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The articles in this special issue focus on the applications supported by quantum radar. QUANTUM physics has had a large impact on our everyday life. Relevant examples range from transistors and Monolithic microwave-integrated circuit, or MMIC,1 that make most of our technological society, as well as to nuclear energy, and to the science of materials. Some of these technologies have met with indisputable success while others have not proven to be useful to date. It should be remembered that the idea of a clean energy coming from nuclear fusion is an unfulfilled promise: such technologies have always involved a change of paradigm that made them quite different from known expectations. Note that Von Neumann machines, the foundations of our computers, are based on hardware made from quantum mechanics effects but they are otherwise easy to understand. A quantum computer is a rather different object and not so easy to comprehend unless an extensive knowledge of quantum physics is achieved. The same applies to the quantum radar (QR), the main topic of this special issue, which is quite different from a classical radar, both in terms of the working principles and the kind of hardware to realize it. The today’s radars are based on the propagation of electromagnetic waves of the classical (nonquantum) physics. The QR promises to be a radical change. It should be said that we are yet at a pioneering stage and we cannot be certain yet if a goal will be achieved nor in what form we will see it, though, R&D is progressing.

The detection of objects in the presence of significant background noise is a problem of fundamental interest in sensing. In this article, we theoretically analyze a prototype target detection protocol, the quantum temporal correlation (QTC) detection protocol, which is implemented in this article utilizing spontaneous parametric down-converted photon-pair sources. The QTC-detection protocol only requires time-resolved photon-counting detection, which is phase-insensitive and therefore suitable for optical target detection. As a comparison to the QTC-detection protocol, we also consider a classical phase-insensitive target detection protocol based on intensity detection that is practical in the optical regime. We formulated the target detection problem as a total probe photon transmission estimation problem and obtain an analytical expression of the receiver operating characteristic (ROC) curves. We carry out experiments using a semiconductor waveguide source, which we developed and previously reported. The experimental results agree very well with the theoretical prediction. In particular, we find that in a high-level environment noise and loss, the QTC-detection protocol can achieve performance comparable to that of the classical protocol (that is practical in the optical regime) but with $\simeq\!\text{57}$ times lower detection time in terms of ROC curve metric. The performance of the QTC-detection protocol experiment setup could be further improved with a higher transmission of the reference photon and better detector time uncertainty. Furthermore, the probe photons in the QTC-detection protocol are completely indistinguishable from the background noise and therefore useful for covert ranging applications. Finally, our technological platform is highly scalable as well as tunable and thus amenable to large scale integration, which is necessary for practical applications.

For an airborne sensor, there is a pressing need to be able to detect/track submerged submarines, shipwrecks, sea mines, unexploded explosive ordnance, and buried drums during maritime surveillance. Traditional usage is the magnetic anomaly detection (MAD), where the small changes in the earth's magnetic field caused by the ferrous components of the targets are measured. The primary means of long-range detection and classification of targets are with passive and active acoustic sensors, and MAD is used for accurate final localization. MAD could also be used for land-based targets but this is not common. Knowing the relationship between the magnetic signature and the kinematic parameters, the tracking problem can be formulated under a Bayesian framework. In this article, multiple nonlinear filters are used for a real single surface-target tracking problem in maritime surveillance using an airborne total-field sensor. The posterior Cramer–Rao lower bound for MAD is derived. Given the total-field measurements, these filters can estimate the kinematic states as well as the permanent moments and induced moments effectively. Results demonstrate the effectiveness of the proposed nonlinear filters as well as the impact of using MAD as part of airborne surveillance.

Geo-registration and geo-location of data collected by video sensors such as electro-optical and infrared cameras are two fundamental steps in the airborne surveillance of ground targets. With the availability of high-resolution imaging sensors and detailed mapping or terrain data sources, video data plays an increasingly important role in modern surveillance platforms like unmanned aerial vehicles and airborne, ground, or maritime surveillance systems. Surveillance systems without any compensation for the inevitable sensor registration errors, i.e., biases, may make geo-location erroneous and render the surveillance platform less effective for precision targeting. This article deals with the modeling of sensor biases in geo-location and proposes a method to estimate them. The proposed method leads to a minimization problem with a nonlinear cost function. Detailed derivation of the bias model is given along with an algorithm to find the bias parameters. The achievable lower bounds for debiased geo-location are provided and simulations are used to demonstrate the validity of the proposed method.

