Qixiang Niu

This paper addresses the challenge of optimizing detection and communication performance in integrated systems for underwater detection and communication by proposing a waveform design and signal processing method based on an overcomplete wavelet Fourier dictionary. First, by leveraging the multiresolution properties of wavelets and the time–frequency localization of the Fourier transform, a nonorthogonal overcomplete waveform is designed tailored to underwater doubly dispersive channels, thereby enhancing performance under Doppler shifts and multipath effects. Next, a sparse reconstruction algorithm is introduced based on a multiresolution dictionary that extracts signal features layer by layer, reducing computational complexity while improving target detection accuracy and underwater communication. Finally, by analyzing the upper and lower bounds of the pairwise error probability, the improvement in bit error rate achieved by the waveform design is quantified. Simulation results show that the proposed method outperforms conventional approaches in terms of the wideband ambiguity function, detection probability, and pairwise error probability, providing theoretical support for integrated systems for underwater detection and communication.

 
@ARTICLE{niu2026waveform,
  author={Niu, Qixiang and Shi, Wentao and Zhang, Qunfei and Wang, Boheng},
  journal={Acta Acustica},
  title={Waveform Design and Processing for Underwater Integrated Detection and Communication Based on Affine Fourier Dictionary},
  year={2026},
  volume={51},
  number={2},
  pages={501-514},
  doi={10.12395/0371-0025.2025089}}
 

Passive sonar is crucial for underwater target detection but often experiences reduced localization accuracy due to sensor biases and positional deviations caused by environmental factors. Traditional approaches struggle to effectively address these biases and also lack an integrated analysis of bias estimation, sensor classification, and fusion algorithms. To address this issue, this paper proposes a bias estimation-classification-fusion localization (BECFL) algorithm, integrating bias estimation, sensor classification, and adaptive fusion weighting. Within the BECFL framework, dynamic stochastic models are first developed to characterize underwater target motion, sensor biases, and multipath fading channels. Sensor biases are then estimated using maximum likelihood estimation (MLE) based on multiple sensor observations. Sensors are subsequently classified through an improved expectation-maximization (EM) algorithm, iteratively refining the classification parameters. A variable step-size weighting fusion approach is applied, reducing the impact of sensor biases on localization results. Simulations demonstrate that, compared to existing methods, the BECFL algorithm improves localization accuracy and maintains robust performance even under significant sensor bias, data loss, false alarms, and missed detections. Additionally, analyses of actual underwater data confirm the practical applicability of the proposed algorithm for passive sonar detection and localization in underwater environments.

 
@ARTICLE{niu2026underwater,
  author={Niu, Qixiang and Shi, Wentao and Zhang, Qunfei and Zhang, Dongzhe},
  journal={Digital Signal Processing},
  title={Underwater Passive Sonar Fusion Detection Based on Sensor Bias Estimation and Classification},
  year={2026},
  volume={168},
  pages={105596},
  doi={10.1016/j.dsp.2025.105596}}
 

Integrated systems for underwater detection and communication (ISUDC) plays a pivotal role in improving sonar integration and efficiency and has become a key research focus. This article tackles the underwater doubly dispersive wireless channel (DDWC) by introducing a novel transmitter side waveform design and a receiver side channel estimation algorithm based on affine frequency division multiplexing (AFDM) within the ISUDC framework. At the transmitter we employ AFDM as the core signal and target minimization of weighted sidelobes in the wideband ambiguity function (WAF). We use numerical analysis to quantify coding effects on the WAF and apply optimized random phase perturbations in P4 encoding via particle swarm optimization (PSO) to enhance detection and improve time Doppler resolution. At the receiver we develop a sparse channel estimation method based on an affine Fourier dictionary, which uses pilot signals to estimate phase perturbations and exploits delay-Doppler sparsity to improve accuracy in dynamic underwater environments while reducing multipath interference. We also derive new bounds on the pairwise error probability (PEP) for underwater acoustic DDWC, including numerical lower bounds and Chernoff upper bounds. Simulations demonstrate that jointly optimizing waveform design and channel estimation reduces PEP and normalized mean-square error (NMSE), provides superior detection for consecutive identical coded symbols and yields an ideal “thumbtack” shaped WAF. The proposed framework delivers a reliable and efficient solution for ISUDC in complex underwater environments.

