In this work, we propose the orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation with frequency modulated continuous wave (FMCW) (ODDM-FMCW) waveform to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose a square-root-Nyquist-filtered FMCW (SRN-FMCW) waveform to address limitations of conventional linear FMCW waveforms in ISAC systems. To better integrate with ODDM, we generate SRN-FMCW by embedding symbols in the DD domain, referred to as a DD-SRN-FMCW frame. A DD chirp compression receiver is designed to obtain the channel response efficiently. Next, we construct the proposed ODDM-FMCW waveform for ISAC by superimposing a DD-SRN-FMCW frame onto an ODDM data frame. A comprehensive performance analysis of the ODDM-FMCW waveform is presented, covering peak-to-average power ratio, spectrum, ambiguity function, and Cramér-Rao bound for delay and Doppler estimation. Numerical results show that the proposed ODDM-FMCW waveform delivers excellent ISAC performance in terms of root mean square error for sensing and bit error rate for communications.

Orthogonal delay-Doppler (DD) division multiplexing (ODDM) modulation has recently been proposed as a promising paradigm for communications in doubly-selective channels. In this work, we propose a novel ODDM with frequency modulated continuous wave (FMCW) (ODDM-FMCW) signal to enable integrated sensing and communication (ISAC) with a low peak-to-average power ratio (PAPR). We first propose the DD-domain embedded root-raised-cosine filtered FMCW (DD-RRC-FMCW) signal, where digital chirp compression is introduced for efficient radar signal processing. By superimposing this signal onto an ODDM data frame, we obtain the ODDM-FMCW signal for the proposed ISAC system. Next, we introduce a modified orthogonal matching pursuit algorithm for data-aided sensing. The algorithm is then combined with the soft successive interference cancellation with minimum mean square error detector to perform joint channel estimation and data detection. Our numerical results show that the proposed ODDM-FMCW signal delivers excellent normalized mean square error and bit error rate performance for ISAC.

We introduce BeaMsteerX (BMX), a novel mmWave hand hygiene gesture recognition technique that improves accuracy in longer ranges (1.5m). BMX steers a mmWave beam towards multiple directions around the subject, generating multiple views of the gesture that are then intelligently combined using deep learning to enhance gesture classification. We evaluated BMX using off-the-shelf mmWave radars and collected a total of 7,200 hand hygiene gesture data from 10 subjects performing a 6-step hand-rubbing procedure, as recommended by the World Health Organization, using sanitizer, at 1.5m---over 5 times longer than in prior works. BMX outperforms state-of-the-art approaches by 31--43% and achieves 91% accuracy at boresight by combining only two beams, demonstrating superior gesture classification in low SNR scenarios. BMX maintained its effectiveness even when the subject was positioned 30° away from the boresight, exhibiting a modest 5% drop in accuracy.

When the line-of-sight between the base station and mobile users is unavailable, reconfigurable intelligent surfaces (RIS) can be exploited to ensure connectivity and improve data transmission performance. The objective of this paper is to estimate and predict timevarying user-RIS channels with low pilot overhead using a small number of sparsely distributed active RIS elements. Structured covariance matrix interpolation is performed to fully utilize the array aperture from the sparse semi-passive RIS. Channel variation over time due to user movement and environmental factors can make it difficult to utilize all time slots for channel estimation and data transmission. To address this challenge, we propose a model based on long short-term memory (LSTM) networks for channel estimation and prediction to reduce the required training pilot signals and increase the transmission data rate using parallel computation. Simulation results verify the capability of the proposed approach to enhance data transmission in wireless networks and demonstrate its effectiveness compared to other machine learning models.