Many quantum radars currently studied in the literature use a phenomenon called entanglement to address the problem of distinguishing signal from noise, which is one of the most important problems faced by any radar. Until recently, entanglement-based quantum radars at radio frequencies existed only in theory; their practicality was very much in doubt. The situation has changed with a recent experimental implementation of all the necessary components of a quantum two-mode squeezing (QTMS) radar, which operates at microwave frequencies and can be described as a quantum range finder. In this article, we lay out the exact problem solved by this prototype, namely quantum noise, and explain how entanglement can overcome the existence of this noise. By analyzing the QTMS radar prototype, we point out a technological route to an entanglement-based quantum radar that can, in principle, perform all tasks that radars can and must do, such as array processing, clutter suppression, and image processing (including synthetic aperture radar and inverse synthetic aperture radar).

Recently, the authors have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. This radar, which they call a quantum two-mode squeezing radar (QTMS radar), generates a pair of entangled microwave signals and transmits one of them through free space, using the other signal as a reference to perform matched filtering. The specific type of entanglement is called a two-mode squeezed vacuum, a type of continuous-variable entanglement between two frequencies. Motivated by the success of these experiments, they try to better understand the entangled QTMS radar signals in this study. They do so by comparing it to a simpler, more conventional radar system, which they call a two-mode noise radar (TMN radar). They also show how both types of radars are related to standard noise radars as described in the literature. They find that the signals for QTMS radar signals and TMN radar signals have the same mathematical form and that they are related to noise radar by a simple mathematical transformation. This shows that QTMS radar signals can be emulated by a fictional, idealised TMN radar and that it is possible to apply results from the noise radar literature to QTMS radar.

We evaluate the feasibility of a quantum monopulse radar, focusing on quantum illumination (QI) radars and quantum two-mode squeezing (QTMS) radars. Based on their similarity with noise radar, for which monopulse operation is known to be possible, we find that QTMS radars can be adapted into monopulse radars, but QI radars cannot. We conclude that quantum monopulse radars are feasible.

The most important task of any radar is to detect targets. From this perspective, they are machines for distinguishing between two hypotheses: target absent and target present. The test statistic—or detector function—used by the radar is clearly of primary importance. In this paper, we explore the properties of a detector function for quantum two-mode squeezing radar and noise radar. This detector is derived from a second-order approximation of the likelihood ratio, and is attractive because it has a simple mathematical form. In order to analyze the performance of this detector function, we derive an expression for the receiver operating characteristic curve and verify it via simulations.

Reports on the IQC Workshop on Quantum Illumination: From Theory to Practice. The workshop was held 3–4 December 2019 at the Institute of Quantum Computing (IQC), University of Waterloo, a well-known research center for quantum information as well as home to the world’s first microwave quantum radar experiment that was published in the literature.

We analyze the potential application of a generalized likelihood ratio (GLR)-based detector function to quantum two-mode squeezing (QTMS) radars and standard noise radars. We give an expression for the likelihood ratio (LR) in terms of the maximum-likelihood estimate of the correlation coefficient between the received and reference signals of the radar. Interestingly, we found that a previously-studied detector function outperforms the GLR detector, though not in all parameter regimes. This runs counter to the intuition, based on the Neyman-Pearson lemma, that the LR test is optimal. We discuss why the lemma does not hold in this particular case and why the search for detector functions for QTMS radars and noise radars remains open. However, the GLR detector is a good choice when the correlation coefficient is high, the number of integrated samples is low, and appropriate computational resources are available.

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.

Micro-Doppler methods of detecting and classifying small UAVs are limited in range due to the weak radar returns from their plastic propellers. Smaller windows of data instead of longer windows are used for detection as stationarity assumptions often fail for longer windows. Traditional non-parametric methods may be inadequate as they have limited spectral resolution with smaller windows and may provide false detection when radar returns are weak. A rotary drone detector using the number of Helicopter Rotation Modulation (HERM) lines is considered in this paper. Two parametric methods for estimating the number of HERM lines, Minimum Description Length (MDL) and Akaike Information Criterion (AIC), are considered for detection purposes. Experiments using real data acquired using a micro-helicopter drone and a commercial ultra-wide band radar reveal that MDL performs significantly better than AIC and the traditional Fourier-based non-parametric estimation methods.

