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Drone classification based on radar return signal is an important task for public safety applications. Determining the make or class of a drone gives information about the potential intent of the UAV. We present a novel method for classifying commercially available drones based on their radar return signal, using a convolutional neural network. Our approach achieves 0.46 mean Average Precision (mAP) on a simulated dataset at 5 dB SNR.

Many existing methods of object detection, including edge detection, blob detection, and background subtraction (implemented in libraries such as OpenCV) have proven to be enormously successful when applied to many types of video datasets. However, detecting objects over water presents challenges that are unique and not easily accommodated for by pre-existing algorithms available in popular image processing libraries. In this paper, existing approaches are brie y reviewed, and the challenges encountered in above-water video datasets are highlighted. A recently proposed approach to object detection in radar images - a novel, pixel-intensity statistic based thresholding approach | is then reviewed. In this paper, this approach has been successfully applied to EO/IR datasets as well, extending the implementation to ensure success when applied onto other types of image datasets.

Recent experimental results have demonstrated gains in sensing capability using novel possibilities offered by quantum mechanics. In particular, a prototype radar which uses quantum techniques to enhance detection ability has been built in a laboratory, showing that quantum radars at RF frequencies are feasible. This prototype is called a quantum two-mode squeezing radar (QTMS radar). In this paper, we use the QTMS radar as a springboard to review the concept of quantum squeezing. We find that Josephson parametric amplifiers (JPAs), one of which was used in the QTMS radar prototype, can be employed to produce two-mode and one-mode squeezed states at RF frequencies. We then briefly discuss some of the possible applications of such states to the field of radar engineering.

Many quantum technologies, such as quantum computers, rely on a phenomenon called entanglement. One reason why quantum networks are being studied is because they can distribute entanglement to their users. In this paper, we describe how quantum radars, particularly the recently-developed quantum two-mode squeezing radar (QTMS radar), can be used with quantum networks. On a related note, we also point out how QTMS radar can be vulnerable to interception if an adversary has access to the measurement record that the radar uses to distinguish signal from noise.

Quantum physics has a growing influence on sensor technology; particularly, in the areas of quantum computer science, quantum communications, and quantum sensing based on recent insights from atomic, molecular and optical physics. These quantum contributions have the potential to impact information fusion techniques. Quantum information technology (QIT) methods of interest suggest benefits for information fusion, so a panel was organized to articulate methods of importance for the community. The panel discussion presented many ideas from which the leading impact for information fusion is directly related to the sub-Rayleigh sensing that reduces uncertainty for object assessment through enhanced resolution. The second areas of importance is in the cyber security of data that supports data, sensor, and information fusion. Some elements of QIT that require further analysis is in quantum computing for which only a limited set of information fusion techniques can harness the methods associated with quantum computer architectures. The panel reviewed various aspects of QIT for information fusion which provides a foundation to identify future alignment between quantum and information fusion techniques.

Python State Estimation and Modeling Library, pystemlib, is a library that implements Bayesian State Estimation theory for modeling and tracking target objects. This library was developed to overcome the limitations associated with licensed programming languages as well as imperative and numerical matrix-based programming styles that were used in previously developed libraries. pystemlib incorporates object-oriented, functional, and symbolic programming to develop accurate and easy-to-use tracking filters and models. This library is also capable of mapping state estimation results onto the geographical areas to which they correspond. Future work on this library will include optimizing the algorithms for speed and extending the library to incorporate multi-target tracking, data fusion, and image and video processing.

The ability to detect and unambiguously follow all moving entities in a state-space is important in multiple domains both in defence (e.g. air surveillance, maritime situational awareness, ground moving target indication) and the civil sphere (e.g. astronomy, biology, epidemiology, dispersion modelling). However, tracking and state estimation researchers and practitioners have difficulties recreating state-of-the-art algorithms in order to benchmark their own work. Furthermore, system developers need to assess which algorithms meet operational requirements objectively and exhaustively rather than intuitively or driven by personal favourites. We have therefore commenced the development of a collaborative initiative to create an open source framework for production, demonstration and evaluation of Tracking and State Estimation algorithms. The initiative will develop a (MIT-licensed) software platform for researchers and practitioners to test, verify and benchmark a variety of multi-sensor and multi-object state estimation algorithms. The initiative is supported by four defence laboratories, who will contribute to the development effort for the framework. The tracking and state estimation community will derive significant benefits from this work, including: access to repositories of verified and validated tracking and state estimation algorithms, a framework for the evaluation of multiple algorithms, standardisation of interfaces and access to challenging data sets. Keywords: Tracking

