DOA and Subarray-Interval Estimation for Arbitrarily Distributed UAV Swarm System

In this paper,DOA and subarray-interval estimation are considered and applied to arbitrarily distributed unmanned aerial vehicle(UAV)swarm system,in which multiple small UAVs containing uniform linear array(ULA)are divided by unknown intervals because of dynamic moving.Three parameters are taken to indicate the steering vector,namely,the direction of arrivals(DOAs)of target users,the intervals of UAVs,and the orientation angles of UAVs.The orientation angles are first estimated with an auxiliary user and the DOAs are obtained through a search free rooting method,despite the intervals among the UAVs.Afterwards,the intervals among UAVs can also be calculated via exhaustive when the number of target users are no less than three.We further develop a low-complex method to reduce the computational complexity during subarray-interval estimation.The deterministic Cramér-Rao bound(CRB)of the DOA,orientation angle and subarray-interval can be inferred in a closed form.Eventually,numerical instances are cited to verify the research results.

Overlapped Subarray Architecture of an Wideband Phased Array Antenna with Interference Suppression Capability

This paper presents a novel architecture of combining the linear array of antenna elements, where each antenna element has digitally selectable true time-delays as weights. Use of time-delays for beam-formation inherently makes the phased array network a wideband system. In particular, this technique envisage a new method of sharing antenna elements, by fixed overlapped sub-array architecture, which is able to maintain permissible element spacing to avoid grating lobe in antenna pattern. Moreover, this scheme additionally offers an easier null steering capability to the subarray architecture. This method essentially eliminates the need for intensive computation of complex weight vectors attached to each antenna element.

Multi-dimensional ambiguity function for subarray-based space-time coding radar

Space-time coding radar has been recently proposed and investigated.It is a radar framework which can perform transmit beamforming at the receiver.However,the range resolution decreases when the number of the transmit element increases.A subarray-based space-time coding(sub-STC)radar is explored to alleviate the range resolution reduction.For the proposed radar configuration,an identical waveform is transmitted and it introduces a small time offset in different subarrays.The multidimensional ambiguity function of sub-STC radar is defined by considering resolutions in multiple domains including the range,Doppler,angle and probing direction.Analyses on properties of the multi-dimensional ambiguity function of the sub-STC radar with regard to the spatial coverage,resolution performance and low sidelobes are also given.Results reveal that the range resolution and low sidelobes performance are improved with the proposed approach.

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

Log Anomaly Detection Through GPT-2 for Large Scale Systems

As the scale of software systems expands,maintaining their stable operation has become an extraordinary challenge.System logs are semi-structured text generated by the recording function in the source code and have important research significance in software service anomaly detection.Existing log anomaly detection methods mainly focus on the statistical characteristics of logs,making it difficult to distinguish the semantic differences between normal and abnormal logs,and performing poorly on real-world industrial log data.In this paper,we propose an unsupervised framework for log anomaly detection based on generative pre-training-2(GPT-2).We apply our approach to two industrial systems.The experimental results on two datasets show that our approach outperforms state-of-the-art approaches for log anomaly detection.

Finite sensor selection algorithm in distributed MIMO radar for joint target tracking and detection

Due to the requirement of anti-interception and the limitation of processing capability of the fusion center, the subarray selection is very important for the distributed multiple-input multiple-output(MIMO) radar system, especially in the hostile environment. In such conditions, an efficient subarray selection strategy is proposed for MIMO radar performing tasks of target tracking and detection. The goal of the proposed strategy is to minimize the worst-case predicted posterior Cramer-Rao lower bound(PCRLB) while maximizing the detection probability for a certain region. It is shown that the subarray selection problem is NP-hard, and a modified particle swarm optimization(MPSO) algorithm is developed as the solution strategy. A large number of simulations verify that the MPSO can provide close performance to the exhaustive search(ES) algorithm. Furthermore, the MPSO has the advantages of simpler structure and lower computational complexity than the multi-start local search algorithm.

High-resolution bottom detection algorithm for a multibeam echo-sounder system with a U-shaped array

High-resolution approaches such as multiple signal classification and estimation of signal parameters via rotational invariance techniques（ESPRIT） are currently employed widely in multibeam echo-sounder（MBES）systems for sea floor bathymetry,where a uniform line array is also required.However,due to the requirements in terms of the system coverage/resolution and installation space constraints,an MBES system usually employs a receiving array with a special shape,which means that high-resolution algorithms cannot be applied directly.In addition,the short-term stationary echo signals make it difficult to estimate the covariance matrix required by the high-resolution approaches,which further increases the complexity when applying the high-resolution algorithms in the MBES systems.The ESPRIT with multiple-angle subarray beamforming is employed to reduce the requirements in terms of the signal-to-noise ratio,number of snapshots,and computational effort.The simulations show that the new processing method can provide better fine-structure resolution.Then a highresolution bottom detection（HRBD） algorithm is developed by combining the new processing method with virtual array transformation.The application of the HRBD algorithm to a U-shaped array is also discuss.The computer simulations and experimental data processing results verify the effectiveness of the proposed algorithm.