Specific emitter identification (SEI) exploits device-dependent RF fingerprints to distinguish individual transmitters and is important for securing large-scale Internet-of-Things (IoT) deployments. While deep SEI can achieve near-perfect accuracy under same-day evaluation, real deployments rarely satisfy this assumption. At scale, per-day labeling is infeasible; models must therefore generalize from a labeled source day to an unlabeled target day, where day-to-day propagation drift induces distribution shifts and can substantially degrade performance under direct transfer (without adaptation). To address this challenge, we propose a unified unsupervised domain adaptation (UDA) framework for cross-day SEI that requires neither hardware calibration nor handcrafted features. The proposed objective integrates adversarial domain alignment, confidence-aware pseudo-labeling to exploit high-confidence target samples safely, and a cross-domain contrastive regularizer to preserve class-discriminative geometry. We further provide an analysis offering insight into how each component contributes to target-domain generalization. Experiments on two public RF benchmarks from different wireless technologies demonstrate robust cross-day performance across diverse transfers. On WiSig–ManySig, our method achieves 99.78% mean cross-day accuracy over six source-to-target day transfers, ranking best in five cases and remaining within 0.04% of the best in the remaining case. On a LoRa benchmark, it achieves 90.64% mean cross-day accuracy over ten day-transfer pairs, validating the framework beyond Wi-Fi and under larger transfer diversity.

Spectrum sensing plays a crucial role in fields such as sixth-generation (6G) wireless systems, Internet of Things (IoT), and cognitive radio. Blind signal detection is a crucial component of spectrum sensing. Current signal detection methods mainly focus on detection signal in a fixed single channel. Some multi-signal detection methods utilize spectrum for detecting multiple signals in broadband reception. However, the estimation of signal occupancy time exhibits poor performance. Some studies have noted that the Short-Time Fourier Transform (STFT) effectively characterizes the time-frequency occupancy properties of signals in electromagnetic space and have employed deep learning techniques to further enhance multi-signal sensing abilities. However, these methods directly apply image processing neural network models without considering the unique morphology of signals in spectrograms. In this article, we tackle the distinctive morphology of signals in spectrograms by introducing a Vertical-Horizontal Convolutional (VHConv) structure. This innovation aims to boost the feature extraction capacity of neural networks. We also introduce an attention-based decoder prediction head to enhance the receptive field of the prediction network and improve multi-signal detection performance. Based on comparative experiments, we find: 1) the implementation of the VHConv module notably improved signal detection precision and recall, achieving a notable Average Precision (AP) improvement from 29.9% to 67.7% at an 8dB Signal-to-Noise Ratio (SNR) when integrated into a convolutional neural network. 2) Utilizing a decoder prediction head networks enhanced the neural network’s capability in estimating time-frequency occupancy parameters. 3) Integrating decoder prediction head networks with Intersection over Union (IoU) loss during the training process significantly improved precision, achieving a 6.2% detection precision enhancement (from 80.3% to 86.5%) at recall rates above 80%. The experimental results demonstrate that the proposed method surpasses current methods, effectively enhancing the multi-signal detection capability of spectrum sensing systems.

In this paper, a decentralized iterative maximum likelihood (ML) direct position determination (DIM-DPD) algorithm is proposed based on the expectation maximization (EM) concept for user equipment (UE) localization in Wi-Fi systems. By innovatively treating the non-line-of-sight (NLoS) angles-of-arrival (AoAs) and observed times-of-arrival (ToAs) as nuisance parameters in the received signal model and parameter estimation procedure, the proposed DIM-DPD demonstrates its adaptability and localization efficiency in dense multipath indoor environments. In the proposed method, the position of the UE is incorporated in the vectors denoting the location differences between the UE and the access point (APs), referred to as the UE-AP location difference vectors. The set of UE-AP location difference vectors allows constructing the related UE-AP variables, serving as the latent variables in the EM iterations. Then, by taking advantage of the alternating projection technique, the nuisance parameters and UE-AP variables in the DIM-DPD algorithm are alternatively updated on separated APs in parallel. Furthermore, instead of traditional grid search, the UE position is updated with an efficient closed-form solution by aggregating the distributed estimated low-dimensional UE-AP variables. Thus, overall, the proposed DIM-DPD approach facilitates decentralized direct localization with implementation feasible processing complexity. The provided numerical simulation results demonstrate that the proposed DIM-DPD algorithm achieves high positioning accuracy, fast convergence, and a good balance between computational complexity and performance.

