This work explores the relationship between sensing accuracy and precoding coefficients for edge artificial intelligence (AI) inference in integrated sensing, communication and computation (ISCC) networks. We start by constructing a system model of an over-the-air-empowered ISCC network for edge AI inference, involving distributed edge sensors for feature extraction and an edge server for classification. Based on this model, we introduce a discriminant gain (DG) to characterize sensing accuracy and novelly derive an explicit function of the DG about precoding coefficients, giving valuable insights into precoding design. Guided by this, we propose an effective precoding algorithm to solve a non-convex DG-maximization problem. Simulation results demonstrate that the proposed design achieves up to$15\%$and$10\%$sensing accuracy improvements on synthetic and real-world datasets, respectively, over the conventional scheme at low SNR, thereby validating its effectiveness and superiority for edge AI inference in ISCC networks.

In recent years, security has emerged as a critical aspect of integrated sensing and communication (ISAC) systems. While significant research has focused on secure communications, particularly in ensuring physical layer security, the issue of sensing security has received comparatively less attention. This paper addresses the sensing security problem in ISAC, particularly under the threat of multi-intercept adversaries. We consider a realistic scenario in which the sensing target is an advanced electronic reconnaissance aircraft capable of employing multiple signal interception techniques, such as power detection (PD) and cyclostationary analysis (CA). To evaluate sensing security under such sophisticated threats, we analyze two critical features of the transmitted signal: (i) power distribution and (ii) cyclic spectrum. Further, we introduce a novel ergodic cyclic spectrum metric which leverages the intrinsic mathematical structure of cyclostationary signals to more comprehensively characterize their behavior. Building on this analysis, we formulate a new ISAC design problem that explicitly considers sensing security, and we develop a low-complexity, efficient optimization approach to solve it. Simulation results demonstrate that the proposed metric is both effective and insightful, and that our ISAC design significantly enhances sensing security performance in the presence of multi-intercept threats.

Reconfigurable Intelligent Surface (RIS) is a key technology for radar and communication systems. This paper focuses on designing RIS-aided communication-compatible MIMO radar unimodular waveform design for radar and communication coexistence. The goal is to minimize the RIS-aided spatial Integrated Sidelobe Level Ratio (ISLR) under spectral constraint and unimodular constraints on both the waveform and RIS phase shifts. This is a challenging non-convex problem that existing methods cannot solve directly. We observe that the spectral constraint can be rewritten as a smooth non-negative function, and the Product Complex Circle Manifold (PCCM) naturally satisfies the unimodular constraints. Based on these insights, we propose an Inequality Constrained Product Manifold Optimization (ICPMO) framework. The spectral constraint is handled using a smooth penalty function, reformulating the problem as an unconstrained optimization on the PCCM. We then develop a Parallel Conjugate Gradient Descent (PCGD) algorithm without relaxing the objective. Simulations show our method reduces beam sidelobes by about 10 dB and improves energy distribution nulling compared to non-RIS methods.

The orthogonal frequency division multiplexing (OFDM) sequences are widely used in 4 G, 5 G and integrated sensing and communication (ISAC). In this paper, we address the challenge of designing orthogonal OFDM sequences. The weighted sum of the auto-correlation and cross-correlation is minimized. Considering the base station (BS) hardware constraints, the length of OFDM sequence in time domain and frequency domain is inconsistent. Furthermore, we consider the limitation of peak to average power ratio (PAPR). To solve the non-convex problem, we have developed an efficient alternating direction method of multipliers (ADMM) algorithm. Numerical simulations confirm the effectiveness of the proposed algorithm.

