In a time-division broadcast positioning system (TDBPS), localizing mobile targets using classical time difference of arrival (TDOA) methods poses significant challenges. Concurrent TDOA measurements are infeasible because targets receive signals from different anchors and extract their transmission times at different reception times, as well as at varying positions. Traditional TDOA estimation schemes implicitly assume that the target remains stationary during the measurement period, which is impractical for mobile targets exhibiting high dynamics. Existing methods for mobile target localization are mostly specialized and rely on motion modeling and do not rely on the concurrent TDOA measurements. This issue limits their direct use of the well-established classical TDOA-based localization methods and complicating the entire localization process. In this article, to obtain concurrent TDOA estimates at any instant out of the sequential measurements for direct use of existing TDOA-based localization methods, we propose a novel TDOA estimation method, termed parameterized TDOA (P-TDOA). By approximating the time-varying TDOA as a polynomial function over a short period, we transform the TDOA estimation problem into a model parameter estimation problem and derive the desired TDOA estimates thereafter. Theoretical analysis shows that, under certain conditions, the proposed P-TDOA method closely approaches the Cramér–Rao Lower Bound (CRLB) for TDOA estimation in concurrent measurement scenarios, despite measurements being obtained sequentially. Extensive numerical simulations validate our theoretical analysis and demonstrate the effectiveness of the proposed method, highlighting substantial improvements over existing approaches across various scenarios.

Doppler shift is an important measurement for localization and synchronization (LAS), and is available in various practical systems. Existing studies on LAS techniques in a time-division broadcast LAS system (TDBS) only use sequential time-of-arrival (TOA) measurements from the broadcast signals. In this article, we develop a new optimal LAS method in the TDBS, namely, LAS-SDT, by taking advantage of the sequential Doppler shift and TOA measurements. It achieves higher accuracy compared with the conventional TOA-only method for user devices (UDs) with motion and clock drift. Another two variant methods, LAS-SDT-v for the case with UD velocity aiding and LAS-SDT-k for the case with UD clock drift aiding, are developed. We derive the Cramér–Rao lower bound (CRLB) for these different cases. We show analytically that the accuracies of the estimated UD position, clock offset, velocity, and clock drift are all significantly higher than those of the conventional LAS method using TOAs only. Numerical results corroborate the theoretical analysis and show the optimal estimation performance of the LAS-SDT.

A two-way time-of-arrival (TOA) system is composed of anchor nodes (ANs) and user devices (UDs). Two-way TOA measurements between AN-UD pairs are obtained via round-trip communications to achieve localization and synchronization (LAS) for a UD. Existing LAS method for a moving UD with clock drift adopts an iterative algorithm, which requires accurate initialization and has high computational complexity. In this letter, we propose a new closed-form two-way TOA LAS approach, namely CFTWLAS, which does not require initialization, has low complexity and empirically achieves optimal LAS accuracy. We first linearize the LAS problem by squaring and differencing the two-way TOA equations. We employ two auxiliary variables to simplify the problem to finding the analytical solution of quadratic equations. Due to the measurement noise, we can only obtain a raw LAS estimation from the solution of the auxiliary variables. Then, a weighted least squares step is applied to further refine the raw estimation. We analyze the theoretical error of the new CFTWLAS and show that it empirically reaches the Cramér-Rao lower bound (CRLB) with sufficient ANs under the condition of proper geometry and small noise. Numerical results in a 3D scenario verify the theoretical analysis that the estimation accuracy of the new CFTWLAS method reaches CRLB in the presented experiments when the number of ANs is large, the geometry is appropriate, and the noise is small. Unlike the iterative method whose complexity increases with the iteration count, the new CFTWLAS has constant low complexity.