Automatic modulation classification (AMC) plays a pivotal role in the radio monitoring systems for Internet of Things (IoT) and spectrum management. Many contemporary deep-learning-based AMC methods overlook the effects of signal frequency offset and signal sampling rate jitter caused by Doppler effect and signal bandwidth estimate deviation, respectively. Moreover, these approaches struggle with recognizing unknown classes effectively. In this article, we introduce an innovative signal data augmentation strategy during the training of deep neural networks. The approach involves utilizing multiple time domain and frequency domain signal representations as inputs to the neural networks. Additionally, it incorporates a group classifier and group constraint mechanism to enhance the unknown class recognition ability of deep neural network. The study investigates two prominent neural network architectures: 1) the convolutional neural network (CNN) and 2) attention mechanism-based transformer. Based on the experiment results, it indicates that: 1) data augmentation and multiple domain representations improve classification accuracy when frequency offset and sampling rate jitter existence; 2) the convergence speed of the transformer architecture-based neural network is faster than CNN-based neural network, but the former is easier to overfitting; and 3) the discrimination ability of unknown class was improved obviously when the neural network uses group classifier and training with group constraint. Experimental results also demonstrate that the proposed methods enhanced the ability of blind signal modulation recognition in radio monitoring systems.

Linear array-based three-dimensional (3-D) localization is a recently proposed technology. It uses a set of newly defined one-dimensional (1-D) angles, called space angle (SA), to locate the source. Integrating SA with time difference of arrival (TDOA) promises higher accuracy, and more attractive, provides the formulation leading to closedform solutions. Currently, studies of hybrid SA-TDOA localization leverage iteration to refine the performance to the Cramer-Rao lower' bound (CRLB), which suffers from the possible convergence issues. This paper advances this topic by proposing a two-stage weighted least squares (TSWLS) closed-form estimator, which avoids the risk of divergence and can attain the CRLB if the noise is mild. Analytical and numerical results show the performance ascendancy of the proposed method.

Radar point cloud registration is vital for various applications in autonomous driving, robotics, and localization. However, existing methods have not adequately accounted for weakly structured texture scenes in urban environments, such as long-distance monotonous walls and open roads, which are prevalent in urban environments. Failures in point cloud registration in these scenarios can significantly affect the performance of the aforementioned applications. In order to improve the registration accuracy in weakly structured texture scenes of urban environments, we propose a novel mismatch elimination and an improved sample consensus registration method. The experimental results demonstrate that the proposed method achieves higher registration accuracy in weakly structured texture scenes than the compared methods.

The traditional direct position determination (DPD) for multiple targets usually requires transmitting raw data to the fusion center (FC), which occupies large transmission bandwidth and hardware resource. To solve this problem, we adopt one-bit analog-to-digital converters (ADCs) for a distributed subarray (DS) system, and propose an one-bit DPD method with multiple signal classification (1-bit DPD-MUSIC). The method approximates the one-bit signal as a scaled infinite-bit signal with a quantization error. Thus, the MUSIC algorithm can be straightforwardly applied without extra preprocessing. Simulations demonstrate that the proposed method saves large bandwidth and power with a slight loss of performance compared with the infinite-bit one. Moreover, 1-bit DPD-MUSIC is computationally efficient and outperforms the one-bit DPD based on maximum likelihood estimate (MLE) one for the close targets.

The traditional one-bit direct position determination (DPD) based on zero-thresholding quantization (ZQ) scheme suffers from remarkable performance degradation. To solve this problem, this paper proposes a Rao-test-based method for the one-bit signal based on time-varying quantization (TQ) scheme (TQ-one-bit Rao test, TQ-OBRT) to detect and locate the target simultaneously. We derive the one-bit Cramér-Rao Lower bound (CRLB) based on TQ scheme. Then, we obtain the optimal quantization (OQ) scheme by the minimizing the one-bit CRLB, and introduce a random quantization (RQ) scheme without iterative processing. Simulation results show that the performance of the proposed OQ and RQ schemes is superior to that of the conventional ZQ scheme.