In dual-function radar-communication (DFRC) systems, precise and concurrent estimations of channel state information (CSI) and target directions are imperative to ensure simultaneous communications and sensing. This paper delves into a massive MIMO system employing hybrid beamforming (HBF), identified as a viable solution for achieving significant antenna gains while maintaining manageable hardware costs. The study focuses on a MIMO-DFRC system with a subarray-connection HBF architecture, proposing a preamble-by-preamble methodology to enable simultaneous wireless communications and target tracking on a per-frame basis. In the initial frames, we exploit the Doppler discrepancies between communication paths and fast-moving targets to segregate them. This sets the stage for iterative refinements of Doppler frequencies, angles of departure (AoDs), and angles of arrival (AoAs) estimations in the least square manner. Utilizing these estimations accrued from previous frames, the hybrid precoder and combiner of the subsequent frames are designed to boost a weighted signal-to-noise ratio (SNR), safeguarding the target tracking accuracy while concurrently refining the CSI estimation. Numerical simulations validate the proposed algorithm, demonstrating effective tracking performance with low overhead in MIMO-DFRC systems.

Polarization adds an additional dimension to the radar signals, contributing to waveform diversity. Codesign of unimodular waveforms and filters with polarimetric power allocation for maximizing the signal-to-interference-plus-noise ratio (SINR) plays a key role in the polarimetric radar system. The problem is challenging to solve due to the nonconvex nature of the objective function and constraints, coupled with the interdependence of multiple variables. Existing methods mainly solve this problem by fixing the power allocation or relaxing the objective function and obtaining the receive filters with matrix inversion. We directly address this problem without matrix inversion by using the proposed adaptive unified manifold optimization (AUMO) framework. Specifically, a unified manifold space (UMS) is constructed to satisfy the constraints of unimodular waveform, filters, and power, transforming the problem to an unconstrained optimization problem over the manifold. To solve this problem, a parallel conjugate gradient (PCG) algorithm is derived. This algorithm can adaptively change the step size by exploring the local features of the manifold space. The experimental results based on the measured data show that the proposed method outperforms existing methods in terms of SINR gain and execution time.

The virtual sensing (VS) technique enables an active noise control (ANC) system to estimate the virtual error signal for control using remote monitoring microphones. However, instances where noise characteristics and primary paths exhibit variations lead to a noticeable decline in performance for the conventional VS technique. To address this challenge, we propose the cognitive VS technique in this paper. Its objective is to enhance VS performance by providing a more precise estimate of the error signal based on environmental cognition. Differing from the previous selective VS technique, the cognitive VS technique connects both the reference and monitoring microphones to a lightweight classifier. Hence, the cognitive VS technique has the capability to dynamically adjust the VS filter in accordance with the noise and environmental conditions identified by the classifier. Simulation results demonstrate that the cognitive VS technique surpasses conventional and selective VS techniques in terms of adaptivity and generalisation when noise characteristics change and primary paths are time-varying.

Recently, federated learning (FL) has been threat-ened by the gradient inversion attack that infers user-private data from shared gradients. To cope with this problem, the differential privacy (DP) technique is widely employed in FL. However, when FL faces the non-independent identically distributed (non-IID) data scenarios, applying DP to protect user data privacy remains inefficient in terms of model accuracy and communication costs. In this paper, inspired by the Mixup data augmentation method, we propose a privacy-preserving FL approach called Fed2VAEs to address this problem. Specifically, we introduce a Mixup Module consisting of two variational autoencoders to remove the private information of user data. To balance the trade-off between data privacy and data utility, from the perspective of mutual information, a learning objective is proposed. We conduct extensive experiments under different non-IID data settings, and the experimental results show that Fed2VAEs can significantly reduce the communication cost and improve model accuracy (up to 8.57%) on the premise of successfully protecting user data privacy.