Broadly speaking, in quantum illumination we can say that a proposed protocol has a “quantum advantage” if it outperforms all possible classical protocols. In the optical domain of LIDAR, this is the most useful metric as lasers can routinely produce nearly ideal classical states of light at room temperature (RT). This is not the case in the microwave domain of RADAR where the photon energy is much less than the 300K thermal energy, meaning that a real RT microwave source will always be contaminated by significant thermal noise. Thus, it is not clear if it is technologically possible to produce an ideal classical microwave signal at RT. It is therefore interesting to ask if a microwave quantum illumination protocol can have a “practical advantage” compared to the best technologically feasible RT microwave source. In this paper, we look to frame this question more precisely. As a concrete example, we present experimental results showing that, contrary to recent claims in the literature [1], an entangled microwave source amplified by a cryogenic HEMT amplifier fails to obtain any performance advantage over a simply constructed RT source and, in facts, performs significantly worse. We present a simple theory which explains the experimental results and which offers guidance on how a practical advantage might be achieved.

A detector function for a newly proposed quantum radar known as quantum two-mode squeezing (QTMS) radar is analyzed in this paper. The detector function proposed in this paper behaves like a normal distribution function when the number of radar samples integrated for the detection is over 500 samples. A closed form expression for the receiver operating characteristic (ROC) curve for QTMS radar is derived under the assumption that the radar integrates more than 500 samples to detect the presence or absence of a target. The detector function can also be applied to noise radar.

The detection and parametric estimation of low-SNR radar signals, particularly linear frequency modulated (LFM) radar signals, is a problem of considerable interest. In prior work, this problem has been investigated using various signal processing techniques, such as maximum likelihood estimation, fractional Fourier transform and Wigner-Ville-based methods, to analyze the signal parameters of a complex linear frequency modulated signal. Other work has focused on applying deep learning to automatically recognize various radar waveform types and their features, such as linear frequency modulation (LFM), Barker code and rectangular waveforms. In this paper, we investigate this problem from a machine learning perspective for multiple LFM radar signals given a priori information. We explore the use of naive Bayes, support vector machine and neural network classifiers to identify the LFM chirp rate, out of a set of known chirp rates, from a specific radar emitter under varying SNR conditions. Simulation results demonstrate the viability of this technique to identify the radar LFM mode in very low signal-to-noise ratio conditions down to -20 dB where using existing approaches (e.g., Wigner-Ville) fail.

The detection of objects under large background conditions is a problem of fundamental interest in sensing. In this talk we theoretically analyze a prototype target detection protocol, the quantum time correlated (QTC) detection protocol, with spontaneous parametric down-converted photon-pair sources. The QTC detection protocol only requires time-resolved photon counting detection, which is phase-insensitive and therefore suitable for optical target detection. As a comparison to the QTC detection protocol we also consider a classical phase-insensitive target detection protocol based on intensity detection. We formulated the target detection problem as a total probe photon transmission estimation problem. We carry out experiments using a semiconductor waveguide source. The experimental results agree very well with the theoretical prediction. In particular, we find that in a high-level environment noise and loss, the QTC detection protocol is able to achieve comparable to the classical protocol target detection performance but with 10–100 fold lower required time on target detection in terms of ROC curve metric. The performance of the QTC detection experiment setup could be further improved with a higher transmission of the reference photon and better detector time uncertainty. Furthermore, unlike classical target detection and ranging protocol, the probe photons in our QTC detection protocol are completely indistinguishable from the background noise and therefore useful for covert ranging applications. Finally, our technological platform is highly scalable and tunable and thus amenable to large scale integration necessary for practical applications.

We analyze the signal-to-noise ratio (SNR) metric in the context of quantum two-mode squeezing (QTMS) radar and find that there are actually two SNRs associated with a QTMS radar, one for the received signal and another for a signal retained inside the radar. Definitions for these SNRs are proposed which are simpler than those hitherto used in the quantum radar literature. We plot receiver operating characteristic (ROC) curves for varying values of these two SNRs. These plots show that the quality of the matched filtering performed by the radar, as quantified by the SNR of the retained signal, can have a strong impact on detection performance.