The advancement of autonomous driving technology has driven a surge of applications in urban environments, where the precision of the registration of keypoint-based radar point clouds plays a crucial role in determining the overall performance of these applications. However, numerous inaccurate keypoints would be detected in the presence of multipath, beam spread, and noise, leading to the reduction of registration accuracy of the radar point cloud. To deal with this problem, we propose an improved keypoint detection method, where a novel candidate keypoint selection strategy and a threshold selection strategy are designed. The results of experiments based on a public radar dataset demonstrate that the proposed method can effectively reduce the number of inaccurate keypoints. Moreover, it achieves high precision on radar point cloud registration compared to the state-of-the-art method.

The Direct Position Determination (DPD) methods are widely employed in ditributed radar systems, especially under low signal-to-noise ratio (SNR) scenarios. However, the DPD methods usually require exhaustive search resulting in insufferable computational complexity. In this paper, a fast DPD based on particle group optimization (termed PSO-DPD) is proposed to solve this problem under Neyman-Pearson (NP) criteria. Specifically, the proposed method formulates the D-PD problem as a non-convex optimization problem and then solves this problem by employing the PSO method. Further-more, our algorithm is considered under the assumption that each node is spatially coherent and noncoherent, respectively. Simulation results demonstrate that the proposed algorithm reduces computational complexity rapidly compared with the exhaustive-search one. In addition, the positioning accuracy of our method is comparable to the exhaustive-search one.

Abstract Phase retrieval (PR) of block‐sparse signals is a new branch of sparse PR that causes rising research, which focusses with methods owing a high successful rate. However, the recovery performances of existing methods for block sparsity are usually unfit for large‐scale problems with unacceptable compute complexity. We derive an algorithm for PR of block sparsity via variational Bayesian learning with expectation maximisation to mitigate this drawback. In the proposed algorithm, the block‐sparse structure is modelled by the hierarchical constructional priors with a novel adaptive coupled pattern, which provides a strong relationship between the neighbour blocks. Simulations indicate that the proposed algorithm outperforms the existing methods in success rate, noise‐robustness, and signal detection rate in large‐scale cases with acceptable computation complexity.

Locating a device is a basic element for many Internet of Things (IoT) applications. In particular, it often demands an algorithm having low complexity to limit the energy consumption and most important, sufficient robustness without knowing the device in the near-field for point localization or in the far-field for direction of arrival (DOA) estimation. This article proposes a new localization algorithm that can achieve the two purposes, with the theoretical analysis to validate the optimal accuracy and the real data experiment to support the promising performance. The first objective is achieved by a closed-form solution and the second is accomplished by using the modified polar representation (MPR) of the source position, based on a new formulation for the localization problem. While the MPR localization method has been introduced before, it is not sufficiently robust for IoT application to handle the large equal radius (LER) scenario or the presence of sensor position errors. The proposed algorithm uses a different MPR formulation, which is able to handle the LER scenario, sensor position errors, and has low computational complexity.

This paper focuses on the three-dimensional (3-D) source localization problem using one-dimensional (1-D) angle of arrival (AOA) and time difference of arrival (TDOA) measurements. A weighted least square (WLS) estimate is proposed by exploiting the pseudo-linearization technique for this novel scheme. This method provides a closed-form solution of the source position. Then, the Gauss-Newton method is applied to improve the accuracy further. The Cramer-Rao lower bound (CRLB) is derived to evaluate the performance. Simulation results show that the performance of the proposed localization algorithm is close to the CRLB in small measurement noise region.

Joint angle and delay estimation (JADE) has become a key technique for accurate localization in dense multipath indoor scenarios. This paper addresses the problem of JADE for multiple reflections of a multi-carrier signal impinging on multiple-antenna system. Inspired by the Minimum Variance Distortionless Response (MVDR), we propose an efficient spectral method for this problem. The proposed method is a high resolution estimator which only need two-dimensional search. Unlike the maximum likelihood JADE estimator, the number of paths is not required known a prior. Furthermore, the smoothing technique and diagonal loading technique are exploited to construct reasonable sample covariance matrix. Simulation results using the IEEE 802.11ac standard's setup parameters are provided to validate the proposed method's effectiveness. At a low or medium signal-to-noise ratio (SNR), the performance of the proposed method is superior to the MUSIC-JADE algorithm with path number estimation.

