result, we show that the information measure is highly dependent on target entropy and target measurement covariance. A constant information measure maintains a fixed spectral efficiency to support the RF convergence of radar and communications. The result is a radar implementing a novel target scheduling algorithm based on information instead of heuristic or ad hoc methods. The CIR mathematically ensures that spectral use is justified

Mode Selection and Target Classification in Cognitive Radar Networks

Cognitive Radar Networks were proposed by Simon Haykin in 2006 to address problems with large legacy radar implementations - primarily, single-point vulnerabilities and lack of adaptability. This work proposes to leverage the adaptability of cognitive radar networks to trade between active radar observation, which uses high power and risks interception, and passive signal parameter estimation, which uses target emissions to gain side information and lower the power necessary to accurately track multiple targets. The goal of the network is to learn over many target tracks both the characteristics of the targets as well as the optimal action choices for each type of target. In order to select between the available actions, we utilize a multi-armed bandit model, using current class information as prior information. When the active radar action is selected, the node estimates the physical behavior of targets through the radar emissions. When the passive action is selected, the node estimates the radio behavior of targets through passive sensing. Over many target tracks, the network collects the observed behavior of targets and forms clusters of similarly-behaved targets. In this way, the network meta-learns the target class distributions while learning the optimal mode selections for each target class.Comment: 6 pages, 5 figure

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

Adaptive Beamsteering Cognitive Radar with Integrated Search-and-Track of Swarm Targets

The article of record as published may be found at http://dx.doi.org/10.1109/ACCESS.2021.3069350, IEEE AccessAdaptive beamsteering cognitive radar (AB-CRr) systems seek to improve detection and tracking performance by formulating a beam placement strategy adapted to their environment. AB-CRr builds a probabilistic model of the target environment that enables it to more efficiently employ its limited resources to locate and track targets. In this work, we investigate methods for adapting the AB- CRr framework to detect and track large target swarms. This is achieved by integrating the properties of correlated-motion swarms into both the radar tracking model and AB-CRr’s underlying dynamic probability model. As a result, a list of newly CRr-integrated contributions are enumerated: a) improved uncertainty function design, b) incorporates Mahalanobis nearest neighbors multi-target association methodology into AB-CRr, c) introduces a novel Kalman-based consolidated swarm tracking methodology with a common velocity state vector that frames targets as a correlated collection of swarm members, d) introduces an improved uncertainty growth model for updating environment probability map, e) introduces a method for incorporating estimated swarm structure and behavior into the uncertainty update model referred to as "track hinting", and f) introduces new metrics for swarm search/detection and tracking called swarm centroid track error and swarm tracking dwell ratio. The results demonstrate that AB-CRr is capable of adapting its beamsteering strategy to efficiently perform resource balancing between target search and swarm tracking applications, while taking advantage of group structure and intra-swarm target correlation to resist large swarms overloading available resources.Approved for public release; distribution is unlimited