For radio-based time-of-arrival (TOA) positioning systems applied in harsh environments, obstacles in the surroundings and on the vehicle itself will block the signals from the anchors, reduce the number of available TOA measurements and thus degrade the localization performance. Conventional multi-antenna positioning technique requires a good initialization to avoid local minima, and suffers from location ambiguity due to insufficient number of TOA measurements and/or poor geometry of anchors at a single epoch. In this paper, taking advantage of the multi-epoch and multi-antenna (MEMA) TOA measurements bridged by inter-epoch constraints to utilize more information and improve the geometry of visible anchors, we propose a new positioning method, namely MEMA-TOA method. A new initialization method based on semidefinite programming (SDP), namely MEMA-SDP, is first designed to address the initialization problem of the MEMA-TOA method. Then, an iterative refinement step is developed to obtain the optimal positioning result based on the MEMA-SDP initialization. We derive the Cramér-Rao lower bound (CRLB) to analyze the accuracy of the new MEMA-TOA method theoretically, and show its superior positioning performance over the conventional single-epoch and multi-antenna (SEMA) localization method. Simulation results in harsh environments demonstrate that i) the new MEMA-SDP provides an initial estimation that is close to the real location, and empirically guarantees the global optimality of the final refined positioning solution, and ii) compared with the conventional SEMA method, the new MEMA-TOA method has higher positioning accuracy without location ambiguity, consistent with the theoretical analysis.

In a localization system based-on asymmetric network, only one of the anchor nodes (ANs) transmits signal. A sensor node (SN) receives it and then transmits signal that is received by all ANs to form time-of-arrival (TOA) measurements. SN localization is achieved based-on these TOA measurements along with the known AN positions. Existing work all assumes the SN is stationary. This will cause extra localization error for a moving SN. We develop an optimal localization method based-on maximum likelihood (ML) estimator, namely ML-LOC, utilizing information on the SN velocity and clock drift, to determine the position of a moving SN. We analyze its localization error and derive the Cramér-Rao lower bound (CRLB). Results from numerical simulations verify its optimal performance. We implement a prototype hardware localization system based-on consumer level ultra-wide band (UWB) chips. Experiments using the real system are carried out. Results validate the performance of the proposed method and show its feasibility in real-world applications.

In a time of arrival (TOA) or pseudorange-based positioning system, user location is obtained by observing multiple anchor nodes (ANs) at known positions. Utilizing more than one positioning systems, e.g., combining global positioning system (GPS) and BeiDou navigation satellite system (BDS), brings better positioning accuracy. However, ANs from two systems are usually synchronized to two different clock sources. Different from single-system localization, an extra user-to-system clock offset needs to be handled. Existing dual-system methods either have high computational complexity or suboptimal positioning accuracy. In this article, we propose a new closed-form dual-system localization (CDL) approach that has low complexity and optimal localization accuracy. We first convert the nonlinear problem into a linear one by squaring the distance equations and employing intermediate variables. Then, a weighted least-squares (WLSs) method is used to optimize the positioning accuracy. We prove that the positioning error of the new method reaches Cramér-Rao lower bound (CRLB) in far-field conditions with small measurement noise. Simulations on 2-D and 3-D positioning scenes are conducted. Results show that, compared with the iterative approach, which has high complexity and requires a good initialization, the new CDL method does not require initialization and has lower computational complexity with comparable positioning accuracy. The numerical results verify the theoretical analysis on positioning accuracy, and show that the new CDL method has superior performance over the state-of-the-art closed-form method. Experiments using real GPS and BDS data verify the applicability of the new CDL method and the superiority of its performance in the real world.