In this paper, we consider the problem of direction of arrival(DOA) estimation with one-bit quantized datas. Based on the existing one-bit multiple signal classification(MUSIC) algorithm, this paper gets datas form a uniform rectangular array(URA) then lets datas quantized by one-bit Analog to Digital Converters(ADCs) and introduces a switching matrix. The covariance matrix of one-bit datas is reconstructed by conjugation and product with the switching matrix, and then added to itself to obtain a new one-bit covariance matrix. This paper proposes an improved MUSIC algorithm based on the new covariance matrix, which is the one-bit modified multiple signal classification(MMUSIC) algorithm, and proves in simulation that when the number of snapshots is small, the signal-to-noise ratio is small, and the number of arrays is appropriate, the angle measurement performance of the one-bit MMUSIC algorithm is better than the existing one-bit MU-SIC algorithm, and the Cramer-Rao Bound(CRB) is given as a reference lower bound for the angle measurement performance.

Resource allocation for enhancing the target capacity of multiple target tracking (MTT) with desired accuracies for given transmit power is the key issue in radar networks. Most existing methods solve this problem with heuristic evolutionary methods or convex relaxation methods with high computational cost, which lack the real-time adaptability for dynamic threat scenarios. To overcome this issue, we propose a real-time Adaptive Manifold Optimization (AMO) framework. This is achieved by utilizing the inherent real oblique characteristic of the power matrix constraints. Specifically, we construct a real oblique manifold that satisfies the constraints, enabling the problem to be rephrased as an unconstrained problem over the manifold space. Then, we derive a conjugate gradient algorithm for direct optimization of the problem. Simulation results demonstrate that the proposed method outperforms existing approaches in terms of target capacity, MTT accuracy and computational cost.

The joint design of waveforms and filters has key applications in polarimetric radar target detection. This letter studies the joint design of waveforms and filters to maximize the signal-to-interference-to-noise ratio (SINR) of polarimetric radar, which is a nonconvex and NP-hard problem. Most existing works solve it based on matrix inversion and problem relaxation, which inevitably introduce high complexity and relaxation errors. We notice that a unified manifold space naturally satisfies the constant modulus constraint (CMC) and the norm constraint. Based on this characteristic, we proposed a parallel manifold joint optimization (PMJO) method to solve it without relaxing the objective function. Specifically, the unified product manifold is constructed to satisfy both waveform and filter constraints. Subsequently, the problem is transformed into an unconstrained one by projecting it onto the product manifold space. Finally, a parallel conjugate gradient method is proposed to simultaneously optimize waveforms and filters, which can adaptively adjust the step size and fully explore the product manifold space. Simulation results show that our method can obtain a 2-dB performance advantage compared with the existing methods, while having a half-order of magnitude advantage in time complexity.

The over-the-horizon (OTH) radar systems are seriously affected by sea clutter. The precise estimation of sea clutter covariance (SCC) plays an important role in suppressing the sea clutter, especially in the case of limited samples. Towards this end, this letter proposes a robust dictionary learning (DL) based on the SCC reconstruction method to suppress sea clutter with a single observation sample. The DL procedure is formulated as a non-convex optimization problem, which is known to be NP-hard. To address this problem and reconstruct the SCC effectively, we propose a novel alternating proximal method designed for solving ℓ0-norm based problems. Extensive analyses demonstrate that our proposed algorithm exhibits global convergence with a sub-linear convergence rate. Through simulation results, we verify the effectiveness of our proposed algorithm, demonstrating a notable performance improvement compared to conventional methods using single samples. Additionally, we conduct experiments using real sea clutter datasets, further demonstrating the practical applicability of our method.

Doppler ambiguity in missile-borne radar is usually caused by the low pulse-repetition-frequency (PRF) waveforms, which may result in a significant performance loss of target detection and location in the presence of clutter. To solve the issue of Doppler ambiguity clutter in missile-borne space-time adaptive processing (STAP) radar, this paper proposes a novel clutter suppression approach with the aid of designing the pulse interval (PI) of the waveform. Specifically, we formulate the optimization problem by considering the metric of minimizing the maximum sidelobe level in the Doppler domain, subject to the constraint of a given coherent processing interval (CPI).To solve the complicated problem, the modified genetic algorithm combining the simulated annealing algorithm (MGA-SA) is devised. Extensive simulations showcase that the proposed method surpasses SCNR by over 3dB in clutter suppression effectiveness when compared to conventional techniques, all while maintaining the same dwell time and frequency resources.

