An adaptive dwell time scheduling model for phased array radar based on three-way decision

Real-time resource allocation is crucial for phased array radars to undertake multi-task with limited resources such as in the situation of multi-target tracking, in which targets need to be prioritized so that resources can be allocated accordingly and effectively. In this paper, a three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time. Using the model, the threat posed by a target is measured by an evaluation function, and therefore, a target is assigned to one of the three possible decision regions, i.e., positive region, negative region, and boundary region. A different region has a various priority in terms of resource demand, and as such, a different radar resource allocation decision is applied to each region to satisfy different tracking accuracy of multi-target. In addition, the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time. The advantages and the performance of the proposed model has been verified by experimental simulations with comparison to the traditional two-way decision model and the three-way decision model without threshold optimization. The experiential results have demonstrated that the performance of the proposed model has a certain advantage in detecting high threat targets. 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Performance evaluation of track association and maintenance for a MFPAR with doppler velocity measurements

This study investigates the effects of incorporating Doppler velocity measurements directly into track association and maintenance parts for single and multiple target tracking unit in a multi function phased array radar (MFPAR). Since Doppler velocity is the major discriminant of clutter from a desired target, the measurement set has been expanded from range, azimuth and elevation angles to include Doppler velocity measurements. We have developed data association and maintenance part of a well known tracking method, Interacting Multiple Model Probabilistic Data Association

Using Interacting Multiple Model Filters to Indicate Program Risk

Technology development has increased exponentially. Program managers are pushed to accelerate development. There are many resources available to program managers that enable acceleration, such as: additional resources in the form of funding, people and technology. There are also negative impacts to acceleration, such as: inclusion, inexperience program managers, and communication. This research seeks to identify the limit to which a program or project can be accelerated before the program manager begins to accept an unacceptable amount of pre-determined risk. This research will utilize estimation algorithms used by sensor systems to estimate the current and future state of objects in space. The most common estimation algorithm used is the Kalman filter developed by Kalman (Bar-Shalom, Rong Li, & Kirubarajan, 2001). This research will examine the use of two Kalman filters in for the form of an Interacting Multiple Model (IMM) in order to predict the future state of the program. Traditional multiple model filters use Bayesian technique to adaptively switch between different motion models implemented in the filter structure (USA Patent No. 7030809, 2005). These logic designs rely upon a predefined Markov Switching Matrix (MSM). If the future state approaches a predetermined acceptable level of risk, the MSM will indicate to the program manager that the project has potentially reached a level of unacceptable risk

Multi Sensor Multi Target Perception and Tracking for Informed Decisions in Public Road Scenarios

Multi-target tracking in public traffic calls for a tracking system with automated track initiation and termination facilities in a randomly evolving driving environment. Besides, the key problem of data association needs to be handled effectively considering the limitations in the computational resources on-board an autonomous car. The challenge of the tracking problem is further evident in the use of high-resolution automotive sensors which return multiple detections per object. Furthermore, it is customary to use multiple sensors that cover different and/or over-lapping Field of View and fuse sensor detections to provide robust and reliable tracking. As a consequence, in high-resolution multi-sensor settings, the data association uncertainty, and the corresponding tracking complexity increases pointing to a systematic approach to handle and process sensor detections. In this work, we present a multi-target tracking system that addresses target birth/initiation and death/termination processes with automatic track management features. These tracking functionalities can help facilitate perception during common events in public traffic as participants (suddenly) change lanes, navigate intersections, overtake and/or brake in emergencies, etc. Various tracking approaches including the ones based on joint integrated probability data association (JIPDA) filter, Linear Multi-target Integrated Probabilistic Data Association (LMIPDA) Filter, and their multi-detection variants are adapted to specifically include algorithms that handle track initiation and termination, clutter density estimation and track management. The utility of the filtering module is further elaborated by integrating it into a trajectory tracking problem based on model predictive control. To cope with tracking complexity in the case of multiple high-resolution sensors, we propose a hybrid scheme that combines the approaches of data clustering at the local sensor and multiple detections tracking schemes at the fusion layer. We implement a track-to-track fusion scheme that de-correlates local (sensor) tracks to avoid double counting and apply a measurement partitioning scheme to re-purpose the LMIPDA tracking algorithm to multi-detection cases. In addition to the measurement partitioning approach, a joint extent and kinematic state estimation scheme are integrated into the LMIPDA approach to facilitate perception and tracking of an individual as well as group targets as applied to multi-lane public traffic. We formulate the tracking problem as a two hierarchical layer. This arrangement enhances the multi-target tracking performance in situations including but not limited to target initialization(birth process), target occlusion, missed detections, unresolved measurement, target maneuver, etc. Also, target groups expose complex individual target interactions to help in situation assessment which is challenging to capture otherwise. The simulation studies are complemented by experimental studies performed on single and multiple (group) targets. Target detections are collected from a high-resolution radar at a frequency of 20Hz; whereas RTK-GPS data is made available as ground truth for one of the target vehicle\u27s trajectory

