This paper proposes a new leak detection and location method based on vibration sensors and generalised cross-correlation techniques. Considering the estimation errors of the power spectral densities (PSDs) and the cross-spectral density (CSD), the proposed method employs a modified maximum-likelihood (ML) prefilter with a regularisation factor. We derive a theoretical variance of the time difference estimation error through summation in the discrete-frequency domain, and find the optimal regularisation factor that minimises the theoretical variance in practical water pipe channels. The proposed method is compared with conventional correlation-based techniques via numerical simulations using a water pipe channel model, and it is shown through field measurement that the proposed modified ML prefilter outperforms conventional prefilters for the generalised cross-correlation. In addition, we provide a formula to calculate the leak location using the time difference estimate when different types of pipes are connected.

This paper proposes a detection and classification method for unmanned aerial vehicles, commonly called drones, using sensor fusion schemes. Datasets for drone detection and classification are collected by field measurements of actual drones using the optical camera, radar, and audio microphone as well as obtained from open online databases. In the first stage of the proposed method, drone detection and classification are conducted using the convolutional neural network (CNN) models separately trained by the optical images, radar range-Doppler maps, and audio spectrograms. Then, the CNN output probabilities are combined by the multinomial logistic regression to improve the drone surveillance accuracy through the fusion of the optical, radar, and audio sensors. Numerical simulations are performed with the experimental data and the open datasets. From the results, it is verified that the proposed sensor fusion method can improve the drone detection accuracy by up to 15.6% and can enhance the drone classification accuracy by up to 28.1% in terms of the F-score, compared to individual sensing schemes.

In this article, we investigate a secure uplink multiple-input multiple-output communication system, in which the multiple users transmit information to a legitimate access point (AP) when a passive eavesdropper exists. A secure multiple access method is proposed for single-antenna users by employing space–time line codes (STLCs), which enables noncoherent detection at the legitimate AP without channel state information (CSI) and achieves full spatial diversity. While extending the secure STLC scheme from the single-antenna to two-antenna users by combining the STLC with artificial noise (AN) injection, we design a low-complexity AN signals with the optimal power control scheme. The proposed AN is ensured to be eliminated after STLC decoding at the legitimate AP, while sustained in eavesdropper’s received signals as interference. Theoretical sum secrecy rates are derived by considering time-varying fading channels with the uncertainty of CSI at the users, and the numerical results verify that the proposed STLC schemes outperform the existing secure transmission schemes in terms of the sum secrecy rate.

In this paper, a general two-way relay (TWR) transmission method is proposed under per-antenna power constraints by combining space-time line codes (STLCs) with transmit power allocation for two source nodes. We introduce a general STLC-based encoding scheme for a decode-and-forward TWR, and derive the detection signal-to-interference-plus-noise ratio (SINR) values at two source nodes. An optimal encoder structure with power allocation is proposed in terms of maximizing the minimum SINRs, and it is verified that the optimal encoder is identical to the superposition of two conventional STLCs. An iterative method is proposed to find the optimal power allocation for detection at two source nodes. Moreover, a low-complexity suboptimal encoder is proposed for practical implementation. Numerical simulations present that the proposed STLC-based transmission method outperforms a conventional eigen-beamforming scheme with nulling and an STLC-based scheme with equal power allocation, in terms of average bit error rate of source nodes, regardless of the distribution of source nodes, the number of TWR antennas, and the transmit power.

In this paper, a multiuser space-time line code (MU-STLC) scheme is newly designed that concurrently delivers multiple STLC signals to multiple users, and a preprocessing matrix for the MU-STLC is derived based on the minimum mean square error criterion. The novel MU-STLC method retains the conventional STLC receiver structure so that each user linearly combines the received signals without using the full channel state information to decode the STLC signals. With more transmit antennas than the number of users having two receive antennas, a transmit antenna selection (TAS) scheme is investigated in combination with the proposed MU-STLC method, and the detection signal-to-interferenceplus-noise ratio (SINR) is derived depending on a specific TAS pattern. The performance improvement obtained from the TAS is significant, yet finding the optimal TAS pattern is a combinatorial problem that requires prohibitively high computational complexity. To resolve this issue, a greedy TAS algorithm is also proposed that iteratively selects the transmit antenna maximizing the detection SINR in each greedy step. The numerical results verify the efficacy of the proposed MU-STLC system with the SINR-based greedy TAS algorithm in terms of the bit error rate performance and computational complexity. For example, comparing with a scheme that selects four antennas from eight antennas randomly to support four users, the proposed TAS scheme can reduce the required signal-to-noise ratio for achieving 10 ^-3 bit-error-rate by approximately 6 dB when quadrature phase-shift keying is employed. Furthermore, the proposed method can achieve comparable performance to the optimal antenna selection scheme with the reduced computational complexity by O(M ⁵ ) from O(M ^U+3 ), where M and U are the numbers of transmit antennas and selected antennas (or users), respectively.