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A joint design of both sensing and communication can lead to substantial enhancement for both subsystems in terms of size, cost as well as spectrum and hardware efficiency. In the last decade, integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize the spectrum on a single and shared hardware platform. Recent studies focused on developing multi-function approaches to share the spectrum between radar sensing and communications. Index modulation (IM) is one particular approach to incorporate information-bearing communication symbols into the emitted radar waveforms. While IM has been well investigated in communications-only systems, the implementation adoption of IM concept in ISAC has recently attracted researchers to achieve improved energy/spectral efficiency while maintaining satisfactory radar sensing performance. This article focuses on recent studies on IM-ISAC, and presents in detail the analytical background and relevance of the major IM-ISAC applications., Comment: Accepted Paper in IEEE Signal Processing Magazine, 12pages5figures

The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR). We develop a design approach based on supervised neural network where class labels are generated using an efficient sparse beamformer spectral analysis (SBSA) approach. SBSA uses explicit information of the unknown narrowband interference environment for training the network and bears close performance to training using enumerations, i.e., exhaustive search which is computationally prohibitive for large arrays. The employed DNN effectively approximates the unknown mapping from the input received data spatial correlations to the output of sparse configuration with effective interference mitigation capability. The problem is posed as a multi-label classification problem where the selected antenna locations achieving MaxSINR are indicated by the output layer of DNN. In addition to evaluating the performance of the DNN in terms of the classification accuracy, we evaluate the performance in terms of the the ability of the classified sparse array to mitigate interference and maximize signal power. It is shown that even in the case of miss-classification, where at least one sensor location doesn't match the optimal locations, the DNN effectively learns the sub-optimal sparse configuration which has desirable SINR characteristics. This shows the ability of the DNN to learn the proposed optimization algorithms, hence paving the way for efficient real-time implementation.

Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment. Beamforming performance of MIMO radar is guided by both beamforming weight coefficients and the transceiver configuration. We propose a cognitive-driven MIMO array design where both the beamforming weights and the transceiver configuration are adaptively and concurrently optimized under different environmental conditions. The perception-action cycle involves data collection of full virtual array, covariance reconstruction and joint design of the transmit and receive arrays by antenna selection.The optimal transceiver array design is realized by promoting two-dimensional group sparsity via iteratively minimizing reweighted mixed L21-norm, with constraints imposed on transceiver antenna spacing for proper transmit/receive isolation. Simulations are provided to demonstrate the "perception-action" capability of the proposed cognitive sparse MIMO array in achieving enhanced beamforming and anti-jamming in dynamic target and interference environment.

Sparse array design aided by emerging fast sensor switching technologies can lower the overall system overhead by reducing the number of expensive transceiver chains. In this paper, we examine the active sparse array design enabling the maximum signal to interference plus noise ratio (MaxSINR) beamforming at the MIMO radar receiver. The proposed approach entails an entwined design, i.e., jointly selecting the optimum transmit and receive sensor locations for accomplishing MaxSINR receive beamforming. Specifically, we consider a co-located multiple-input multiple-output (MIMO) radar platform with orthogonal transmitted waveforms, and examine antenna selections at the transmit and receive arrays. The optimum active sparse array transceiver design problem is formulated as successive convex approximation (SCA) alongside the two-dimensional group sparsity promoting regularization. Several examples are provided to demonstrate the effectiveness of the proposed approach in utilizing the given transmit/receive array aperture and degrees of freedom for achieving MaxSINR beamforming.

Multiple-input multiple-output (MIMO) array based millimeter-wave (MMW) imaging has a tangible prospect in applications of concealed weapons detection. A near-field imaging algorithm based on wavenumber domain processing is proposed for a cylindrical MIMO array scheme with uniformly spaced transmit and receive antennas over both the vertical and horizontal-arc directions. The spectrum aliasing associated with the proposed MIMO array is analyzed through a zero-filling discrete-time Fourier transform. The analysis shows that an undersampled array can be used in recovering the MMW image by a wavenumber domain algorithm. The requirements for the antenna inter-element spacing of the MIMO array are delineated. Numerical simulations as well as comparisons with the backprojection (BP) algorithm are provided to demonstrate the effectiveness of the proposed method., Comment: 10 pages, 20 figures, paper submitted to IEEE Transactions on Aerospace and Electronic Systems

Radar for indoor monitoring is an emerging area of research and development, covering and supporting different health and wellbeing applications of smart homes, assisted living, and medical diagnosis. We report on a successful RF sensing system for home monitoring applications. The system recognizes Activities of Daily Living(ADL) and detects unique motion characteristics, using data processing and training algorithms. We also examine the challenges of continuously monitoring various human activities which can be categorized into translation motions (active mode) and in-place motions (resting mode). We use the range-map, offered by a range-Doppler radar, to obtain the transition time between these two categories, characterized by changing and constant range values, respectively. This is achieved using the Radon transform that identifies straight lines of different slopes in the range-map image. Over the in-place motion time intervals, where activities have insignificant or negligible range swath, power threshold of the radar return micro-Doppler signatures,which is employed to define the time-spans of individual activities with insignificant or negligible range swath. Finding both the transition times and the time-spans of the different motions leads to improved classifications, as it avoids decisions rendered over time windows covering mixed activities., Comment: 12 pages