The implementation of MIMO radar in automotive applications requires careful waveform design in order to permit the separation of the transmit channels at the receiver. While there are a number of approaches for achieving this, slow-time coding is preferred in automotive applications as it allows the use of scaled versions of a single LFM waveform waveform across all antennas and pulses. In this paper, we present hybrid slow-time scheme that delivers the MIMO functionality in automotive radar systems by partitioning the transmit array into groups of transmit elements. Code division multiplexing is then applied across the groups whereas DDMA is used within each group. Spatial processing is then employed to generate a two dimensional angle-Doppler spectrum, leaving the target detection to the end. This approach requires only a small number of orthogonal codes to be designed, which alleviates the code design challenge, and enhances performance while keeping the probability of target collisions to a minimum. Simulations demonstrate the ability of the proposed method to resolve the target ambiguities.

This paper considers a wireless network assisted by an intelligent reflecting surface (IRS) to enhance data transmission between the base station and mobile users. Our objective is to estimate and predict the user-IRS channels by exploiting a small number of sparsely distributed active elements with a low pilot overhead. The Hermitian and Toeplitz properties of the data covariance matrices are used to perform covariance matrix interpolation for enhanced estimation of the time-varying user-IRS multipath channels, and a machine learning-based channel predictor is developed to predict the channels based on prior channel estimates so as to shorten the required training pilot signals and enhance the transmission data rate. Simulation results verify the effectiveness of the proposed method for accurate channel estimation and prediction.

In this paper, we investigate the signaling strategy of communications embedding in frequency-hopping (FH) multiple input multiple output (MIMO) radar. Previous work that embeds communication symbols into the emission of MIMO radar with orthogonal FH waveforms via phase modulation, and waveform orthogonality compromises the transmit processing gain of the radar. Yet, the directional transmit pattern can be maintained via an appropriate design of the transmit beamforming weight vector associated with each orthogonal waveform. Moreover, communication symbols can be embedded into the complex transmit beampattern via both amplitude and phase. In this paper, we propose two extended signaling strategies, which fully employs the flexibility of complex beampattern in tandem with spatial modulation, to combine the merits provided by waveform diversity and transmit beamforming. As a result, the communication data rate is significantly increased, while directional transmit gain is simultaneously preserved. In order to permit the complex beampattern to be varied in accordance with communication symbols, we also propose a new approach to the beamformer design which circumvents the computationally-consuming optimization. Simulation results demonstrate the effectiveness of the proposed dual-function signaling strategies.

The performance of direction of arrival (DOA) estimation using antenna arrays is fundamentally limited by the Cramér-Rao bound (CRB), which is intimately tied to the array configuration. In systems where a subset of antennas can be selected from a larger array, the array configuration can be recruited to enhance the DOA estimation performance by choosing the optimal subarray that minimizes the CRB. This strategy has been demonstrated to provide performance improvements, albeit at a substantial computational cost. Here, we leverage the power of unsupervised learning to reduce the computational cost of antenna selection for enhanced DOA estimation resulting in a practical online implementation of the array reconfiguration. We formulate the changing DOA estimation problem as a game in the context of online convex optimization, and employ a gradient-based technique that makes a move at each step in order to minimize the total loss after T steps. We compare the performance of the proposed method with the exhaustive search and a Dinkelbach-type algorithm that provides an approximate solution. We show that the proposed method is able to provide a solution that is close to the exhaustive search at a fraction of the computational time, thus permitting online implementation of the selection strategy.

Adaptive beamforming of large antenna arrays is difficult to implement due to prohibitively high hardware cost and computational complexity. An antenna selection strategy was utilized to maximize the output signal-to-interference-plus- noise ratio (SINR) with fewer antennas by optimizing array configurations. However, antenna selection scheme exhibits high degradation in performance compared to the full array system. In this paper, we consider a reduced-dimensional beamspace beamformer, where analogue phase shifters adaptively synthesize a subset of orthogonal beams whose outputs are then processed in a beamspace beamformer. We examine the selection problem to adaptively identify the beams most relevant to achieving almost the full beamspace performance, especially in the generalized case without any prior information. Simulation results demonstrated that the beam selection enjoys the complexity advantages, while simultaneously enhancing the output SINR of antenna selection.

