This letter investigates the circularity of fractional Fourier transform (FRFT) coefficients containing noise only, and proves that all coefficients coming from white Gaussian noise are circular via the discrete FRFT. In order to use the spectrum kurtosis (SK) as a Gaussian test to check if linear frequency modulation (LFM) signals are present in a set of FRFT points, the effect of the noncircularity of Gaussian variables upon the SK of FRFT coefficients is studied. The SK of the α th-order FRFT coefficients for LFM signals embedded in a white Gaussian noise is also derived in this letter. Finally the signal detection algorithm based on FRFT and SK is proposed. The effectiveness and robustness of this algorithm are evaluated via simulations under lower SNR and weaker components.

The cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter has emerged as a promising tool for tracking a time-varying number of targets. However, the standard CBMeMBer filter may perform poorly when measurements are coupled with sensor biases. This paper extends the CBMeMBer filter for simultaneous target tracking and sensor biases estimation by introducing the sensor translational biases into the multi-Bernoulli distribution. In the extended CBMeMBer filter, the biases are modeled as the first order Gauss-Markov process and assumed to be uncorrelated with target states. Furthermore, the sequential Monte Carlo (SMC) method is adopted to handle the non-linearity and the non-Gaussian conditions. Simulations are carried out to examine the performance of the proposed filter.

At the stage of software debugging, the effective interaction between software debugging engineers and fault localization techniques can greatly improve fault localization performance. However, most fault localization approaches usually ignore this interaction and merely utilize the information from testing. Due to different goals of testing and fault localization, the lack of interaction may lead to the issue of information inadequacy, which can substantially degrade fault localization performance. In addition, human work is costly and error-prone. It is vital to study and simulate the pattern of debugging engineers as they apply their knowledge and experience to this interaction to promote fault localization effectiveness and reduce their workload. Thus this paper proposes an effective fault localization approach to simulate this interaction via feedback. Based on results obtained from fault localization techniques, this approach utilizes test data generation techniques to automatically produce feedback for interacting with these fault localization techniques, and then iterate this process to improve fault localization performance until a specific stopping condition is satisfied. Experiments on two standard benchmarks demonstrate the significant improvement of our approach over a promising fault localization technique, namely the spectrum-based fault localization technique.

With the aim to improve the effectiveness of SAWSDL service discovery, this paper proposes a novel discovery method for SAWSDL services, which is based on the matchmaking of so-called fine-grained data semantics that is defined via sets of atomic elements with built-in data types. The fine-grained data semantics can be obtained by a transformation algorithm that decomposes parameters at message level into a set of atomic elements, considering the characteristics of SAWSDL service structure and semantic annotations. Then, a matchmaking algorithm is proposed for the matching of fine-grained data semantics, which avoids the complex and expensive structural matching at the message level. The fine-grained data semantics is transparent to the specific data structure of message-level parameters, therefore, it can help to match successfully similar Web services with different data structures of parameters. Moreover, a comprehensive measure is proposed by considering together several important components of SAWSDL service descriptions at the same time. Finally, this method is evaluated on SAWSDL service discovery test collection SAWSDL-TC2 and compared with other SAWSDL matchmakers. The experimental results show that our method can improve the effectiveness of SAWSDL service discovery with low average query response time. The results imply that fine-grained parameters fit to represent the data semantics of SAWSDL services, especially when data structures of parameters are not important for semantics.

In this letter, a new analysis technique for finding the convexity of iterative maximum likelihood (IML) methods for direction-of-arrival (DOA) estimation is presented. The proposed technique can pave the way in avoiding the local solution when the IML methods are utilized to estimate DOA, especially for the scenarios of array with large antennas. From the derivation, we can see that as long as the initial DOA belongs to the approximate convex range estimated by our proposed technique, the IML methods can estimate the DOA very well without entering into local minima, which is particularly true for the large arrays. Furthermore, numerical experiments show us the results tallied well with our theoretical derivations.