It is well known that the Direct Position Determination (DPD) methods outperform the two-step methods at low Signal to Noise Ratio (SNR). The better robustness of DPD approach to the noise comes from its signal-level constraint, i.e., the received signals of different receivers come from the same transmitter. Based on the idea that the more strict DPD constraint will also improve the robustness to the Multi-Path (MP) interference in indoor environment, we derive the conventional DPD estimator for the indoor localization. Furthermore, considering the power of MP interference in beam space may be larger than that of Direct-Path (DP) for the unequal beam gain of hybrid antenna array, we improve our DPD estimator to suppress the false alarm caused by the stronger MP interference. The simulation results indicate that the improved DPD estimator is more robust to the MP interference, and compared with the two-step methods, the proposed DPD estimators have better positioning accuracy.

Images from different aspect views for one target, known as multiple views, are widely applied to improve synthetic aperture radar (SAR) target recognition. However, most of existing multi-view methods have strict constraint on the angle interval among multiple views. In this paper, a new multiview fusion method free from interval limitation using expectation maximization (EM) is explored for SAR image classification. Firstly, we apply convolutional neural network (CNN) to extract features effectively owning to its powerful ability of feature learning and then obtain the classification probability. Secondly, Multi-view Label Set (MLS) is automatically constructed from multiple views according to the probability and finally we use EM algorithm to classify SAR images intelligently. It is worth noting that the proposed method can be used flexibly according to the number of perspectives obtained and without angle interval constraint among multiple views. Experiments demonstrate that the proposed method has better recognition performance than some state-of-the-art methods on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

The estimation accuracy of time difference of arrival (TDOA) and frequency difference of arrival (FDOA)is determined by the frequency and time distribution, signal energy and noise. TDOA estimation is mainly decided by frequency distribution, while FDOA estimation generally relies on time distribution. Frequency‐hopping (FH) signals have broad distribution in frequency and time domains, the problem of low estimation accuracy for TDOA and FDOA of single‐hop signal can be overcome through multi‐hop coherent integration to improve the use of efficient bandwidth and duration. Focus on the TDOA/FDOA estimation problem of FH signals; this study proposes a high accuracy TDOA/FDOA estimation algorithm, which will conduce high precision of localisation. The cross‐ambiguity function (CAF) of single‐hop baseband signal is first analysed, then the CAF of each hop signal is deduced, and the phase relationship of each CAF is revealed through TDOA and FDOA dimensions. Coherent integration is realised by FDOA normalised compensation and phase compensation. The theory performances of TDOA/FDOA estimation for FH signals are derived, and the influence of the signal parameters on TDOA/FDOA estimate accuracy is indicated. Monte Carlo simulations validate that the performance of proposed coherent integration TDOA/FDOA estimation algorithm for FH signals gets greater improvements than that of single‐hop signal, and the performance improvements accord with the theory analysis well.

It is well known that the direct position determination (D-PD) method outperforms the most common two-step localization method when the signal-to-noise ratio (SNR) is low. The advantage comes from the fact that the DPD method avoids estimating the intermediate parameters for localization. However, the DPD method has heavy computation load because of depending on exhaustive searching, especially in the multiple sources localization scenario where the problem is a high-dimensional optimization problem. In this paper, we constructed a cost function using the orthogonal relationship between received signals and noise. We reveal that the nature of the cost function is to verify the column-correlation of the matrix. Finally, we got the Determinant-based cost function which is more efficient and requires less computation resources in searching phase.

Time difference of arrival (TDOA) point positioning in the Cartesian coordinates is practical for a near-field source, and it will suffer from the thresholding effect when the source is in the far-field where only direction of arrival (DOA) can be obtained. Localization in the modified polar representation (MPR) is able to alleviate this problem, where point positioning and DOA estimation are unified into a single framework. The state-of-the-art literature only has an iterative realization of the maximum likelihood estimator (MLE) for this problem. This paper develops an algebraic closed-form positioning solution for MPR. The proposed algorithm avoids the initialization issue and is much more computationally efficient than the MLE with comparable accuracy. Simulation results validate the advocated performance.

This paper investigates time-of-arrival (TOA) source node self-positioning with unknown clock skews in wireless sensor networks. For the source-to-anchor direction, source node clock skew does not affect the localization performance. When synchronized anchor nodes simultaneously transmit signals to a source node, the source node clock skew will degrade the localization performance. A semidefinite programming (SDP) algorithm that jointly estimates the source position and clock skew is proposed for the latter case. The proposed algorithm is better than the two kinds of existing schemes, namely, asynchronous TOA localization and TDOA localization. We also tune the algorithm to the case of anchor nodes position uncertainties. Simulation results validate the performance of the proposed algorithm.