 
@ARTICLE{11124251,
  author={Niu, Qixiang and Shi, Wentao and Jing, Lianyou and He, Chengbing and Zhang, Qunfei and Tan, Weijie},
  journal={IEEE Internet of Things Journal}, 
  title={Joint Optimization of Underwater Acoustic ISUDC Waveform Design and Sparse Channel Estimation Algorithms}, 
  year={2025},
  volume={12},
  number={20},
  pages={43756-43771},
  keywords={Channel estimation;Underwater acoustics;Receivers;Mathematical models;Transmitters;Dispersion;Perturbation methods;Delays;OFDM;Encoding;Affine frequency division multiplexing (AFDM);integrated systems for underwater detection and communication (ISUDC);P4 encoding optimization;sparse channel estimation;underwater doubly dispersive channels},
  doi={10.1109/JIOT.2025.3598477}}
 

In this article, the direct-wave suppression problem is investigated based on the bistatic integrated system of underwater detection and communication (ISUDC). The bistatic ISUDC system has the advantages of saving space and reducing energy consumption. However, in the system, the direct wave and strong multipath are much stronger than the target echo signal, which brings difficulties in echo extraction and subsequent signal processing. Through system modeling, the received signal model of ISUDC signal after underwater multipath and channel fading is considered. An improved algorithm double window CLEAN (DW-CLEAN) algorithm is proposed based on the CLEAN algorithm. The algorithm utilizes the first layer of window shift to achieve the purpose of eliminating the effect of underwater multipath and the second layer of the window is used to achieve the signal frequency and time correlation reduction. In order to achieve almost constant amplitude and time delay for each signal segment, the effect of time variation of direct wave on direct-wave reconstruction is minimized. Finally, the windowed ISUDC signal is reconstructed by the CLEAN algorithm on the direct-wave signal to obtain the echo signal. The experimental analysis shows that the algorithm has convergence and stability, can effectively suppress the direct wave and strong multipath interference, and eliminates the influence of the time-varying instantaneous frequency of the ISUDC waveform on the direct-wave estimation. Thus, a more complete echo signal and better processing performance are obtained.

 
@ARTICLE{10422775,
  author={Niu, Qixiang and Shi, Wentao and Zhang, Qunfei and Zhang, Chenglin},
  journal={IEEE Sensors Journal}, 
  title={An Improved CLEAN Direct-Wave Suppression Algorithm in Integrated System of Underwater Detection and Communication}, 
  year={2024},
  volume={24},
  number={6},
  pages={8503-8516},
  keywords={Signal processing algorithms;Fading channels;Time-frequency analysis;Sensors;Estimation;Propagation losses;Frequency modulation;Direct-wave suppression;dual base;signal processing;underwater integrated system for detection and communication;underwater multipath},
  doi={10.1109/JSEN.2024.3360274}}
 

For the integrated system of underwater detection and communication, an emitted waveform should satisfy both detection and communication requirements. The signal processing method at the receiving end must also accomplish target detection and communication accordingly. This study uses a generalised sinusoidal frequency modulated (GSFM) waveform with a carrier to ensure detection performance. At the same time, the communication information encoding with Gaussian Minimum Shift Keying is modulated to the GSFM signal for communication purposes. Unlike previous work, an improved Blind Source Separation algorithm is utilised at the receiving end, which is better adapted to waveform separation and processing in the underwater time-varying unknown environment. The analysis of detection probability, peak-to-average ratio, and signal processing results show that the proposed waveform and corresponding signal processing scheme can effectively meet the need for integrated underwater detection and communication system.

 
@ARTICLE{niu2023waveform,
  title={Waveform design and signal processing method for integrated underwater detection and communication system},
  author={Niu, Qixiang and Zhang, Qunfei and Shi, Wentao},
  journal={IET Radar, Sonar \& Navigation},
  volume={17},
  number={4},
  pages={617--627},
  year={2023},
  publisher={Wiley Online Library}}
 

In bistatic sonar systems, the receiver is inevitably affected by the direct-path signal from the transmitter and its multipath interference, which severely degrades target detection performance. To suppress such interference, we propose an improved robust principal component analysis (RPCA) method with a residual-adaptive regularization parameter. The method is based on the low-rank property of the direct-path and multipath interference, and the sparsity of the target echo. Because the conventional RPCA method employs a fixed regularization parameter, it is prone to underfitting or overfitting in bistatic direct-path suppression. In the proposed method, the regularization parameter is adaptively adjusted at each iteration according to the residual using the median absolute deviation (MAD), thereby enabling better separation of the interference from the target echo signal. Sea-trial results demonstrate that the proposed method achieves target detection and parameter estimation at an SIR of −58 dB, outperforming the conventional RPCA method and its adaptive extensions in bistatic sonar environments. Moreover, it requires fewer iterations and converges faster, indicating its suitability for direct-path and multipath suppression in bistatic sonar environments.