The challenge of ownship navigation for an airborne platform in the absence of precise navigation information is an important problem. In this paper, the problem is solved using the assumed known GPS locations of landmarks by casting it in a Bayesian state-space framework. It is assumed that no information is available from the navigation sensors. The platform kinematic state is inferred by using a nonlinear filter, such as the extended Kalman filter. The performance is assessed as a function of the density of landmarks and platform manoeuvres in a simulation environment.

An airborne EO/IR (electro-optical/infrared) camera system comprises of a suite of sensors, such as a narrow and wide field of view (FOV) EO and mid-wave IR sensors. EO/IR camera systems are regularly employed on military and search and rescue aircrafts. The EO/IR system can be used to detect and identify objects rapidly in daylight and at night, often with superior performance in challenging conditions such as fog. There exist several algorithms for detecting potential targets in the bearing elevation grid. The nonlinear filtering problem is one of estimation of the kinematic parameters from bearing and elevation measurements from a moving platform. In this paper, we developed a complete model for the state of a target as detected by an airborne EO/IR system and simulated a typical scenario with single target with 1 or 2 airborne sensors. We have demonstrated the ability to track the target with 'high precision' and noted the improvement from using two sensors on a single platform or on separate platforms. The performance of the Extended Kalman filter (EKF) is investigated on simulated data. Image/video data collected from an IR sensor on an airborne platform are processed using an image tracking by detection algorithm., Signal Processing, Sensor/Information Fusion, and Target Recognition XXIV, April 20-22, 2015, Series: Proceedings of SPIE; no. 9474

The problem of tracking a number of time-varying slow-moving targets in the presence of clutter and false alarms is particularly challenging for the ground moving target indication (GMTI) application. It requires adaptive clutter cancellation techniques such as space-time adaptive processing to deal with the mainbeam clutter. In addition, GMTI radars are also used for generating synthetic aperture radar (SAR) imagery. In this paper, we analysis the performance of the joint probabilistic data association (JPDA) filter for varying coherent processing intervals (CPI) by using experimental airborne radar data with a view towards a more efficient use of GMTI and SAR modes of an airborne AESA radar.

Supersymmetry (SUSY), or Bose-Fermi symmetry, is an attempt to provide a unified description of all of the fundamental interactions. Although originally introduced in the quantum field theory context, it was noted by Witten that the understanding of certain features of SUSY was simpler in the quantum mechanical setting. Some aspects of the vast subject of supersymmetric quantum mechanics, and the supersymmetric Fokker-Planck equation, of relavance to continuous-time nonlinear filtering theory are briefly reviewed. The applicability of certain remarkable results in supersymmetric quantum mechanics to nonlinear filtering is noted and illustrated with a few examples.

The magnetic signal from a ferromagnetic object at a large distance can be modelled as that of a magnetic dipole. In many applications, the inverse problem of estimating the position, velocity and magnetic moment vector is of interest. Given the relationship between these parameters and the magnetic signature, this can be formulated as a Bayesian state estimation problem. In this paper, the extended Kalman filter is used to estimate the kinematic states as well as the dipole moment, given the total field measurements.

The detection of ground-moving targets requires clutter cancellation, which is typically performed using space-time adaptive processing (STAP). The detections from STAP provide the measurements of range, bearing, and Doppler. These measurements can then be fed to Bayesian state estimators. In this paper, results from an airborne radar data set are processed and the performance of filtering and smoothing algorithms are compared. The standard nonlinear filtering algorithms, namely the extended Kalman filter, are used. It is found that while the smoother performance is significantly better than that of the filter, the smoothing window need not be large to obtain the superior performance.