In this paper, two novel robust widely linear beamforming algorithms based on the technique of shrinkage are proposed, i.e., the WL-RBLW and the WL-OAS. Firstly, in order to remove the signal-of-interest's (SOl's) component from the sample covariance matrix (SCM), the augmented interference-plus-noise covariance matrix (A-IPNCM) is reconstructed based on the spatial spectrum of noncircular coefficient. Then, a modified Rao-Blackwell Ledoit-Wolf (RBLW) estimator and a modified Oracle Approximating Shrinkage (OAS) estimator are developed to directly estimate the desired signal's extended steering vector. Only the prior knowledge of the antenna array geometry and the angular sector in which the desired signal is located are utilized in the proposed algorithms. Compared with several representative robust WL beamformers, numerical simulations demonstrate that the proposed beamformers can achieve a better performance.

The most state-of-art time-difference-of-arrival (TDOA) localization algorithms are performed under the assumption that all the nodes are synchronized. However, for a widely distributed wireless sensor networks (WSNs), time synchronization between all the nodes is not a trival problem. In this paper, we study the problem of source localization using signal TDOA measurements in the system of nodes part synchronization. Starting from the maximum likelihood estimator (MLE), we develop a semidefinite programming (SDP) approach. Besides, we extend the SDP algorithm to the case of non-accurate sensor position. Simulation results validate the localization performance of the proposed SDP algorithms.

Random phase noises arising from frequency jitter of transmit signal and atmospheric turbulence result in corrupted synthetic aperture radar (SAR) imagery, which in turn degrades change detection (CD) performance. In this paper, a phase retrieval (PR) based SAR reconstruction autofocus framework by exploiting the hidden convexity is proposed with the goal of achieving reliable persistent surveillance CD. Firstly the original non-convex quartic SAR reconstruction is reformulated as a convex quadratic program. Under the minimum phase assumption, the auto-correlation retrieval- Kolmogorov factorization (CoRK) algorithm is then utilized to optimally and efficiently retrieve the underlying SAR reflectivity. The devised scheme possesses effective capabilities of phase noise mitigation, thus has a superior CD performance. Experimental results are provided to verify the effectiveness of the proposed method.

A new joint synchronization and localization method for wireless sensor networks using two-way exchanged timestamps is proposed in this paper. The goal is to jointly localize and synchronize the source node, assuming that the locations and clock parameters of the anchor nodes are known. We first form the measurement model and derive the Cramér-Rao lower bound (CRLB). An analysis of the advantages and disadvantages of a recent scheme on joint synchronization and localization motivates us to develop a maximum likelihood estimator (MLE) that effectively resolves the issues of this existing scheme. A novel semidefinite programming method is then proposed to transform the nonconvex MLE problem into a convex optimization problem. Extensive simulation results are obtained to compare the synchronization and localization performances of proposed scheme and a few state-of-the-art existing schemes.

The robust adaptive beamforming (RAB) problem for noncircular signals with the desired signal's steering vector (SV) mismatch is considered. As we know, noncircular signals are widely used in the satellite communication and radio communication. Some existing approaches for estimating the extended SV of the desired signal are based on the second-order cone programming (SOCP), which results in a high computational cost. In this paper, we propose a novel robust widely linear (WL) beamforming algorithm by using a low-complexity shrinkage-based approach. The augmented interference-plus-noise covariance matrix (IPNCM) is reconstructed first by using the SVs corresponding to the interference region. Then, a modified oracle approximating shrinkage (OAS) method is applied to estimate the desired signal's extended SV. Only the prior knowledge of the antenna array geometry and the angular sector in which the desired signal is located are utilized in the proposed method. Numerical simulations show that the proposed algorithm outperforms the existing robust WL beamforming methods.

In this paper, we consider using time-of-arrival (TOA) measurements from single moving receiver to locate a stationary source which emits periodical signal. First, we give the TOA measurements model and deduce the Cramér-Rao lower bounds (CRLB). Then, we formulated the maximum likelihood estimation (MLE) problem. We use the semidefinite programming (SDP) method to relax the nonconvex MLE problem into convex problem. It is shown that the original SDP algorithm can not provide a high-quality solution. We jointly add second-order-cone (SOC) constraints and penalty term to improve the tightness of the original SDP algorithm. Besides, we also consider the presence of receiver position errors, and develop the robust localization algorithm. Numerical simulations are conducted to demonstrate the localization performance of the proposed algorithms by comparing with the CRLB.