Autonomous agents for multi-function radar resource management

The multifunction radar, aided by advances in electronically steered phased array technology, is capable of supporting numerous, differing and potentially conflicting tasks. However, the full potential of the radar system is only realised through its ability to automatically manage and configure the finite resource it has available. This thesis details the novel application of agent systems to this multifunction radar resource management problem. Agent systems are computational societies where the synergy of local interactions between agents produces emergent, global desirable behaviour. In this thesis the measures and models which can be used to allocate radar resource is explored; this choice of objective function is crucial as it determines which attribute is allocated resource and consequently constitutes a description of the problem to be solved. A variety of task specific and information theoretic measures are derived and compared. It is shown that by utilising as wide a variety of measures and models as possible the radar’s multifunction capability is enhanced. An agent based radar resource manager is developed using the JADE Framework which is used to apply the sequential first price auction and continuous double auctions to the multifunction radar resource management problem. The application of the sequential first price auction leads to the development of the Sequential First Price Auction Resource Management algorithm from which numerous novel conclusions on radar resource management algorithm design are drawn. The application of the continuous double auction leads to the development of the Continuous Double Auction Parameter Selection (CDAPS) algorithm. The CDAPS algorithm improves the current state of the art by producing an improved allocation with low computational burden. The algorithm is shown to give worthwhile improvements in task performance over a conventional rule based approach for the tracking and surveillance functions as well as exhibiting graceful degradation and adaptation to a dynamic environment

Efficient Kalman Filtering and Smoothing

The Kalman filter and Kalman smoother are important components in modern multitarget tracking systems. Their application are vast which include guidance, navigation and control of vehicles. On top of that, when the motion model is uncertain, MultipleModel approach can be combined with the filtering and smoothing method. However, with large amount of retrodiction window size, number of motion models and large number of targets, this process can become very computationally intensive and thus time consuming. Very often, real-time processing is needed in the world of tracking and therefore, this computational bottleneck become a problem. This is the motivation behind this thesis, to reduce the computational complexity when multi-target, multiwindow or multi-model applications are used. This thesis presents several approaches to tackle this multi-dimensional problems in terms of complexity while maintaining satisfactory precision. A natural step forward will be in leveraging the modern multi-core architectures. However, in order to parallelize such process, these algorithms have to be reformulated to be fitted into the parallel processors. In order to parallelise multi-target and multi-window scenario, this thesis introduce nested parallelism and prefix-sum algorithm to tackle the problem and realised this on Intel Knights Landing (KNL) Processor and OpenMP memory model. On the other hand, in the case of limited parallel resources, this thesis also develop alternatives called Fast Kalman smoother (FRTS) to lower the computation complexity due to multi-window problem. Specifically the smoother algorithm is reformulated such that it is computationally independent of number of window size in the fixed-lag configuration. Although the underlying mathematics is the same as the conventional approach, FRTS introduced numerical stability issue which makes the smoother unstable. Therefore, this thesis introduce the idea of condition number to monitor the deterioration rate in order to correct the numerical error once the pre-set threshold is breached. In addition to the large number of targets and retrodiction window size mentioned earlier, the number of models running simultaneously make the problem even more challenging in the perspective of real-time performance. Since such algorithms are the fundamental backbone of a large amount of multi-frame tracking algorithms, it would be beneficial to have a multi-model algorithm that is computationally independent to number of model utilised. Consequently, this thesis extend the FRTS concept to fixed-lag Multiple-Model smoothing method to achieve this goal. The proposed algorithms are compared and tested through an extensive and exhaustive set of evaluations against the literature, and discuss the relative merits. These evaluations show that these contributions pave a way to secure substantial performance gains for multi-dimensional tracking algorithms over conventional approaches