The concept of dual function radar-communication (DFRC) provides solution to the problem of spectrum scarcity. This paper examines a multiple-input multiple-output (MIMO) DFRC system with the assistance of a reconfigurable intelligent surface (RIS). The system is capable of sensing multiple spatial directions while serving multiple users via orthogonal frequency division multiplexing (OFDM). The objective of this study is to design the radiated waveforms and receive filters utilized by both the radar and users. The mutual information (MI) is used as an objective function, on average transmit power, for multiple targets while adhering to constraints on power leakage in specific directions and maintaining each user’s error rate. To address this problem, we propose an optimal solution based on a computational genetic algorithm (GA) using bisection method. The performance of the solution is demonstrated by numerical examples and it is shown that, our proposed algorithm can achieve optimum MI and the use of RIS with the MIMO DFRC system improving the system performance.

In this work, the differential received signal strength based localization problem is addressed. Based on the measurement model, we present the constrained weighted least squares (CWLS) approach, which is difficult to be solved directly due to its nonconvex nature. However, by performing the semidefinite relaxation (SDR) technique, the CWLS problem can be relaxed into a semidefinite programming problem (SDP), which can be efficiently solved using modern convex optimization algorithms. Moreover, the SDR is proved to be tight, and hence ensures the corresponding SDP find the optimal solution of the original CWLS problem. Numerical simulations are included to corroborate the theoretical results and promising performance.

Recently, Gan and Luo have proposed a direction-of-arrival estimation method for uncorrelated and coherent signals in the presence of multipath propagation [3]. In their method, uncorrelated and coherent signals are distinguished by rotational invariance techniques and the property of the moduli of eigenvalues. However, due to the limitation of finite number of sensors, the pseudo-inverse matrix derived in this method is an approximate one. When the number of sensors is small, the approximation error is large, which adversely affects the property of the moduli of eigenvalues. Consequently, the method in [3] performs poorly in identifying uncorrelated signals under such circumstance. Moreover, in cases of small number of snapshots and low signal to noise ratio, the performance of their method is poor as well. Therefore, in this letter we first study the approximation in [3] and then propose an improved method that performs better in distinguishing between uncorrelated signals and coherent signals and in the aforementioned two cases. The simulation results demonstrate the effectiveness and efficiency of the proposed method.

This paper aims to design multiple-input multiple-output (MIMO) radar receiving weights and transmitting waveforms, in order to obtain better spatial filtering performance and enhance the robustness in the case of signal-dependent interference and jointly inaccurate estimated angles of target and interference. Generally, an alternate iterative optimization algorithm is proposed for the joint design problem. Specifically, the receiving weights are designed by the generalized eigenvalue decomposition of the matrix which contains the estimated information of the target and interference. As the cost function of the transmitting waveform design is fractional, the fractional optimization problem is first converted into a secondary optimization problem. Based on the proposed algorithm, a closed-form solution of the waveform is given using the alternating projection. At the analysis stage, in the presence of estimated errors under the environment of signal-dependent interference, a robust signal-to-interference and noise ratio (SINR) performance is obtained using a small amount of calculation with an iterative procedure. Numerical examples verify the effectiveness of the performances of the designed waveform in terms of the SINR, beampattern and pulse compression.

We propose a novel approach to the target localization problem using Doppler frequency shift measurements. We first reformulate the maximum likelihood estimation (MLE) as a constrained weighted least squares (CWLS) estimation, and then perform the semidefinite relaxation to relax the CWLS problem as a convex semidefinite programming (SDP) problem, which can be efficiently solved using modern convex optimization methods. Finally, the SDP solution can be used to initialize the original MLE which can provide estimates achieve the Cramer-Rao lower bound accuracy. Simulations corroborate the good performance of the proposed method.

In this work, we address the stationary target localization problem by using Doppler frequency shift (DFS) measurements. Based on the measurement model, the maximum likelihood estimation (MLE) of the target position is reformulated as a constrained weighted least squares (CWLS) problem. However, due to its non-convex nature, it is difficult to solve the problem directly. Thus, in order to yield a semidefinite programming (SDP) problem, we perform a semidefinite relaxation (SDR) technique to relax the CWLS problem. Although the SDP is a relaxation of the original MLE, it can facilitate an accurate estimate without post processing. Simulations are provided to confirm the promising performance of the proposed method.