This paper proposed a novel weighted multidimensional scaling (MDS) estimator for estimating the position of a stationary emitter with sensor position uncertainties using time‐difference‐of‐arrival measurements. The solution is closed form and unbiased. It is shown analytically to achieve the Cramer–Rao lower bound performance in small noise region. Simulation results show that the proposed estimator offers smaller bias and mean square error than the two‐step weighted least square approach and traditional MDS estimator ignoring sensor position uncertainties at moderate noise level. Additionally, the computation complexities of them are comparable.

The classic two‐step approach for time difference of arrival (TDOA) geolocation is suboptimal since the TDOA measurements have not followed the constraint that all measurements should be consistent for a geolocation of a single emitter. In this study, the direct TDOA geolocation approach is proposed for frequency‐hopping (FH) emitters. It makes use of the sparsity of the FH signals in frequency domain, and constructs a cross correlation function (CCF) matrix in frequency domain, then the location estimate is obtained by searching the maximum eigenvalue of the CCF matrix in a two dimensional grid. The Cramer–Rao lower bound has been derived. The resolution for a single FH signal geolocation is also analysed. Further, an extension of the new method for multiple FH emitters direct TDOA geolocation has been presented. The performance comparison between the direct approach and the conventional two‐step method has been made by simulations. The results demonstrated that the proposed method outperforms the conventional two‐step method. The simulations also demonstrated the effectiveness of the new method in locating multiple FH emitters.

In this paper, we propose a novel greedy algorithm referred to as look-ahead hybrid matching pursuit (LAHMP) for multipolarization through-wall radar imaging (TWRI). From the viewpoint of compressive sensing, the task of multipolarization TWRI can be formulated as a problem of sparsity pattern recovery under the joint sparsity model. A newly developed greedy algorithm for joint sparsity model, hybrid matching pursuit (HMP), combines the strengths of orthogonal matching pursuit and subspace pursuit and improves the accuracy of the sparsity pattern recovery. Besides, the look-ahead strategy can select an optimal atom by evaluating its effectiveness on the overall reconstruction quality. Through integrating the virtues of HMP with the look-ahead strategy, the proposed LAHMP aims to more accurately select atoms corresponding to the true targets behind walls. Experiments based on measured radar data show that, compared to existing greedy algorithms, LAHMP provides better image quality at affordable expense of computational complexity.

In this paper, the completion of missing measurements in a squared distances matrix through Nystrom completion algorithm has been investigated. This missing occurred due to limitation of power when the sensors are deployed in a large area. The Nystrom algorithm has overcome the classical multidimensional scaling in a low and moderate signal to noise ratio, in addition it performs well as the number of missing entries increase. The plotted figures show admissible consequences for the proposed algorithm.

This letter focuses on locating passively a point source in the three-dimensional (3D) space, using the hybrid measurements of time difference of arrival (TDOA) and angle of arrival (AOA) observed at two stations. We propose a simple closed-form solution method by constructing new relationships between the hybrid measurements and the unknown source position. The mean-square error (MSE) matrix of the proposed solution is derived under the small error condition. Theoretical analysis discloses that the performance of the proposed solution can attain the Cramér-Rao bound (CRB) for Gaussian noise over the small error region where the bias compared to variance is small to be ignored. The proposed solution can be extended directly to more than two observing stations with CRB performance maintained theoretically. Simulations validate the performance of the proposed method.

This work addresses the direction-of-arrival (DOA) estimation issue with multiple noncoherent subarrays. We use a maximum likelihood approach to derive a weighted MUSIC (w-MUSIC) algorithm for such arrays, which obtains the overall spatial spectrum via combining the weighted MUSIC spectrum of the subarrays. Theoretical analysis and numerical examples demonstrate that the w-MUSIC algorithm has a better performance compared to a previously introduced MUSIC algorithm for noncoherent subarrays.

Mobile station (MS) localisation that plays an important role in the process of target continuous localisation has received considerable attention. In this study, a new framework based on subspace approach for positioning an MS at minimum localisation system with the use of time-of-arrival measurements is introduced. Unlike ordinary multidimensional scaling algorithm using eigendcomposition or inverse computation to estimate the MS position, a computationally simple weighting estimator is proposed by introducing Lagrange multiplier and mean-square error weighting matrix. Computer simulations are included to corroborate the theoretical development and to contrast the estimator performance with several conventional algorithms as well as the Cramér–Rao lower bound (CRLB). It is shown that the new method with low computational complexity attains the CRLB for zero-mean white Gaussian range error at moderate noise level.