Multiple Input Multiple Output (MIMO) radar unimodular waveform design with range-ISL optimization is a key technology in remote sensing. Due to the non-convex quartic objective function and constant modulus constraint (CMC), the problem is NP-hard and non-convex. Existing methods mainly include relaxation methods or non-relaxation methods with huge computational cost. We notice that complex circle manifold (CCM) naturally satisfies the CMC. By projecting onto the CCM, the problem is transformed into an unconstrained minimization problem that can be addressed using the Riemannian gradient descent (RGD) algorithm. Furthermore, we notice that the RGD algorithm can be unfolded into a deep learning model. Hence, a computationally efficient method without relaxation, Iterative Deep Unfolding Network (IDUN), is proposed. First, this problem is converted into an unconstrained fourth-order polynomial minimization problem on the CCM. Then, by unfolding RGD algorithm as the network layer, IDUN is developed with adaptively learning the step sizes. Compared with existing methods, the proposed method has superior performance and less computational cost.

Federated Learning (FL) has been widely used to facilitate distributed and privacy-preserving machine learning in recent years. Different from centralized training that usually has independent and identically distributed (IID) distribution of all users' data, FL suffers from significant communication cost and model performance degradation due to the non-IID data from individual edge devices. Existing work calibrates the local models using a global anchor or sharing global data. However, these studies either assume that the central server has the global dataset or require participating devices to share raw data, which incurs additional communication costs and privacy concerns. In this paper, we proposeSlaugFL, a novel selective GAN-based data augmentation scheme for communication-efficient edge FL, which selects representative devices to share specific local class prototypes with the central server for GAN model training and improves FL performance with the trained GAN. Specifically, on the server side, we generate diverse labeled candidate data with the help of powerful generative models (the stable diffusion model and ChatGPT). To ensure that the GAN-generated data possesses a similar domain to the devices' local data, we leverage these selected local class prototypes to pick desired GAN training samples from the labeled candidate data. On the device side, we propose a dual-calibration approach consisting of two calibration manners. Concretely, we augment devices' non-IID data with the trained GAN model, where devices utilize the trained GAN model to generate the IID dataset. Thus, the device's local model can be directly calibrated with the augmented data. With the generated IID data, we yield privacy-free (p-f) global class prototypes which can be employed to further calibrate devices' local models. Combining these two calibrations effectively improves devices' local models. Extensive experimental results show thatSlaugFLcan significantly reduce the communication cost (up to 52.49%) while achieving the same accuracy, compared to the state-of-the-art work.

Nowadays, face recognition technology has been dramatically boosted by the advances in deep learning and big data fields. However, this also poses grand challenges in protecting personal identity information in intelligent applications of the Internet of Things (IoT). Existing methods based on the$K$-Same algorithm have low effectiveness for protecting personal identity while preserving face attributes. In this article, we propose an attribute-preserving face de-identification framework called Enhanced Embedded AutoEncoders to address this problem. Our framework consists of three parts: 1) a privacy removal network (PRN); 2) a feature selection network; and 3) a privacy evaluation network. The main purpose of our framework is to ensure that the PRN is capable of discarding information involving identity privacy and retaining desired face attributes for certain prediction applications. In order to achieve this goal, the design of the PRN is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. Extensive experimental results show that our framework outperforms existing methods by an average of 3.42%–26.22% in terms of data utility under comparable face de-identification performance, which indicates that the proposed framework can not only effectively retain face attributes but also protect personal identity well.