 
@ARTICLE{11653533,
  author={Wang, Boheng and Cui, Xiaodong and Zhang, Qunfei and Niu, Qixiang and Liu, Jiajie},
  journal={IEEE Sensors Journal},
  title={An Improved RPCA Method for Direct-Path and Multipath Interference Suppression in Bistatic Sonar},
  year={2026},
  note={Early Access},
  doi={10.1109/JSEN.2026.3721107}}
 

The use of deep learning (DL) for sound event localization and classification with wireless acoustic sensor networks (WASNs) is an emerging research area. However, current methods for sound event localization and classification exhibit limitations in perceiving extensive soundscapes. They are typically effective only for a fraction of the soundscapes and do not fully exploit the surrounding information. Moreover, in outdoor settings, the performance accuracy is susceptible to the adverse effects of signal attenuation and environmental noise. In this article, we propose a DL-based method that integrates frequency, temporal, and spatial domain features with attention mechanisms to estimate the location and the class of sound sources using a WASN in an outdoor setting. We introduce soundmap features to capture spatial information across multiple frequency bands and time frames. Furthermore, we integrate attention mechanisms to learn channelwise relationships and temporal dependencies within acoustic features. To evaluate the proposed method, we conduct experiments using simulated datasets with different levels of noise and sizes of the monitoring area, as well as different array and source positions. Moreover, we conduct a real-world experiment in an outdoor environment with dimensions of 100 × 80 m. The experimental results demonstrate the superiority of the proposed method over state-of-the-art methods in both sound event classification (SEC) and sound source localization (SSL) tasks.

 
@ARTICLE{11192195,
  author={Zhang, Dongzhe and Chen, Jianfeng and Bai, Jisheng and Wang, Mou and Shi, Dongyuan and Niu, Qixiang and Bernardini, Alberto},
  journal={IEEE Sensors Journal},
  title={Sound Event Localization and Classification Using Wireless Acoustic Sensor Networks in Outdoor Environments},
  year={2025},
  volume={25},
  number={22},
  pages={42141-42153},
  doi={10.1109/JSEN.2025.3614680}}
 

Internet of Vehicles (IoV) has become the key technology to improve road safety and traffic efficiency. However, with the explosion of the number of vehicles and more frequent authentication, computing and communication costs increase. Nevertheless, most of the traditional IoV authentication protocols lack scalability and are vulnerable to physical attacks and internal attacks, so they are difficult to meet the needs of modern IoV environment. To address these issues, this article proposes a hierarchical mutual authentication protocol for the IoV based on physical unclonable function (PUF) and fog computing. In this protocol, we design a three-layer architecture for IoV supported by fog computing, where the fog node (FN) acts as an intermediate authentication layer, managing a group of roadside units (RSUs) and sharing the computational tasks of the trusted authority (TA) to alleviate its burden. Moreover, PUFs are embedded in entity devices to encrypt sensitive parameters, preventing internal data leakage. Based on this architecture, our protocol implements both vehicle-to-infrastructure (V2I) authentication and group authentication. In the V2I authentication, the FN distributes session keys in bulk to a group of RSUs and multiple vehicles, significantly reducing the repetitive authentication overhead between vehicles and RSUs. In the group authentication, the RSU authenticates vehicles and distributes group key, thereby avoiding the need for frequent authentication. We have also implemented fast updates of session keys and group keys, independently completed by the FN and RSU, reducing reliance on the TA and enhancing key security. We have conducted both formal and informal security analyses of the proposed protocol and used the ProVerif tool to verify its security. The results demonstrate that the protocol meets the security requirements needed for IoV. The evaluation results show that the proposed protocol can significantly reduce the computation and communication overhead, and improve the overall performance of the system.