Mobile station (MS) localisation, which plays an important role in the process of target continuous localisation, has received considerable attention. In this study, a new framework based on subspace approach for positioning an MS at three or more base stations (BSs) with the use of time-of-arrival (TOA) measurements is introduced. It is shown that the proposed approach is a generalisation of the mobile localisation method based on multidimensional scaling (MDS) analysis. Through computer simulations and computational complexity analysis, the authors can see that the proposed algorithm has a comparable performance with conventional MDS localisation method, however, the computational complexity has been greatly reduced.

Impulse response of multi-path channel can be estimated by using a short training sequence when the channel is sparse. Though the ordinary orthogonal matching pursuit (OMP) provides fast sparse multi-path channel (SMPC) estimation, it suffers from inter-atom interference (IAI), especially in the case of SMPC with a large delay spread and short training sequence. Herein, an adaptive IAI mitigation method is proposed to improve OMP algorithm based on a sensing dictionary, which is utilized to prevent false atoms from being selected due to serious IAI. Numeral experiments illustrate that the improved OMP algorithm based on adaptive IAI mitigation outperforms both the ordinary OMP algorithm.

An improved direction-of-arrivals (DOAs) estimation via phase information of sparse solution is presented in this paper. Unlike the conventional sparse source localization approach using the amplitude of sparse solutions only, through a special partition of the receiving data of the sensors, the phase information of the available sparse solutions is also extracted to estimate DOAs. For the true DOAs exactly on the grids which are used to generate the over-complete dictionary, the performance of our method is close to the conventional sparse source localization method. For the true DOAs that are not on the grids, our method is far superior to the conventional method, as demonstrated by several simulation results.

The true Cramer-Rao lower bound (CRB) for the time delay estimation has been obtained for narrowband signals with the carrier phase as a deterministic parameter already. However, the carrier phase is usually a random parameter in noncoherent receiver in the applications such as radar, sonar and communication systems. And accuracy of the time delay estimation is affected by this nuisance carrier phase. In this paper, the true Cramer-Rao lower bound for the time delay estimation is derived and analyzed in the presence of a random carrier phase. The new bound is tighter than the one obtained under the condition that the carrier phase is not random. We show that this relation indicates the influence of the random carrier phase on the true CRB, and the penalty resulting from this random carrier phase increases severely with decreasing signal-to-noise ratio. The explanation about this influence is also given from the point of information theory. Simulations are provided to support the theoretical results.

We demonstrate that the objective function of a basis selection problem in Laplacian noise environment falls into the framework of support vector regression (SVR), and, by iteratively solving a convex quadratic programming (QP) problem that guarantees a globally optimal solution, the sparse solution to the inverse problem can be found. The effectiveness of the proposed algorithm is verified via the application to direction-of-arrival (DOA) estimation. Different from the existing DOA estimation method based on SVR, the proposed algorithm is applicable with single snapshot and does not have to know the number of the sources. Meanwhile, the method does not require a large number of training sets, which in turn decreases the computational complexity.

A novel noise subspace based method is applied to the minimum localization system using time-of-arrival (TOA) measurements from three base stations (BS). Since the distance measurement between the mobile station (MS) and the BS bears analogy to the multidimensional similarity (MDS) between their coordinates, we express the MS coordinate as the linear combination of the BSs' coordinates, where the weight vector lies in the noise subspace of the MDS matrix. It is proved that this weight vector is the area coordinate of the MS when the triangle formed by the three BSs serves as the reference frame. Because the dimension knowledge of the localization problem is utilized to estimate the noise subspace and to mitigate the errors in TOA measurements, the proposed method is superior to the ordinary linear localization method in most of the enhanced quadrants of the area coordinates system.

This paper considers the scattering environment and demonstrates that we can solve the localization problem in the case of non-line-of-sight if we could separate the multipath components with the same absolute value of Doppler-shifted frequency and estimate accurate time and direction of arrival of each multi-path component. Using a Doppler symmetric constraint on the angles between moving direction of the mobile station and line-of-sight of scatterers, a novel set of localization equations is established and the target position is estimated by a 2D searching method. Simulation results show that the proposed method is superior to the conventional localization method by taking advantage of the environment information.