Target determination based on Doppler frequency shift (DFS) measurements is a nontrivial problem because of the nonlinear relation between the position space and the measurements. The conventional methods such as numerical iterative algorithm and grid searching are used to obtain the solution, while the former requires an initial position estimate and the latter needs huge amount of calculations. In this letter, to avoid the problems appearing in those conventional methods, an effective solution is proposed, in which two best linear unbiased estimators (BULEs) are employed to obtain an explicit solution of the proximate target position. Subsequently, this obtained explicit solution is used to initialize the problem of original maximum likelihood estimation (MLE), which can provide a more accurate estimate.

In this paper, the problem of target detection and tracking utilizing the single frequency network (SFN) is addressed. Specifically, by exploiting the characteristics of the signal in SFN, a novel likelihood model which avoids the measurement origin uncertain problem in the point measurement model is proposed. The particle filter based track-before-detect (PF-TBD) algorithm is adopted for the proposed SFN likelihood to detect and track the possibly existed target. The advantage of using TBD algorithm is that it is suitable for the condition of low SNR, and specially, in SFN, it can avoid the data association between the measurement and the transmitters. The performance of the adopted algorithm is examined via simulations.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l0-norm minimization by l1-norm minimization, our method designs a reweighted l1 norm to substitute the l0 norm. The capability of the reweighted l1 norm to bridge the gap between the l0- and l1-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l1-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

Spatial compressive sensing (SCS) has recently been applied to direction-of-arrival (DOA) estimation, owing to its advantages over conventional versions. However the performance of compressive sensing (CS)-based estimation methods degrades when the true DOAs are not exactly on the discretized sampling grid. We solve the off-grid DOA estimation problem using the deterministic maximum likelihood (DML) estimation method. In this letter, on the basis of the convexity of the DML function, we propose a computationally efficient algorithm framework for off-grid DOA estimation. Numerical experiments demonstrate the superior performance of the proposed methods in terms of accuracy, robustness and speed.

Geometric dilution of precision (GDOP) is a measure showing the positioning accuracy at different spatial locations in location systems. Although expressions of GDOP for the time of arrival (TOA), time difference of arrival (TDOA), and angle of arrival (AOA) systems have been developed, no closed form expression of GDOP are available for the received signal strength (RSS) system. This letter derives an explicit GDOP expression utilizing the RSS measurement in the wireless sensor networks.

The information maximization (Infomax) based on information entropy theory is a class of methods that can be used to blindly separate the sources. Torkkola applied the Infomax criterion to blindly separate the mixtures where the sources have been delayed with respect to each other. Compared to the frequency domain methods, this time domain method has simple adaptation rules and can be easily implemented. However, Torkkola's method works only in the real valued field. In this letter, the Infomax for blind separation of the delayed sources is extended to the complex case for processing of complex valued signals. Firstly, based on the gradient ascent the adaptation rules for the parameters of the unmixing network are derived and the steps of algorithm are given. Then, a measurement matrix is constructed to evaluate the separation performance. The results of computer experiment support the extended algorithm.

This paper considers tracking of a non-cooperative emitter based on a single sensor. To this end, the direct target motion analysis (DTMA) approach, where the target state is straightforwardly achieved from the received signal, is exploited. In order to achieve observability, the sensor has to perform a maneuver relative to the emitter. By suitably building an approximated likelihood function, the unscented Kalman filter (UKF), which is able to work under high nonlinearity of the measurement model, is adopted to recursively estimate the target state. Besides, the posterior Cramér-Rao bound (PCRB) of DTMA, which can be used as performance benchmark, is also achieved. The effectiveness of proposed method is verified via simulation experiments.

In non-cooperative scenarios, the estimation of direct sequence spread spectrum (DS-SS) signals has to be done in a blind manner. In this letter, we consider the spreading sequence estimation problem for DS-SS signals. First, the maximum likelihood estimate (MLE) of spreading sequence is derived, then a semidefinite relaxation (SDR) approach is proposed to cope with the exponential complexity of performing MLE. Simulation results demonstrate that the proposed approach provides significant performance improvements compared to existing methods, especially in the case of low numbers of data samples and low signal-to-noise ratio (SNR) situations.