Array spatial thinning is employed to select the most effective antenna elements in a large phased array for optimum performance concerning hardware and computational costs, in conjunction with managing element failure and radio interference mitigation. We formulate spatial array thinning under connectivity constraints to make the thinning applicable in large arrays. By introducing graph optimization, the problem is recast as a k-clique version of a generalized minimum clique problem. Furthermore, by studying optimum clustering for the proposed formulation, we show by an example that the unconstrained thinning performance is achievable, even with connectivity constraints.

Presents corrections to the paper, “On the estimation of the parameters of a real sinusoid in noise,” (Ye, S., et al), IEEE Signal Process. Lett., vol. 24, no. 5, pp. 638–642, May 2017.

The increase in the number of degrees of freedoms (DoF) that is afforded by multiple-input-multiple-output (MIMO) phased arrays is accompanied by an increase in hardware and computational costs. We mitigate this problem in a collocated MIMO phased array system by employing a selection strategy where a subset of K transmitter-receiver (Tx-Rx) pairs is chosen from the availableN pairs. We formulate the selection task as an optimization problem using the spatial correlation coefficient (SCC). Minimizing the SCC leads to an increase in the orthogonality of the signal and interference subspaces. We formulate and solve both the joint Tx-Rx selection problem and factored selection where the Tx and Rx are decoupled and treated separately. We show that both approaches can achieve excellent trade-off between performance and cost. While the factored problem compromises performance with respect to the joint Tx-Rx selection, it allows for better transmit power efficiency, thus increasing the received signal-to-noise ratio.

A single-snapshot fast computational Fourier-based direction-of-arrival (DOA) estimation method is introduced. This method applies the fast Fourier transform (FFT) to sensor data and performs effective cancellation of spectral leakage caused by sidelobe interactions, leading to unbiased DOA estimates of multiple sources. Successful elimination of spectral leakage is achieved by a sequential removal of strong sinc functions in the spatial frequency domain through an iterative interpolation process. The simulation results demonstrate superior performance of the proposed method over beamforming and other iterative FFT-based DOA estimation techniques as well as the high-resolution Root-MUSIC algorithm.

We propose and comprehensively analyze a computationally efficient algorithm to estimate the parameters of a real sinusoidal signal in noise. This method uses the fast Fourier Transform and is, therefore, computationally efficient. Accounting for the interference due to the negative spectral component allows the frequency to be estimated very accurately. Estimates of the amplitude and phase are derived in the process and are necessary for the suppression of the leakage. Theoretical analysis establishes that the estimator is asymptotically unbiased and achieves the Cramér-Rao lower bound. Simulation results are presented to verify the theory and demonstrate that the estimation performance is superior to other estimators in the literature.

This paper establishes the role of sparse arrays and sparse sampling in antijam global navigation satellite systems (GNSS). We show that both jammer direction of arrival estimation methods and mitigation techniques benefit from the design flexibility of sparse arrays and their extended virtual apertures or coarrays. Taking advantage of information redundancy, significant reduction in hardware and computational cost materializes when selecting a subset of array antennas without sacrificing jammer nulling or localization capabilities. In addition to the spatial array sparsity, antijam can utilize sparsity of jammers in the spatio-temporal frequency domains. By virtue of their finite number, jammers in the field of view are sparse in the azimuth and elevation directions. For the class of frequency modulated jammers, sparsity is also exhibited in the joint time-frequency signal representation. These spatial and signal characteristics have called for the development of sparsity-aware antijam techniques for the accurate estimation of jammer space-time-frequency signature, enabling its effective sensing and excision. Both theory and simulation examples demonstrate the utility of coarrays, sparse reconstructions, and antenna selection techniques for antijam GNSS.