Most research on drone detection and classification focus on using features from micro-Doppler signatures with blade flashes. However, these methods are limited in range and require radars with high pulse repetition frequency (PRF)–at least twice the maximum tip velocity. A different method to detect and classify drones at longer ranges using a low PRF radar is desired. In the literature, the cepstrum method was shown to be able to estimate the rotation rate when the PRF is insufficient. An alternative way of analyzing micro-Doppler is by using a long windowed Short-time Fourier transform (STFT) to generate HElicopter Rotation Modulation (HERM) lines. HERM lines exhibit similar behavior to a cepstrogram, with spectral lines separated in frequency by a value related to the rotation rate. In this paper, the separation frequency of HERM lines was estimated using a log harmonic summation algorithm. The proposed algorithm was tested on a simple HERM line model and also on real data obtained from two blade single rotor micro-helicopter drone. The algorithm was shown to be more resilient than cepstrum under Gaussian noise.

The estimation of interference plus noise covariance (INC) matrix for beamforming applications is considered from a Riemannian space perspective. A new INC estimation technique based on regularized Burg algorithm (RBA), Riemannian mean and Riemannian distance is proposed to maintain a stable performance in presence of angle of arrival mismatch and small sample size with high and low signal to interference plus noise ratio (SINR). The RBA is exploited to generate Toeplitz Hermitian positive definite (THPD) covariance matrices from the estimates of the reflection coefficients for each radar snapshot. The estimated INC is formulated as a linear combination of THPD covariance matrices of the interference plus noise excluding potential target snapshots. The weights of the linear combination operation are based on the Riemannian distance between the Riemannian mean and each THPD covariance matrix. The largest distance (potential target) will have zero weight and the smallest distance will have maximum weight. Simulation results demonstrate the performance of the proposed technique in comparison with sample covariance and Riemannian mean covariance under steering vector mismatch and small sample size in presence of high and low SINR.

In this article, we modify Mumford–Shah level-set model to handle speckles and blur in synthetic aperture radar (SAR) imagery. The proposed model is formulated using a non-local regularization framework. Hence, the model duly cares about local gradient oscillations (corresponding to the fine details/textures) during the evolution process. It is assumed that the speckle intensity is gamma distributed, while designing a maximum a posteriori estimator of the functional. The parameters of the gamma distribution (i.e. scale and shape) are estimated using a maximum likelihood estimator. The regularization parameter of the model is evaluated adaptively using these (estimated) parameters at each iteration. The split-Bregman iterative scheme is employed to improve the convergence rate of the model. The proposed and the state-of-the-art despeckling models are experimentally verified and compared using a large number of speckled and blurred SAR images. Statistical quantifiers are used to numerically evaluate the performance of various models under consideration.

For target detection, quantum two-mode squeezing (QTMS) radars and noise radars require estimation of a covariance matrix. A scalar function of the covariance matrix is then used for deciding the presence or absence of a target. In this paper, estimation of the covariance matrix is carried out by minimizing the Frobenius norm between the sample covariance matrix and the theoretically expected form of the matrix. Two scalars, a normalized correlation coefficient and an unnormalized cross-correlation, are used for detecting the target. Their performances are compared and found to be the same. Probability distributions are then fit to the histograms of these estimated correlations. Using these fitted distributions, expressions are obtained for receiver operating characteristic (ROC) curves that predict the performance of these functions in the task of deciding whether a target is present or not. This work is a first step toward understanding and modeling target detection performance of a QTMS radar or noise radar when using correlation-related measures as detector functions.

Recently, a quantum-enhanced radar transmitter operating at microwave frequencies, called a quantum two-mode squeezing radar (QTMS radar), was demonstrated in the laboratory. Inspired by this, we discuss the possibility of building an array of quantum radars. In order for quantum radars to be practically relevant, it is important that an array can be built. We find that it is indeed possible to build such an array, but that the details matter. A quantum illumination radar array may not be very effective, but a QTMS radar array is quite amenable to array signal processing. We also briefly discuss the practical aspects of building a quantum radar array.

Detection of objects from a moving platform is a challenging problem. When detection of ships is attempted with data acquired from electro-optical (EO) and infrared (IR) sensors on an airborne platform, state-of-the-art algorithms fail. This paper provides a solution for detection of ships from EO/IR video dataset obtained from an airborne platform. A recently proposed statistical thresholding method called N-Modal Discrete (NMD) method that was successfully applied to synthetic aperture radar (SAR) datasets, is modified and applied to the EO/IR video datasets of interest. Results are presented to demonstrate the ability of the proposed method to detect objects in a single-frame.

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.