The estimation of the covariance matrix from real data is required in the application of space-time adaptive processing (STAP) to an airborne ground moving target indication (GMTI) radar. A natural approach to estimation of the covariance matrix that is based on the information geometry has been proposed. In this paper, the output of the Riemannian mean is used in inversion and projection algorithms. It is found that the projection class of algorithms can yield very significant gains, even when the gains due to inversion-based algorithms are marginal over standard algorithms. The performance of the projection class of algorithms does not appear to be overly sensitive to the projected subspace dimension.

In continuous nonlinear filtering theory, we are interested in solving certain parabolic second-order partial dif­ ferential equations (PDEs), such as the Fokker-Planck equation. The fundamental solution of such PDEs can be written in various ways, such as the Feynman-Kac integral and the Feynman path integral (FPI). In addition, the FPI can be defined in several ways. In this paper, the FPI definition based on discretization is reviewed. This has the advantage of being rigorously defined as limits of finite-dimensional integrals. The rigorous and non-rigorous approaches are compared in terms of insight and successes in nonlinear filtering as well as other areas in mathematics.

Many areas of engineering and applied science require the solution of certain parabolic partial differential equa­ tions, such as the Fokker-Planck and Kolmogorov equations. The fundamental solution, or the Green's function, for such PDEs can be written in terms of the Feynman path integral (FPI). The partial differential equation arising in the valuing of options is the Kolmogorov backward equation that is referred to as the Black-Scholes equation. The utility of this is demonstrated and numerical examples that illustrate the high accuracy of option price calculation even when using a fairly coarse grid.

The detection and tracking of surface targets using airborne radars has been extensively investigated in the literature. However, the state-of-the-art techniques in multi-target tracking do not automatically provide information that is potentially of tactical significance, such as anomalous trajectory patterns. In this paper, recent work that attempts to address this problem that is based on stochastic context-free grammars (SCFGs) is reviewed. It is shown that the production rule probabilities in SCFGs can be used to constrain sizes and orientation of target trajectories and hence lead to development of more refined syntactic trackers.

Conventional trackers provide the human operator with estimated target tracks. It is desirable to make higher level inference of the target behaviour/intent (e.g., trajectory inference) in an automated manner. One such approach is to use stochastic context-free grammars and the Earley-Stoelcke parsing algorithm. The problem of inference is reformulated as one of parsing. In this paper, the consistency of stochastic context-free grammars is reviewed. Some examples illustrating the constraints on SCFGs due to consistency are presented, including a toy SCFG that has been used to successfully parse real GMTI radar data.

This paper reviews a simple solution to the continuous-discrete Bayesian nonlinear state estimation problem that has been proposed recently. The key ideas are analytic noise processes, variational Bayes, and the formulation of the problem in terms of generalized coordinates of motion. Some of the algorithms, specifically dynamic expectation maximization and variational filtering, have been shown to outperform existing approaches like extended Kalman filtering and particle filtering. A pedagogical review of the theoretical formulation is presented, with an emphasis on concepts that are not as widely known in the filtering literature. We illustrate the appliction of these concepts using a numerical example.

The output of a GMTI tracker (such as the VS-IMM) over an extended period of time can be viewed as generating a string sequence (namely the mode sequence) that defines the trajectory. In previous work, it was demonstrated with real data that the target trajectory could be (probabilistically) parsed in real-time, assuming any string sequence output from the tracker could arise only from a stochastic context-free grammar (SCFG). In this paper, an GMTI data processing chain, with a view towards the application to syntactic parsing, is presented. An emphasis is placed on the Bayesian formualtions, which provides a unified description of the processing algorithms.

The Benes filtering problem has been shown to be related to the quantum mechanical simple harmonic oscillator. In a previous paper, the exact fundamental solution for the filtering problem was derived. The methods employed included the method of separation of variables for solving PDEs, results from Strum-Liouville theory, and properties of the Hermite special function. In this paper, the results are rederived more simply and directly using Feynman path integral methods. Numerical examples are included that demonstrate the correctness of formulas and their utility in solving continuous-discrete filtering problems with Benes drift and nonlinear measurement model.