In this paper, we consider antenna selection and array reconfiguration in the presence of multiple interferences based on the spatial correlation coefficient (SCC) which characterizes the spatial separation between the desired signal and interference subspace. Minimizing the SCC increases the separation between these two subspaces and leads to enhanced beamforming performance. We formulate this problem as a difference of two concave functions, which we solve through the convex-concave procedure (CCP). We derive the lower bound of the SCC as a function of the number of selected antennas which permits us to determine the required number for achieving the desired performance. We suggest two algorithms for implementing the antenna selection and present simulation results to validate the effectiveness of the proposed strategy.

In this paper, we utilize Bayesian Compressive Sensing (BCS) for direction-of-arrival (DOA) estimation based on the coarray. This enables estimation of more sources than the number of physical antennas. We adopt the covariance vectorization technique to construct the received signal vectors of coarrays for both fully and partially augmentable arrays. We then apply the single measurement vector BCS (SMV-BCS) for DOA estimation. Supporting simulation results for both sparse linear arrays and circular arrays demonstrate the effectiveness of the proposed approach in terms of high resolution and estimation accuracy compared to the MUSIC and sparse signal reconstruction based methods.

The parameter estimation of multiple superimposed complex exponentials in noise has been a popular research problem for decades due to its various practical applications. In this paper, we propose a simple yet accurate estimator for estimating the complex amplitudes and frequencies of the superimposed exponentials. Combining an efficient frequency estimator with a leakage subtraction scheme, the novel method iterates to consecutively estimate each component by gradually reducing the estimation error and increasing the estimation accuracy. Simulation results are presented to verify that the proposed algorithm is capable of obtaining estimation performance that is very close to the Cramer-Rao lower bound.

Antenna array configurations play an important role in direction of arrival (DOA) estimation. In this letter, performance enhancement of DOA estimation is achieved by reconfiguring the multi-antenna receiver through an antenna selection strategy. We derive the Cramer-Rao Bound (CRB) in terms of the selected antennas and associated subarray for both peak sidelobe level (PSL) constrained isotropic and directional arrays in single source cases. Since directional arrays are angle dependent, a Dinklebach type algorithm and convex relaxation are introduced to maintain the optimum selection by adaptively reconfiguring the directional subarrays using semi-definite programming. Simulation results validate the effectiveness of the proposed antenna selection strategy.

Space-time adaptive processing (STAP) is an effective strategy for clutter suppression in airborne radar systems. Limited training data, high computational load and the heterogeneity of training data constitute the main challenges in STAP. In this letter, we propose a new detection strategy based on selecting an optimum subset of antenna-pulse pairs associated with maximum separation between the target and the clutter trajectory. The proposed strategy reduces redundancy while addressing the above three interlinked challenges for detecting slow-moving targets especially in heterogeneous cases. An iterative Min-Max algorithm is proposed to solve the antenna-pulse selection problem, which is NP-hard combinatorial optimization. Extensive simulation results confirm the effectiveness of the proposed strategy.

Nuclear magnetic resonance (NMR) spectroscopy signals are ideally modelled as a superimposition of damped exponentials in additive Gaussian noise. In order to extract the information from these signals, methods are needed to decompose the signal into its components and estimate their parameters. This task can become quite difficult due to factors such as large number of samples, unknown and possibly large number of components, and lineshape distortion. In this paper, we propose a computationally efficient method for peak extraction in proton NMR spectroscopy without any a priori information. This method combines a simple damped complex exponential parameter estimation strategy with lineshape adaptation in the frequency domain. We apply the proposed technique on real NMR data and show that it outperforms competing state of the art methods. It is shown that the new method is capable of extracting very small lines such as satellites.

The estimation of the frequency and decay factor of a single decaying exponential in noise is a problem of prime importance. A popular estimation scheme uses the computationally efficient implementation of the Discrete Fourier transform, the FFT, to obtain a coarse estimate which is then improved by a fine estimation stage. Such estimators, however, show a performance that degrades and departs from the Cramér-Rao Lower Bound (CRLB) as the number of samples increases. To overcome this problem, we propose an iterative, exponentially windowed algorithm. We derive the new estimator's theoretical performance and study its behavior under different decay rates of the window. We show that the estimator has excellent performance that tracks the CRLB with increasing number of samples if the window decay rate is appropriately set.

The configuration of an antenna array plays a fundamental role in the ability of array signal processing to mitigate interference. We propose in this paper a novel reconfigurable adaptive linear array scheme to overcome the drawbacks of traditional array processing. We employ the effective carrier to noise density ratio (C/N0), which is a reliable measure of the performance in the Global Navigation Satellite Systems (GNSS) applications, expressing its dependence on the spatial separation through the Spatial Correlation Coefficient (SCC), with a lower SCC giving better interference mitigation performance. We then formulate the problem of determining the optimal orientation of a linear array in terms of the minimization of SCC. Simulation results show that the proposed method is effective in reducing the SCC and improving the effective C/N0. Finally, we propose a practical implementation where the array orientation is chosen from a number of present orientations.

This letter focuses on the efficient estimation of the frequencies and damping factors of a single 2-D damped complex exponential in additive Gaussian noise. We derive the estimators by extending the FFT-based frequency estimator that relies on interpolation on Fourier coefficients to 2-D damped signals. Performance analysis shows that the algorithm can achieve minimum variances at the fixed point when implemented in an interleaved manner for two iterations. Furthermore, we propose linearized version of the estimators that render them more amenable to real-time DSP implementation. We also demonstrate that the iterative implementation of the algorithm combining both versions is both unbiased and accurate.

In this paper, we propose a computationally simple algorithm for the estimation of the frequencies of a random phase two-dimensional (2-D) complex exponential in additive noise by extending the 1-D estimator developed by Aboutanios and Mulgrew. The procedure of the algorithm is based on a two-stage scheme consisting of a coarse estimator followed by a fine search stage. The separability of the problem implies that the estimator can be applied in each direction. Theoretical analysis shows, however, that the performance of the algorithm converges to the minimum point of the asymptotic variance after two iterations only if the estimation is applied jointly in the two dimensions. As in the 1-D case, this variance is extremely close to the 2-D Cramer-Rao Lower Bound. The simulation results are presented to verify the analysis.

The estimation of the frequency of a complex exponential is relevant to many fields and has been the subject of a significant amount of research. In this paper, we present a novel complex exponential frequency estimation algorithm that is based on the iterative interpolation strategy of Aboutanios and Mulgrew. The A&M algorithm uses two Fourier coefficients and has been shown to reach, in two iterations, a variance that is 0.063 dB above the Cramer-Rao Bound. It, however, requires the calculation of two additional DFT coefficients at each iteration. The new algorithm is computationally simpler as it exploits the standard DFT coefficients at the first iteration. Theoretical analysis and simulation results are presented that demonstrate that the new algorithm maintains the same performance as the A&M estimator.

Traditional space time adaptive processors for radar target detection require a training data set which is usually drawn from adjacent range gates. Clutter heterogeneity, however, can severely limit the available training sample support and consequently degrade the detection performance. The SDS algorithms, on the other hand, overcome this problem by operating solely on the test data without recourse to training data. In this paper we evaluate both of these approaches, in particular the AMF and MLED, using the MCARM data set. We illustrate the performance degradation of the AMF that results from the clutter heterogeneity and the corresponding advantage of the MLED. We also show that a calibration step of the spatial steering vectors results in significant performance improvement of all of the algorithms considered here.

The estimation of the frequency of a sinusoidal signal has been dealt with extensively in the literature. In this letter, we examine the dichotomous search of the periodogram peak algorithm. We provide an insight into the need to pad the data with zeros in order to achieve a performance that is comparable to the Cramer-Rao lower bound (CRB). We also propose a modified dichotomous search estimator that operates on the unpadded data sequence resulting in a computational saving. The modified dichotomous search is shown to have a performance that is comparable with the CRB.