Synthetic aperture radar (SAR) plays an important role in remote sensing by providing electromagnetic images of the observation scene. The prior knowledge-based SAR image reconstruction method can reduce the requirement of data sampling ratio and improve image quality. The existing prior knowledge-based method usually uses the magnitude information of SAR images while ignoring the phase information. However, since the echo and backscattering coefficients are complex values, the phase information of SAR images will help improve the reconstruction accuracy in the image reconstruction process. To improve the reconstruction performance, this paper proposes a SAR image reconstruction and autofocus method using complex-valued feature prior. In the proposed method, the complex-valued feature prior is learned from data by a complex-valued feature projection operator (CFPO), which can characterize and extract scene features in Range-Doppler domain and 2D frequency domain. The proposed CFPO enables more efficient use of echo data and helps to improve image reconstruction and autofocus performance. In addition, the proposed method is implemented by an unfolded deep network, which enables data-driven feature learning and efficient computation. The proposed method is verified by simulated and measured data.

Despite its widespread use in Earth remote sensing, synthetic aperture radar (SAR) image reconstruction remains challenging. The difficulties mainly lie in the handling of diverse scenes and motion errors with sparsely sampled data. Existing matched filtering (MF)-based methods cannot handle sparsely sampled data, while regularization-based methods lack adaptability to scene diversity. Although deep learning-based SAR methods can deal with these two issues, their performance will be degraded by motion errors. To address this, we propose a Transformer-based SAR image reconstruction method called RATIR-Net. The proposed method can obtain SAR images of various scenes under sparse sampling and motion errors by learning the correlations between echo data. In RATIR-Net, CNN-based encoding and decoding blocks are constructed to implement azimuth processes of range profiles (RP) in the range-Doppler domain according to the MF-based method. Meanwhile, a Residual Attention Transformer (RAT) block is designed to extract correlations between RPs, compensating for information loss caused by sparse sampling and suppressing non-correlated perturbations caused by motion errors. The CNN-based encoding and decoding blocks help reduce computing costs, and the RAT block mitigates the dependence on scene features and the influence of motion errors. These make RATIR-Net efficient and effective. Simulation experiments have been conducted to verify the proposed method.

An active noise control (ANC) system generates a secondary sound to destructively interfere with the undesirable noise. Existing ANC algorithms are mainly designed to minimize the power of the residual sound, with few considerations to the listening experience. This results in a pressing issue in practice whereby the residual sound is perceived to be different from the undesirable noise. When the ANC system is deployed to reduce the noise level in a factory environment, workers may feel strange because they are used to detecting the anomaly machine sound by their auditory perception. In order to solve this problem, this paper proposes to integrate anomaly sound detection (ASD) into the ANC system in order for the residual sound to represent the same machine status as the original machine noise. The ASD module is used to simulate human judgement. A homothety constrained ANC algorithm is developed to synchronously reduce the sample-wise power and keep the segment-wise machine status of the residual sound. The experiment results validate the effectiveness of the homothety constrained ANC algorithm in noise reduction, and the subjective test results show that the ASD-integrated ANC system results in less confusing perceptions of the residual sound.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

Abstract Target parameter estimation is considered for the cooperative multiple‐input multiple‐output (MIMO) radar and MIMO communications system. To address the hardware limitation of the radar system, a joint transmitter selection and receiver placement (JTSRP) problem is formulated to minimise the estimation performance benchmarked by the Cramer–Rao bound (CRB), where the transmitters can be selected from a discrete set and the receivers can be deployed over a continuous region. To efficiently solve this mix‐integer non‐linear programming problem approximately, a genetic algorithm (GA)‐based method is proposed. It is shown that the result obtained by the proposed algorithm is close enough to the optimum solution of the JTSRP problem. Numerical examples are presented to analyse the performance of the cooperative system designed by the GA‐based JTSRP.