Reaching Consensus with uncertainty on a network

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 189-197).As modern communication networks become increasingly advanced, so does the ability and necessity to communicate to make more informed decisions. However, communication alone is not sucient; the method by which information is incorporated and used to make the decision is of critical importance. This thesis develops a novel distributed agreement protocol that allows multiple agents to agree upon a parameter vector particularly when each agent has a unique measure of possibly non-Gaussian uncertainty in its estimate. The proposed hyperpa- rameter consensus algorithm builds upon foundations in both the consensus and data fusion communities by applying Bayesian probability theory to the agreement problem. Unique to this approach is the ability to converge to the centralized Bayesian parameter estimate of non-Gaussian distributed variables over arbitrary, strongly connected networks and without the burden of the often prohibitively complex lters used in traditional data fusion solutions. Convergence properties are demonstrated for local estimates described by a number of common probability distributions and over a range of networks. The benet of the proposed method in distributed estimation is shown through its application to a multi-agent reinforcement learning problem. Additionally, this thesis describes the hardware implementation and testing of a distributed coordinated search, acquisition and track algorithm, which is shown to capably handle the con icting goals of searching and tracking. However, it is sensitive to the estimated target noise characteristics and assumes consistent search maps across the fleet.(cont.) Two improvements are presented to correct these issues: an adaptive tracking algorithm which improves the condence of target re-acquisition in periodic tracking scenarios, and a method to combine disjoint probabilistic search maps using the hyperparameter consensus algorithm to obtain the proper centralized search map.by Cameron S. R. Fraser.S.M

Robust decision-making with model uncertainty in aerospace systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (p. 161-168).Actual performance of sequential decision-making problems can be extremely sensitive to errors in the models, and this research addressed the role of robustness in coping with this uncertainty. The first part of this thesis presents a computationally efficient sampling methodology, Dirichlet Sigma Points, for solving robust Markov Decision Processes with transition probability uncertainty. A Dirichlet prior is used to model the uncertainty in the transition probabilities. This approach uses the first two moments of the Dirichlet to generates samples of the uncertain probabilities and uses these samples to find the optimal robust policy. The Dirichlet Sigma Point method requires a much smaller number of samples than conventional Monte Carlo approaches, and is empirically demonstrated to be a very good approximation to the robust solution obtained with a very large number of samples. The second part of this thesis discusses the area of robust hybrid estimation. Model uncertainty in hybrid estimation can result in significant covariance mismatches and inefficient estimates. The specific problem of covariance underestimation is addressed, and a new robust estimator is developed that finds the largest covariance admissible within a prescribed uncertainty set. The robust estimator can be found by solving a small convex optimization problem in conjunction with Monte Carlo sampling, and reduces estimation errors in the presence of transition probability uncertainty. The Dirichlet Sigma Points are extended to this problem to reduce the computational requirements of the estimator. In the final part of the thesis, the Dirichlet Sigma Points are extended for real-time adaptation. Using insight from estimation theory, a modified version of the Dirichlet Sigma Points is presented that significantly improves the response time of classical estimators. The thesis is concluded with hardware implementation of these robust and adaptive algorithms on the RAVEN testbed, demonstrating their applicability to real-life UAV missions.by Luca Francesco Bertuccelli.Ph.D

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements