A new linear sparse array based on the nested array is proposed, which enjoys all the good properties of the two‐level nested array, and can provide more degrees of freedom (DOF). The new array is constructed by two uniform linear arrays (ULAs) and an additional sensor. The sensor locations, the array aperture, and the achievable DOF from its difference co‐array (DCA) are all benefited for closed‐form expressions. Furthermore, the resulting DCA is kept as a hole‐free ULA. The optimal numbers of sensors in the two ULAs provided the total number of physical sensors are derived. This new array can resolve more sources and achieve better angle estimation performance than the two‐level nested array. Simulation results validate these conclusions.

To mitigate the overlapping of the radar and communication frequency bands caused by large-scale devices access, we propose a novel integrated sensing and communication (ISAC) system, where a micro base station (MiBS) simultaneously carries out both target sensing and cooperative communication. Concretely, the MiBS, acting as the sensing equipment, can also serve as a full-duplex decode-and-forward relay to assist end-to-end communication. Moreover, nonorthogonal downlink transmission (NO-DLT) is adopted between the macro base station and the Internet of Things devices, so that the spectrum utilization can be further improved. To facilitate the performance evaluation, both the exact and asymptotic outage probabilities, the ergodic rates associated communication, and the probability of successful sensing detection are characterized. Subsequently, a pair of problems of maximizing the receive signal-to-interference-plus-noise ratio of the sensing signal and maximizing the sum rate of communication are formulated that are solved by the classic Lagrangian method while exploiting the associated function monotonicity. Our simulation results demonstrate that: 1) The proposed ISAC NO-DLT system improves both the communication and sensing performance under the same power consumption as noncooperative NO-DLT and 2) the proposed power allocation (PA) schemes are superior to the random PA scheme.

We propose an integrated end-to-end automatic speech recognition (ASR) paradigm by joint learning of the front-end speech signal processing and back-end acoustic modeling. We believe that “only good signal processing can lead to top ASR performance” in challenging acoustic environments. This notion leads to a unified deep neural network (DNN) framework for distant speech processing that can achieve both high-quality enhanced speech and high-accuracy ASR simultaneously. Our goal is accomplished by two techniques, namely: (i) a reverberation-time-aware DNN based speech dereverberation architecture that can handle a wide range of reverberation times to enhance speech quality of reverberant and noisy speech, followed by (ii) DNN-based multicondition training that takes both clean-condition and multicondition speech into consideration, leveraging upon an exploitation of the data acquired and processed with multichannel microphone arrays, to improve ASR performance. The final end-to-end system is established by a joint optimization of the speech enhancement and recognition DNNs. The recent REverberant Voice Enhancement and Recognition Benchmark (REVERB) Challenge task is used as a test bed for evaluating our proposed framework. We first report on superior objective measures in enhanced speech to those listed in the 2014 REVERB Challenge Workshop on the simulated data test set. Moreover, we obtain the best single-system word error rate (WER) of 13.28% on the 1-channel REVERB simulated data with the proposed DNN-based pre-processing algorithm and clean-condition training. Leveraging upon joint training with more discriminative ASR features and improved neural network based language models, a low single-system WER of 4.46% is attained. Next, a new multi-channel-condition joint learning and testing scheme delivers a state-of-the-art WER of 3.76% on the 8-channel simulated data with a single ASR system. Finally, we also report on a preliminary yet promising experimentation with the REVERB real test data.

We propose anew antenna array design approach for a multiple-input and multiple-output (MIMO) radar, which has closed-form expressions for the sensor locations and the number of achievable degrees of freedom (DOFs). This new approach utilizes the nested array as transmitting and receiving arrays. We employ the difference coarray of the sum coarray (DCSC) of the MIMO radar to obtain more DOFs for direction-of-arrival (DOA) estimation. Via properly designing the interelement spacings of the transmitting and receiving arrays, we can obtain a hole-free DCSC. The characteristics of array geometries are analyzed and the optimal numbers of sensors in transmitting/receiving antenna array are derived when given the total number of physical sensors. Simulations are conducted to demonstrate the advantages of the proposed array in terms of the number of DOFs, the number of resolvable sources, and the DOA estimation performance over the coprime MIMO array.

To improve precision in source localization from a time difference of arrival (TDOA) that has large measurement errors, this paper proposes a TDOA positioning algorithm based on an improved two-step constrained total least-squares algorithm; the algorithm comprise an iterative technique based on the alternating direction method of multipliers (ADMM). The algorithm linearizes the obtained time difference observation equation, and by considering the influence of the TDOA measurement errors on its accuracy, it provides the optimal TDOA positioning model. It first transforms the initial unary optimization problem into a binary optimization problem by introducing intermediate variables. It then obtains a coarse estimate of the source position with ADMM. It subsequently calculates a measurement matrix through the coarse estimate and the finally uses ADMM again to find the final source position. Simulation results show that the proposed algorithm can approach Cramer-Rao lower bound (CRLB) better and outperforms several existing methods when there are large TDOA measurement errors.

In a low-angle target parameter estimation scenario, the backscattered signals from targets are often distorted due to clutter and multipath, which significantly degrades the performance of direction-of-arrival (DOA) estimation. In general, the backscattered multipaths are modeled as coherent signals with respect to the direct path. Decorrelation algorithms such as spatial smoothing and matrix reconstruction can mitigate the effect of multipath. However, most of these methods will perform poorly or even fail for solving parameter estimation problem of low-angle targets in complex terrain where there are rich multipath scatterings. This paper presents a novel method that combines time reversal (TR) technique, coherent signal-subspace method (CSM), and multiple signal classification algorithm to perform DOA estimation in a low-angle target scenario. The TR technique takes advantage of multipath echoes recorded by a sensor array and adaptively adjusts TR probing waveforms to increase the signal-to-noise ratio in a rich scattering environment. Moreover, the CSM method compresses the energy of a signal into a predefined subspace to exploit the full time-bandwidth product of signal sources and cope with coherent wavefronts. As a result, the proposed new DOA estimation algorithm outperforms significantly the conventional methods. The Cramer-Rao bounds analysis and numerical simulations commendably validate the accuracy and robustness of the proposed method.