Deep neural networks (DNNs) have recently received vast attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However,acquiring a sufficiently large training dataset remains a daunting task due to the high human costs and resources required for radar data collection. In this paper, an extended approach to adversarial learning is proposed for generation of synthetic radar micro-Doppler signatures that are well-adapted to different environments. The synthetic data is evaluated using visual interpretation, analysis of kinematic consistency, data diversity, dimensions of the latent space, and saliency maps. A principle-component analysis (PCA) based kinematic-sifting algorithm is introduced to ensure that synthetic signatures are consistent with physically possible human motions. The synthetic dataset is used to train a 19-layer deep convolutional neural network (DCNN) to classify micro-Doppler signatures acquired from an environment different from that of the dataset supplied to the adversarial network. An overall accuracy 93% is achieved on a dataset that contains multiple aspect angles (0 deg., 30 deg., and 45 deg. as well as 60 deg.), with 9% improvement as a result of kinematic sifting.

Hand and arm gesture recognition using the radio frequency (RF) sensing modality proves valuable in manmachine interface and smart environment. In this paper, we use curve matching techniques for measuring the similarity of the maximum instantaneous Doppler frequencies corresponding to different arm gestures. In particular, we apply both Frechet and dynamic time warping (DTW) distances that, unlike the Euclidean (L2) and Manhattan (L1) distances, take into account both the location and the order of the points for rendering two curves similar or dissimilar. It is shown that improved arm gesture classification can be achieved by using the DTW method, in lieu of L2 and L1 distances, under the nearest neighbor (NN) classifier., Comment: 6 pages, 7 figures, 2020 IEEE radar conference. arXiv admin note: substantial text overlap with arXiv:1910.11176

We perform classification of activities of daily living (ADL) using a Frequency-Modulated Continuous Waveform (FMCW) radar. In particular, we consider contiguous motions that are inseparable in time. Both the micro-Doppler signature and range-map are used to determine transitions from translation (walking) to in-place motions and vice versa, as well as to provide motion onset and the offset times. The possible classes of activities post and prior to the translation motion can be separately handled by forward and background classifiers. The paper describes ADL in terms of states and transitioning actions, and sets a framework to deal with separable and inseparable contiguous motions. It is shown that considering only the physically possible classes of motions stemming from the current motion state improves classification rates compared to incorporating all ADL for any given time.

We consider sparse array beamfomer design achieving maximum signal-to interference plus noise ratio (MaxSINR). Both array configuration and weights are attuned to the changing sensing environment. This is accomplished by simultaneously switching among antenna positions and adjusting the corresponding weights. The sparse array optimization design requires estimating the data autocorrelations at all spatial lags across the array aperture. Towards this end, we adopt low rank matrix completion under the semidefinite Toeplitz constraint for interpolating those autocorrelation values corresponding to the missing lags. We compare the performance of matrix completion approach with that of the fully augmentable sparse array design acting on the same objective function. The optimization tool employed is the regularized $l_1$-norm successive convex approximation (SCA). Design examples with simulated data are presented using different operating scenarios, along with performance comparisons among various configurations.

The paper considers sparse array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR) for both single point source and multiple point sources, operating in an interference active environment. Unlike existing sparse design methods which either deal with structured environment-independent or non-structured environment-dependent arrays, our method is a hybrid approach and seeks a full augumentable array that optimizes beamformer performance. This approach proves important for limited aperture that constrains the number of possible uniform grid points for sensor placements. The problem is formulated as quadratically constraint quadratic program (QCQP), with the cost function penalized with weighted l_1-norm squared of the beamformer weight vector. Simulation results are presented to show the effectiveness of the proposed algorithms for array configurability in the case of both single and general rank signal correlation matrices. Performance comparisons among the proposed sparse array, the commonly used uniform arrays, arrays obtained by other design methods, and arrays designed without the augmentability constraint are provided.

Doppler radar systems enable unobtrusive and privacy-preserving long-term monitoring of human motions indoors. In particular, a person's gait can provide important information about their state of health. Utilizing micro-Doppler signatures, we show that radar is capable of detecting small differences between the step motions of the two legs, which results in asymmetric gait. Image-based and physical features are extracted from the radar return signals of several individuals, including four persons with different diagnosed gait disorders. It is shown that gait asymmetry is correctly detected with high probability, irrespective of the underlying pathology, for at least one motion direction., Comment: 6 pages, 5 figures, 4 tables; accepted at the IEEE Radar Conference 2019, Boston, MA, USA

Sparse arrays are popular for performance optimization while keeping the hardware and computational costs down. In this paper, we consider sparse arrays design method for wideband source operating in a wideband jamming environment. Maximizing the signal-to-interference plus noise ratio (MaxSINR) is adopted as an optimization objective for wideband beamforming. Sparse array design problem is formulated in the DFT domain to process the source as parallel narrowband sources. The problem is formulated as quadratically constraint quadratic program (QCQP) alongside the weighted mixed $l_{1-\infty}$-norm squared penalization of the beamformer weight vector. The semidefinite relaxation (SDR) of QCQP promotes sparse solutions by iteratively re-weighting beamformer based on previous iteration. It is shown that the DFT approach reduces the computational cost considerably as compared to the delay line approach, while efficiently utilizing the degrees of freedom to harness the maximum output SINR offered by the given array aperture.