The problem of direction of arrival (DOA) estimation is considered from a geometric point of view. In particular, a new Riemannian geometry direction of arrival (RGDOA) estimation technique based on regularized Burg algorithm (RBA) and Riemannian mean and distance is proposed to maintain robust estimation under low signal-to-noise ratio (SNR) and small sample size. The RBA is exploited on generated Toeplitz Hermitian positive definite (THPD) covariance matrices from the estimates of the reflection coefficients for each radar snapshot. In addition, the Karcher Barycenter is used to calculate the Riemannian mean of THPD covariance matrices. The RGDOA technique is formulated as an optimization problem by minimizing the Riemannian distance between the Riemannian mean and the steering vector Hermitian positive definite covariance matrix. Simulation results indicate the robustness of the RGDOA technique in comparison with MUSIC and MVDR estimation techniques under low SNR and small sample size.

We have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. Because the signal generation process relies on quantum mechanical principles, the system is considered to contain a quantum-enhanced radar transmitter. This transmitter generates a pair of entangled microwave signals and transmits one of them through free space, where the one-way signal is measured using a simple and rudimentary receiver. The type of entanglement used is called two-mode squeezed vacuum (TMSV), so we may call our radar a quantum two-mode squeezing radar (QTMS radar). At the heart of the transmitter is a device called a Josephson parametric amplifier (JPA), which generates the entangled microwave beams; these are then sent through a chain of amplifiers. One beam passes through 0.5 m of free space; the other is measured directly. The two measurement results are correlated in order to distinguish noise from signal. We compare this quantum-enhanced transmitter to a similar one using only conventional components, and find that there is a significant gain when the two systems broadcast signals at approximately the same power.

Compressive sensing (CS) is a signal processing technique for acquiring sparse signals at sampling rates much lower than the Nyquist rate. Traditionally to avoid generation of large sensing matrices for 2D signals or images, individual rows or columns of the images are compressed in the sensing phase. This increases the number of matrix multiplication operations and results in a compressed image with a different aspect ratio than the original uncompressed image. To overcome these issues, in this paper, we investigate a 2D deterministic sensing technique that maintains both the aspect ratio and the morphology of the image. We use a linear filtering-based measurement matrix. Through this paper, we demonstrate that deterministic CS will preserve the features and there by enable analysis of the images such as detection and identification of objects in the compressed domain without the need to perform a computationally expensive reconstruction. In order to demonstrate this, images obtained by infra-red electro-optic camera on an airborne platform (low resolution), LandSat (medium resolution) and multispectral images (high resolutions) are chosen. Features of chosen objects from an uncompressed image are compared with those corresponding objects in the compressed image using template matching to demonstrate that such image analysis can be done in the compressed domain. Frobenius norm-based structural similarity analysis for the images at different levels of compression is presented to demonstrate the similarity in structure. Robustness of the deterministic CS technique is shown by performing template matching based image analysis on noisy compressed images.

Supplemental document

In this paper, the joint multitarget (JoM) estimator proposed for the joint target detection and tracking (JoTT) filter is reformulated for the Gaussian mixture (GM) implementations of the multitarget multi-Bernoulli (MeMBer) filters. For this purpose, a mode-finding algorithm is employed to search for the most significant mode of a GM density. Thus, the maximum a posterior (MAP) estimates of Bernoulli targets are determined. In addition, the multi-Bernoulli versions of the two conflicting objective functions for the Pareto-optimal value of the unknown JoM estimation constant are derived. Simulations compare the performance of the proposed JoM estimator with that of the marginal multitarget (MaM) estimator in a multitarget tracking scenario, where the probability of target detection is a function of target states. The simulation results demonstrate that the proposed JoM estimator outperforms the MaM estimator under moderately low-observable conditions. This is because the incomplete cost function of the MaM estimator is not adequate to obtain accurate cardinality estimates of targets without considering how well targets are localized. Nevertheless, the proposed JoM estimator may suffer from track termination latency more than the MaM estimator due to the definition of its cost function.

The cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter removes the positive bias from the data-updated cardinality estimate in the multi-target multi-Bernoulli (MeMBer) filter. In this study, the relationship between the MeMBer corrector and the multi-Bernoulli random finite set (RFS) distribution is analysed. By utilising this relationship, a filter that offers a new statistical framework for the MeMBer data update process is proposed. Thus, the multi-Bernoulli RFS distribution is extended to model spurious targets arising from targets under the legacy track set with high probabilities of existence. Unlike the CBMeMBer filter, the proposed filter removes the bias observed in the MeMBer filter by distinguishing spurious targets from actual targets, and while doing this, it does not make any limiting assumption on the probability of target detection. In addition, the modelling of spurious targets allows the refinement of the existence probabilities of targets in light of measurements. As a result, the stability of the cardinality estimate is improved while removing the bias. The theoretical analysis performed on the joint detection and state estimation problem of a single target reveals the strengths and limitations of the proposed filter. In addition, numerical simulations are performed in a scenario involving targets with crossing trajectories to demonstrate the filter performance.

Detecting and identifying drones is of great interest due to the proliferation of highly manoeuverable drones with on-board sensors of increasing sensing capabilities. In this paper, we investigate the use of radars for tackling this problem. In particular, we focus on the problem of detecting rotary drones and distinguishing between single-propeller and multi-propeller drones using a micro-Doppler analysis. Two different radars were used, an ultra wideband (UWB) continuous wave (CW) C-band radar and an automotive frequency modulated continuous wave (FMCW) W-band radar, to collect micro-Doppler signatures of the drones. By taking a closer look at HElicopter Rotor Modulation (HERM) lines, the spool and chopping lines are identified for the first time in the context of drones to determine the number of propeller blades. Furthermore, a new multi-frequency analysis method using HERM lines is developed, which allows the detection of propeller rotation rates (spool and chopping frequencies) of single and multi-propeller drones. Therefore, the presented method is a promising technique to aid in the classification of drones.

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.

With the rise of inexpensive, commercially available UAVs (drones) it has become possible to collect data from multiple UAVs equipped with optical sensors. This possibility has enabled tracking and data fusion with multiple airborne platforms. The addition of multiple airborne sensors allows for more robust tracking that is less susceptible to clutter and track proliferation. This paper demonstrates the air-to-ground tracking capabilities of two airborne sensors following a moving ground target using the centralized fusion Extended Kalman Filter and Probabilistic Data Association Filter implemented in the Python library pystemlib. The result of adding multiple airborne sensors is a reduced state estimation error and more robust target state predictions evidenced by a reduced root-mean-square error and smaller area of probabilities. A validation of this approach is demonstrated with real data.

Quantum illumination is an idea for detection of targets in the presence of a noisy background using a photon pair that is entangled in some degree of freedom, such as frequency. This paper provides a simple and illustrative proof-of-concept setup of a quantum illumination investigation. Detection and imaging is investigated in the optical regime using a correlated source and the performance of classical illumination and quantum illumination are compared for detection and imaging experiments. The quantum advantage is demonstrated in the acquired data and possible application spaces and technological requirements are identified for developing practical systems.

Quantum radar has been proposed in the quantum information science literature as a novel approach to sensing that can offer a substantial gain over existing, classical sensors. In particular, a quantum radar based on quantum entanglement, termed quantum illumination radar, has been theoretically shown to demonstrate superiority over the optimal classical sensor, and there have been some experimental validation. However, all of the existing literature in the subject is in language, terminology and metrics that are completely unfamiliar (or relevant) to classical radar experts, and as a result there is understandable skepticism among the radar experts. This paper attempts to bridge the large gap between practical radar scientists and quantum information science (QIS) experts working in the field. We review the theoretical state-of-the-art in field and examine some of the claims in the literature in terms of metrics of relevance to radar detection, tracking and sensor fusion. We also discuss some possible technological routes to building such a radar across the electromagnetic spectrum.

We describe an experiment which comes very close to implementing a quantum radar based on entangled microwaves. A pair of entangled microwave beams are generated using a Josephson parametric amplifier (JPA) and measured by a pair of digitizers after amplification. We present a receiver operating characteristic (ROC) curve for the case where the signal power at the digitizers is −83.84 dBm (corresponding to a JPA power output, before amplification, of −148.26 dBm), as well as for a classically correlated source which attempts to approximate the covariance structure of the entangled JPA signals. The entangled signals are seen to be clearly superior to the classically correlated signals. Although the latter is still interesting from a noise radar perspective, this demonstrates that attempting to simulate entanglement with classical signals causes a measurable decrease in correlation. Our experiment validates the feasibility and desirability of a microwave quantum radar.

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.