This letter proposes a differential error feedback active noise control (FBANC) system in an open end duct to mitigate interferences from its downstream. The interference waves entering the duct cause disturbance to the conventional FBANC controller and even result in divergence of the control filter. This is due to the omni-directivity of the error microphone. The differential microphone array (DMA) can be constructed in a compact size using only one more omni-directional microphone. The DMA forms a frequency-invariant beampattern that presents configurable nulls. When the output of the DMA is used as the error signal, the null can be designed to enhance the robustness of the FBANC system against interferences. However, this differential error signal is converted from the sound pressure gradient instead of the sound pressure. The DMA’s location does not precisely indicate the control point where optimum noise reduction has been achieved. To solve this problem, an auxiliary filter based mapping (AFMap) method is developed to map the differential error signal to the location of an omni-directional microphone in the DMA. Experiment results demonstrate that the proposed differential error FBANC system is much less sensitive to interferences than the conventional FBANC system, and the AFMap method can ensure optimum noise reduction occurring at the target control point.

Synthetic aperture radar (SAR) is an important means for target surveillance through reconstructing the microwave image of the observation area. However, under the condition of low signal-to-clutter ratio (SCR), such as a strong sea clutter situation, it is difficult to surveil targets from SAR images acquired by the traditional matched filter-based imaging methods. To improve the target surveillance performance of SAR, this article proposes a target-oriented SAR imaging method, which can enhance the desired target and improve the SCR in the reconstructed SAR images. By separating the target area from the clutter area, we first establish a target-oriented SAR imaging model, where the generalized regularization is used to characterize the features of the target, contributing to the improvement of SCR in the reconstructed image. Then, the imaging model is solved through a deep network, MF-ADMM-Net, which is obtained by unfolding an alternating direction method of multipliers (ADMM)-based iterative solution. In addition, the training strategy is formulated with the consideration of complex values. Experiments are conducted to verify the performance of image reconstruction and SCR improvement of the proposed method, and comparisons show the superiority of MF-ADMM-Net in effect and efficiency.

Synthetic aperture radar (SAR) can provide high-resolution electromagnetic backscattering images of the illuminated area, playing a significant role in various applications. However, achieving focused SAR images is challenging under sparse sampling and phase error conditions. By exploiting the sparsity or compressibility priors, the state-of-the-art sparsity-driven SAR imaging methods can reconstruct images under the condition of sparse sampling. However, the handcrafted priors used in these methods limit the imaging performance, and the iterative solution schemes reduce the computational efficiency. Besides, the measurement inaccuracy introduced by the phase error also degrades the reconstruction performance of the sparsity-driven imaging methods. To address these issues, a deep network for SAR autofocus imaging is proposed, which alternately performs image reconstruction and phase error estimation. When performing image reconstruction, the sparsity-cognizant total least-square (S-TLS) model is introduced to handle the problem of measurement inaccuracy, contributing to robust reconstruction performance under the condition of phase error. During the implementation of the deep network, a feature transform operator is used to realize data-driven prior knowledge learning and overcome the limitations of handcrafted priors. Moreover, the deep network approach can significantly improve computational efficiency. Experiments on simulated and real data verify the effectiveness and efficiency of the proposed method.

Traditional fingerprint-based positioning approaches work well on static data; they cannot handle scenarios where the data distribution, the feature space and even the signal source evolve over time, which are ubiquitous in real-world applications. One straightforward approach for circumventing these difficulties is to repeat labeled data calibration for maintaining an up-to-date fingerprint database, which is usually infeasible or expensive in large-scale indoor environments. In this paper, we propose a Long Short-Term indoor Positioning (LSTP) framework that enables adaptation at different time scales with low human-effort, and thus extends the effectiveness of existing fingerprinting techniques for a more generalized environment. Specifically, LSTP mainly considers the distribution discrepancy caused by continuous environmental dynamics in short-term positioning and the feature space heterogeneity in long-term positioning. To address the first challenge, we design an incremental ensemble localization model which leverages multiple source classifiers to resolve distribution differences in an online manner. To address the second challenge, we seek to borrow knowledge learned from an earlier time period with plenty of labeled samples for the current time period, thus reducing the required number of new calibration samples. By fully capturing the transferable spatial information across different time periods with multi-level constraints (sample, feature, and model levels), we can study a discriminative domain-invariant space from which we can make better predictions. The experiments on three real-world datasets demonstrate the superiority of the proposed framework, which outperforms the state-of-the-art systems by 18% in mean accuracy.