DRDC has been involved in the development of airborne SAR systems since the 1980s. The current system, designated XWEAR (X-band Wideband Experimental Airborne Radar), is an instrument for the collection of SAR, GMTI and maritime surveillance data at long ranges. VideoSAR is a land imaging mode in which the radar is operated in the spotlight mode for an extended period of time. Radar data is collected persistently on a target of interest while the aircraft is either flying by or circling it. The time span for a single circular data collection can be on the order of 30 minutes. The spotlight data is processed using synthetic apertures of up to 60 seconds in duration, where consecutive apertures can be contiguous or overlapped. The imagery is formed using a back-projection algorithm to a common Cartesian grid. The DRDC VideoSAR mode noncoherently sums the images, either cumulatively, or via a sliding window of, for example, 5 images, to generate an imagery stream presenting the target reflectivity as a function of viewing angle. The image summation results in significant speckle reduction which provides for increased image contrast. The contrast increases rapidly over the first few summed images and continues to increase, but at a lesser rate, as more images are summed. In the case of cumulative summation of the imagery, the shadows quickly become filled in. In the case of a sliding window, the summation introduces a form of persistence into the VideoSAR output analogous to the persistence of analog displays from early radars.

The fundamental solution for the continuous-time filtering problems can be expressed in terms of Feynman path integrals. This enables one to view the solution of filtering problem in terms of an effective action that is a function of the signal and measurement models. The practical utility of the path integral formula is demonstrated via some nontrivial examples. Specifically, it is shown that the simplest approximation of the path integral formula for the fundamental solution of the Fokker-Planck-Kolmogorov forward equation (termed the Dirac-Feynman approximation) can be applied to solve nonlinear continuous-discrete filtering problems quite accurately using sparse grid filtering and Monte-Carlo approaches.

Recently, it has been shown that the continuous-discrete and continuous-continuous nonlinear filtering problems can be formulated and solved in terms of Feynman path integrals. A physical and conceptual explanation of the central results is presented. The major role played by such techniques in modern theoretical physics and pure mathematics is briefly reviewed. Several advantages of the proposed formulation (over other approaches in standard filtering theory literature) are discussed. Also clarified are the origin of some filtering theory results, such as the Yau algorithm for continuous-continuous filtering, and the relation between certain nonlinear filering systems and Euclidean quantum physics.

In certain operational radar modes, slow ground moving targets are detected over several processing intervals using space-time adaptive processing. This enables use of Bayesian filtering and smoothing algorithms for estimation of time-varying moving target parameters. In this paper, some Bayesian filtering algorithms are investigated. The Cram´er-Rao bounds based on subsets of radar measurements (range, angle and Doppler) are derived for typical maneuvering targets and compared against simulated results from Bayesian filters. The performance is also evaluated using real data obtained from DRDC Ottawa's XWEAR radar.

In this paper, several nonlinear filters (EKF/CMKF/CMEKF, UKF and PFs) are compared using real datasets and simulations based on two representative radar datasets. The first dataset was collected from an air traffic control (ATC) radar experiment with several aircraft. The second dataset was recorded from a high frequency surface wave radar (HFSWR) trial that was characterzed by a very long integration time and a limited set of manoeuvre types. RMSE, NEES and NIS are used as measures of performance. Comments on the performance, computational requirements of the nonlinear filters, practical modelling and filter tuning issues for the two types of radars are also presented.

The universal continuous-discrete tracking problem requires the solution of a Fokker-Planck-Kolmogorov forward equation (FPKfe) for an arbitrary initial condition. Using results from quantum mechanics, the exact fundamental solution for the FPKfe is derived for the state model of arbitrary dimension with Benes drift that requires only the computation of elementary transcendental functions and standard linear algebra techniques- no ordinary or partial differential equations need to be solved. The measurement process may be an arbitrary, discrete-time nonlinear stochastic process, and the time step size can be arbitrary. Numerical examples are included, demonstrating its utility in practical implementation.

Many radar tracking problems involve a subset of range, angle, and Doppler measurements. A naive grid-based computational approach is computationally unfeasible. In fact, even an approach based on the use of sparse tensors and a fixed, Cartesian grid is computationally very expensive. It is shown that an adaptive grid that is partly based on measurements reduces the computational load drastically so that the filtered solution is obtainable in real-time. A numerical example is included to demonstrate the reliable and highly accurate solutions obtainable using the proposed combination of measurement-based adaptive grid and sparse tensors.