 
@ARTICLE{11054068,
  author={Jia, Chunzhi and Tan, Weijie and Shen, Ao and Li, Zhen and Chen, Yuling and Zhao, Rui and Niu, Qixiang and Li, Chunguo},
  journal={IEEE Internet of Things Journal},
  title={A PUF-Enhanced Fog-Enabled Hierarchical Authentication Protocol for Internet of Vehicles},
  year={2025},
  volume={12},
  number={18},
  pages={37629-37644},
  doi={10.1109/JIOT.2025.3583822}}
 

Affine Frequency Division Multiplexing (AFDM) has emerged as a promising waveform for integrated underwater detection and communication (IUDC) due to its low computational complexity and robust sensing capabilities. This paper investigates AFDM-based underwater systems, focusing on the impact of signal coding on detection performance through the discrete wideband ambiguity function (WAF). An enhanced P4 coding scheme is proposed, incorporating random phase perturbations optimized via particle swarm optimization (PSO) to improve the WAF properties. The proposed method effectively enhances detection performance by significantly reducing sidelobe levels compared to conventional P4 coding, as demonstrated through simulations. Furthermore, the approach maintains reliable communication with minimal bit error rate degradation. By adjusting the maximum phase perturbation, the method offers a flexible trade-off between detection and communication performance, providing a robust and efficient solution for underwater sensing and data transmission.

 
@INPROCEEDINGS{11431764,
  author={Niu, Qixiang and Ramírez, David and Shi, Wentao and Zhang, Qunfei},
  booktitle={GLOBECOM 2025 - 2025 IEEE Global Communications Conference},
  title={Underwater Detection and Communication Integrated Waveform Design Based on P4 Encoding},
  year={2025},
  pages={2607-2612},
  doi={10.1109/GLOBECOM59602.2025.11431764}}
 

Affine Frequency Division Multiplexing (AFDM) is an advanced communication waveform designed specifically for time-varying channels. This chirp-based multicarrier modulation technique is computationally efficient, which enables compact sonar implementations while achieving robust sensing performance through flexible parameter adjustments. Such characteristics make AFDM well-suited for Integrated Systems for Underwater Detection and Communication (ISUDC). In this paper, we explore an AFDM-based ISUDC system and propose a waveform model capable of transmitting multiple symbols to increase data capacity. We derive the Wideband Ambiguity Function (WAF) for this waveform and enhance it using complementary sequence coding, which reduces sensitivity to WAF variations and improves detection accuracy. Simulation results demonstrate that the proposed AFDM-based ISUDC waveform, featuring both multi-symbol support and complementary sequence coding, increases data capacity and improves communication bit error rate (BER) compared to traditional ISUDC waveforms. Additionally, the optimized WAF achieves enhanced detection performance, fulfilling critical ISUDC requirements.

 
@INPROCEEDINGS{10978131,
  author={Niu, Qixiang and Shi, Wentao and Ramírez, David and Jing, Lianyou and Zhang, Qunfei},
  booktitle={2025 IEEE Wireless Communications and Networking Conference (WCNC)}, 
  title={AFDM-based Integrated System for Underwater Detection and Communication Waveform Design}, 
  year={2025},
  pages={1-6},
  doi={10.1109/WCNC61545.2025.10978131}}
 

This paper presents a new integrated waveform design scheme for underwater detection and communication. The scheme ensures that the waveform has both communication and detection performance by modulating the communication base-band signal into the form of a detection signal. The generalized sinusoidal frequency modulation (GSFM) signal, which is suitable for underwater continuous active sonar detection, is selected as the detection signal carrier. The detection performance and target estimation performance were analyzed by comparing the ambiguity function, azimuth estimation and detection probability with the associated peak-to-average ratio as the test statistic for linear frequency modulation (LFM), GSFM and the designed waveform. By adjusting the GSFM parameters, a range of parameter values for the detection waveform carrier in the integrated waveform is obtained. The simulation results show the effectiveness of the proposed waveform design method.

 
@INPROCEEDINGS{9984531,
  author={Niu, Qixiang and Zhang, Qunfei and Shi, Wentao},
  booktitle={2022 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)}, 
  title={Integrated Waveform Design Scheme Based on Underwater Detection and Communication}, 
  year={2022},
  volume={},
  number={},
  pages={1-5},
  keywords={Frequency modulation;Chirp modulation;Azimuth;Design methodology;Simulation;Estimation;Peak to average power ratio;Integrated system for detection and communication;waveform design;ambiguity functions;continuous active sonar;target estimation},
  doi={10.1109/ICSPCC55723.2022.9984531}}