Transfer learning algorithms (TLAs) are often used to solve the distribution discrepancy issue in fingerprint-based indoor localization. However, existing TLAs cannot react well to real time changes in the environmental dynamics of the target space due to three remarkable shortcomings: a) redundant knowledge in source domain may lead to “negative transfer”; b) the required target domain samples to calculate the distributions are unrealistically feasible for real-time positioning; c) they cannot transfer knowledge efficiently across domains with heterogeneous feature spaces. In this paper, we propose TransLoc, a heterogeneous knowledge transfer framework for fingerprint-based indoor localization, which can perform knowledge transfer efficiently even with only one sample in the target domain. Specifically, we first refine the source domain according to the target domain by removing redundant knowledge in the source domain. Then, we derive a cross-domain mapping, which transfers the specific knowledge of one domain to another domain, to construct a homogeneous feature space. In this new feature space, the transfer weights are computed for training a classifier for target location prediction. To further train the framework efficiently, we combine the mapping and weights learning into a joint objective function and solve it by a three-step iterative optimization algorithm. Extensive simulation and real-world experimental results verify that TransLoc not only significantly outperforms state-of-the-art methods but is also very robust to changing environment.

The accurate estimation of the clutter covariance matrix (CCM) is crucial for space-time adaptive processing (STAP). In this letter, a new intrinsic cyclic characteristic of CCM is found. Then, a novel STAP is proposed based on the cyclic characteristic. In the proposed method, the cyclic CCMs, i.e., the temporal cyclic CCM, the spatial cyclic CCM, and the spatial-temporal cyclic CCM, are first constructed based on the cyclic characteristic. Then, the cyclic CCMs are employed as the secondary data, and the more accurate CCM estimation is obtained by averaging the cyclic CCMs and the estimated CCM of the existing STAP methods. Compared with the existing methods, the proposed method has the following advantages: (1) the proposed method can be directly combined with the various existing STAP methods to improve their performance, (2) the output signal-to-clutter-plus-noise ratio (SCNR) of the proposed method is 2.055 dB higher than that of the traditional STAP methods reported in [7]-[9], and (3) the output SCNR of the proposed method is 1.704 dB higher than that of the knowledge-aided STAP (KA-STAP) reported in [16].

The virtual sensing technique allows the active noise control (ANC) system to work with error microphones that are placed far from the desired zone of quietness (ZoQ). Conventionally, a training stage is required to obtain the auxiliary filters with the temporary error microphones placed in the ZoQ. When the characteristics of the primary noise changes, the auxiliary filters have to be retrained. As a result, the conventional virtual sensing technique can only be used when the frequency band of the primary noise remains unchanged. In order to solve this limitation, this paper proposes a selective virtual sensing technique for the multi-channel feedforward ANC system. The selective virtual sensing technique obtains a bank of auxiliary filters in the subband structure. Based on the frequency-band-matching mechanism, a linear combination of the auxiliary filters is calculated and used in the real-time control stage. Experimental results show that the selective virtual sensing technique achieves better noise reduction performance than the conventional virtual sensing technique when the frequency band of the primary noise fluctuates.