We introduce a simple but effective technique in automatic hand gesture recognition using radar. The proposed technique classifies hand gestures based on the envelopes of their micro-Doppler signatures. These envelopes capture the distinctions among different hand movements and their corresponding positive and negative Doppler frequencies which are generated during each gesture act. We detect the positive and negative envelopes separately, and form a feature vector of their augmentation. We use the $k$-nearest neighbor ($k$NN) classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance (L2), so as not to diminish small but critical envelope values. It is shown that this method outperforms both low-dimension representation techniques based on principal component analysis (PCA) and sparse reconstruction using Gaussian-windowed Fourier dictionary, and can achieve very high classification rates., Comment: 6 pages, 7 figures, 2019 radar conference

Recently, deep neural networks (DNNs) have been the subject of intense research for the classification of radio frequency (RF) signals, such as synthetic aperture radar (SAR) imagery or micro-Doppler signatures. However, a fundamental challenge is the typically small amount of data available due to the high costs and resources required for measurements. Small datasets limit the depth of DNNs implementable, and limit performance. In this work, a novel method for generating diversified radar micro-Doppler signatures using Kinect-based motion capture simulations is proposed as a training database for transfer learning with DNNs. In particular, it is shown that together with residual learning, the proposed DivNet approach allows for the construction of deeper neural networks and offers improved performance in comparison to transfer learning from optical imagery. Furthermore, it is shown that initializing the network using diversified synthetic micro-Doppler signatures enables not only robust performance for previously unseen target profiles, but also class generalization. Results are presented for 7-class and 11-class human activity recognition scenarios using a 4-GHz continuous wave (CW) software-defined radar., Comment: Accepted November 2018 to IEEE Transactions on Aerospace and Electronics Engineering

Objective: In this paper, we demonstrate the applicability of radar for gait classification with application to home security, medical diagnosis, rehabilitation and assisted living. Aiming at identifying changes in gait patterns based on radar micro-Doppler signatures, this work is concerned with solving the intra motion category classification problem of gait recognition. Methods: New gait classification approaches utilizing physical features, subspace features and sum-of-harmonics modeling are presented and their performances are evaluated using experimental K-band radar data of four test subjects. Five different gait classes are considered for each person, including normal, pathological and assisted walks. Results: The proposed approaches are shown to outperform existing methods for radar-based gait recognition which utilize physical features from the cadence-velocity data representation domain as in this paper. The analyzed gait classes are correctly identified with an average accuracy of 93.8%, where a classification rate of 98.5% is achieved for a single gait class. When applied to new data of another individual a classification accuracy on the order of 80% can be expected. Conclusion: Radar micro-Doppler signatures and their Fourier transforms are well suited to capture changes in gait. Five different walking styles are recognized with high accuracy. Significance: Radar-based sensing of human gait is an emerging technology with multi-faceted applications in security and health care industries. We show that radar, as a contact-less sensing technology, can supplement existing gait diagnostic tools with respect to long-term monitoring and reproducibility of the examinations., Comment: 11 pages, 6 figures

In a unified system of passive radar and communication systems of joint transmitter platform, information intended for a communication receiver may be eavesdropped by a passive radar receiver (RR), thereby undermining the security of communications system. To minimize this information security risk, in this paper, we propose a unified passive radar and communications system wherein the signal-to-interference and noise ratio (SINR) at the RR is maximized while ensuring that the information secrecy rate is above a certain threshold value. We consider both scenarios wherein transmissions of the radar waveform and information signals are scheduled with the disjoint (non-overlapping case) as well as with the same set of resources (overlapping case). In both cases, the underlying optimization problems are non-convex. In the former case, we propose alternating optimization (AO) techniques that employ semidefinite programming and computationally efficient semi-analytical approaches. In the latter case, AO method based on semi-definite relaxation approach is proposed to solve the optimization problem. By changing the threshold value of the information secrecy rate, we then characterize the performance tradeoff between passive radar and communication systems with the boundaries of the SINR-secrecy capacity regions. The performance comparison of the proposed optimization methods demonstrate the importance of the semi-analytical approach and the advantage of overlapping case over non-overlapping one., Comment: To appear in Digital Signal Processing, special issue on Joint Radar and Communications