Multiple-Target Tracking in Complex Scenarios

In this dissertation, we develop computationally efficient algorithms for multiple-target tracking: MTT) in complex scenarios. For each of these scenarios, we develop measurement and state-space models, and then exploit the structure in these models to propose efficient tracking algorithms. In addition, we address design issues such as sensor selection and resource allocation. First, we consider MTT when the targets themselves are moving in a time-varying multipath environment. We develop a sparse-measurement model that allows us to exploit the inherent joint delay-Doppler diversity offered by the environment. We then reformulate the problem of MTT as a block-support recovery problem using the sparse measurement model. We exploit the structure of the dictionary matrix to develop a computationally efficient block support recovery algorithm: and thereby a multiple-target tracking algorithm) under the assumption that the channel state describing the time-varying multipath environment is known. Further, we also derive an upper bound on the overall error probability of wrongly identifying the support of the sparse signal. We then relax the assumption that the channel state is known. We develop a new particle filter called the Multiple Rao-Blackwellized Particle Filter: MRBPF) to jointly estimate both the target and the channel states. We also compute the posterior Cramér-Rao bound: PCRB) on the estimates of the target and the channel states and use the PCRB to find a suitable subset of antennas to be used for transmission in each tracking interval, as well as the power transmitted by these antennas. Second, we consider the problem of tracking an unknown number and types of targets using a multi-modal sensor network. In a multi-modal sensor network, different quantities associated with the same state are measured using sensors of different kinds. Hence, an efficient method that can suitably combine the diverse information measured by each sensor is required. We first develop a Hierarchical Particle Filter: HPF) to estimate the unknown state from the multi-modal measurements for a special class of problems which can be modeled hierarchically. We then model our problem of tracking using a hierarchical model and then use the proposed HPF for joint initiation, termination and tracking of multiple targets. The multi-modal data consists of the measurements collected from a radar, an infrared camera and a human scout. We also propose a unified framework for multi-modal sensor management that comprises sensor selection: SS), resource allocation: RA) and data fusion: DF). Our approach is inspired by the trading behavior of economic agents in commercial markets. We model the sensors and the sensor manager as economic agents, and the interaction among them as a double sided market with both consumers and producers. We propose an iterative double auction mechanism for computing the equilibrium of such a market. We relate the equilibrium point to the solutions of SS, RA and DF. Third, we address MTT problem in the presence of data association ambiguity that arises due to clutter. Data association corresponds to the problem of assigning a measurement to each target. We treat the data association and state estimation as separate subproblems. We develop a game-theoretic framework to solve the data association, in which we model each tracker as a player and the set of measurements as strategies. We develop utility functions for each player, and then use a regret-based learning algorithm to find the correlated equilibrium of this game. The game-theoretic approach allows us to associate measurements to all the targets simultaneously. We then use particle filtering on the reduced dimensional state of each target, independently

A Hybrid Approach to Cognition in Radars

In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results

Joint Route Optimization and Multidimensional Resource Management Scheme for Airborne Radar Network in Target Tracking Application

In this article, we investigate the problem of joint route optimization and multidimensional resource management (JRO-MDRM) for an airborne radar network in target tracking application. The mechanism of the proposed JRO-MDRM scheme is to adopt the optimization technique to collaboratively design the flight route, transmit power, dwell time, waveform bandwidth, and pulselength of each airborne radar node subject to the system kinematic limitations and several resource budgets, with the aim of simultaneously enhancing the target tracking accuracy and low probability of intercept (LPI) performance of the overall system. The predicted Bayesian Cramér–Rao lower bound and the probability of intercept are calculated and employed as the metrics to gauge the target tracking performance and LPI performance, respectively. It is shown that the resulting optimization problem is nonlinear and nonconvex, and the corresponding working parameters are coupled in both objective functions, which is generally intractable. By incorporating the particle swarm optimization and cyclic minimization approaches, an efficient four-step solution algorithm is proposed to deal with the above problem. Extensive numerical results are provided to demonstrate the correctness and advantages of our developed scheme compared with other existing benchmarks

Collaborative Trajectory Planning and Resource Allocation for Multi-Target Tracking in Airborne Radar Networks under Spectral Coexistence

This paper develops a collaborative trajectory planning and resource allocation (CTPRA) strategy for multi-target tracking (MTT) in a spectral coexistence environment utilizing airborne radar networks. The key mechanism of the proposed strategy is to jointly design the flight trajectory and optimize the radar assignment, transmit power, dwell time, and signal effective bandwidth allocation of multiple airborne radars, aiming to enhance the MTT performance under the constraints of the tolerable threshold of interference energy, platform kinematic limitations, and given illumination resource budgets. The closed-form expression for the Bayesian Cramér–Rao lower bound (BCRLB) under the consideration of spectral coexistence is calculated and adopted as the optimization criterion of the CTPRA strategy. It is shown that the formulated CTPRA problem is a mixed-integer programming, non-linear, non-convex optimization model owing to its highly coupled Boolean and continuous parameters. By incorporating semi-definite programming (SDP), particle swarm optimization (PSO), and the cyclic minimization technique, an iterative four-stage solution methodology is proposed to tackle the formulated optimization problem efficiently. The numerical results validate the effectiveness and the MTT performance improvement of the proposed CTPRA strategy in comparison with other benchmarks