The main drawback for large-scale applications of WiFi-based localization is the varying characteristics of received signal strength (RSS), which degenerates the localization performance seriously. To mitigate the variation problem, we propose a hybrid fingerprint quality evaluation model (HFQuM) for accurate WiFi localization. HFQuM can intelligently determine the location of a user by evaluating the hybrid fingerprint quality in different subareas, that is a high fingerprint quality indicates that the frequently occurred location label is more likely to be true. To achieve this, in the offline phase, instead of only collecting RSS fingerprints, we construct a WiFi-based group of fingerprints (GOOFs) consisting of RSS, signal strength difference (SSD), and hyperbolic location fingerprint (HLF). Given an RSS testing sample of a user at an unknown location in the online phase, we first construct the multiple supporting sets (MSSs), including a sample space and a label space, selected by the similarity between the online sample and the GOOF. Based on the MSS, HFQuM is able to estimate the user's location as well as subareas and their hybrid fingerprint quality simultaneously by jointly modeling the process of generating the sample space and label space. To further reduce the computational complexity, HFQuM employs an access point (AP) selection algorithm to exclude redundancy APs. Experimental results in a typical library environment verify the superiority of HFQuM in terms of localization accuracy as compared with other existing fingerprint-based methods.

The sensing application of space surveillance has put forward challenges to the Internet of Things (IoT). However, current moving algorithms in IoT rarely aim for target surveillance. In view of energy efficiency for multimodal signals in IoT, this paper mainly investigate three typical target trajectories: 1) line; 2) square; and 3) circle. On a basis of target learning, two types of collaborative sensor movement algorithms are proposed and compared. One approach is based on genetic fuzzy tree (GFT) and the other is based on the neural network (NN). Both algorithms can balance the energy consumption and the tracking performance. Simulation results show that the GFT-based algorithm outperforms NN-based algorithm in tracking error, but it demands more computational cost than that of NN-based scheme. This important result can provide intellectual sensing support in IoT applications, such as target surveillance, anti-terrorism, and unmanned border awareness.

A new method based on the matrices reconstruction is proposed to deal with coherent signals in the presence of the unknown mutual coupling. By using a novel expression of the spatial covariance matrix in the presence of mutual coupling, the interference‐plus‐noise covariance matrix and the desired signal covariance matrix can be reconstructed via estimating the autocorrelation matrix of the signal envelope with unknown mutual coupling in an iteration process. Based on the criterion of the maximum output signal‐to‐interference‐plus‐noise ratio, a subspace orthogonal to the interference subspace can be then found out by using these reconstructed matrices. Therefore, the desired signal and the noise can be let out by mapping this estimated subspace to the observed data. Finally, an optimal weight vector can be obtained by maximising the output power of the desired signal. The performance of the proposed method is quite close to the optimal beamforming. The simulations demonstrate the effectiveness of the proposed beamformer.

Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, autonomous parking, virtual reality, context awareness, condition monitoring, geolocation, smart manufacturing, as well as smart cities. It is well known that indoor localization based on some single fingerprints is rather susceptible to the changing environment. The efficiency of building single fingerprints from one localization system is also low. Recently, we first proposed a group of fingerprints (GOOF) based localization to improve the efficiency of building fingerprints, and then proposed an efficient fusion algorithm, namely, multiple classifiers multiple samples (MUCUS), to improve the accuracy of localization. However, the main drawbacks of MUCUS are the low localization efficiency and low accuracy when all classifiers show poor performance simultaneously. In this paper, based on the aforementioned GOOF, we propose a sliding window aided mode-based (SWIM) fusion algorithm to balance the localization accuracy and efficiency. SWIM first adopts windowing and sliding techniques to improve the localization efficiency, and then obtains a more accurate estimate by minimizing the entropy of multiple classifiers or multiple samples. This can guarantee our estimator to be robust to changing environment and larger noise level. We demonstrate the performance of our algorithms through simulations and real experimental data via two universal software radio peripheral platforms.

In a sky-wave radar, the strong ocean clutter may cover up the echo signal of slow-speed targets. This letter proposed a knowledge-aided ocean clutter suppression method for the sky-wave radar. The proposed method uses the radar carrier frequency and the pulse repetition interval as prior knowledge to reconstruct the prior ocean clutter. This reconstructed clutter is combined with the ionosphere phase perturbation model. The resulting prior clutter is included in the optimal filter design. The simulation results show that the output signal-to-clutter plus noise ratio of this proposed method is 2.507 dB larger than the methods proposed by others.