In a time-division broadcast positioning system, a user device (UD) determines its position by obtaining sequential time of arrival or pseudorange measurements from signals broadcast by multiple synchronized base stations. The existing localization method using sequential pseudorange measurements and a linear clock drift model for the TDPBS, namely, LSPM-D, does not compensate the position displacement caused by the UD movement and will result in position error. In this article, depending on the knowledge of the UD velocity, we develop a set of optimal localization methods for different cases. First, for known UD velocity, we develop the optimal localization method, namely, LSPM-KVD, to compensate the movement-caused position error. We show that the LSPM-D is a special case of the LSPM-KVD when the UD is stationary with zero velocity. Second, for the case with unknown UD velocity, we develop a maximum-likelihood (ML) method to jointly estimate the UD position and velocity, namely, LSPM-UVD. Third, in the case that we have prior distribution information of the UD velocity, we present a maximum a posteriori estimator for localization, namely, LSPM-PVD. We derive the Cramér-Rao lower bound for all three estimators and analyze their localization error performance. We show that the position error of the LSPM-KVD increases as the assumed known velocity deviates from the true value. As expected, the LSPM-KVD has the smallest position error while the LSPM-PVD and the LSPM-UVD are more robust when the prior knowledge of the UD velocity is limited. Numerical results verify the theoretical analysis on the optimality and the positioning accuracy of the proposed methods.

In this article, we design a new time-of-arrival (TOA) system for simultaneous user device (UD) localization and synchronization with a periodic asymmetric ranging network, namely, PARN. The PARN includes one primary anchor node (PAN) transmitting and receiving signals, and many secondary ANs (SANs) only receiving signals. All the UDs can transmit and receive signals. The PAN periodically transmits sync signal and the UD transmits response signal after reception of the sync signal. Using TOA measurements from the periodic sync signal at SANs, we develop a Kalman filtering method to virtually synchronize anchor nodes (ANs) with high accuracy estimation of clock parameters. Employing the virtual synchronization, and TOA measurements from the response signal and sync signal, we then develop a maximum-likelihood (ML) approach, namely, ML-LAS, to simultaneously localize and synchronize a moving UD. We analyze the UD localization and synchronization error, and derive the Cramér-Rao lower bound (CRLB). Different from existing asymmetric ranging network-based TOA systems, the new PARN 1) uses the periodic sync signals at the SAN to exploit the temporal correlated clock information for high accuracy virtual synchronization and 2) compensates the UD movement and clock drift using various TOA measurements to achieve consistent and simultaneous localization and synchronization performance. Numerical results verify the theoretical analysis that the new system has high accuracy in AN clock offset estimation and simultaneous localization and synchronization for a moving UD. We implement a prototype hardware system and demonstrate the feasibility and superiority of the PARN in real-world applications by experiments.

In two-way time-of-arrival (TOA) systems, a user device (UD) obtains its position by round-trip communications to a number of anchor nodes (ANs) at known locations. The objective function of the maximum likelihood (ML) method for two-way TOA localization is nonconvex. Thus, the widely-adopted Gauss-Newton iterative method to solve the ML estimator usually suffers from the local minima problem. In this letter, we convert the original estimator into a convex problem by relaxation, and develop a new semidefinite programming (SDP) based localization method for moving UDs, namely SDP-M. Numerical result demonstrates that compared with the iterative method, which often fall into local minima, the SDP-M always converge to the global optimal solution and significantly reduces the localization error by more than 40%. It also has stable localization accuracy regardless of the UD movement, and outperforms the conventional method for stationary UDs, which has larger error with growing UD velocity.

There are much data sampled continuously by sensors in the wireless sensor network. Storing and mining these data can find more potential information and provide help for decision making. As an important technology for data mining and multi-criteria decision, skyline computation can identify the interesting single points for user. In order to analyze the groups of points, the group-based skyline is proposed to query all the Pareto Optimal groups which are not g-dominated by other groups with the same number of points. Existing algorithms about g-skyline can just compute static data. However, data stream is very common in many applications, and it is very important to design algorithm go query g-skyline over data stream. In this paper, we propose new algorithms to compute g-skyline over a data stream. We present sharing strategy and then present two efficient algorithms: point-arriving algorithm and point-expiring algorithm. The experimental results on three kinds of synthetic data and a real stock data show that our algorithms perform efficiently over a data stream .

Dialogue act recognition is recognized as an important step for computers to understand human dialogues as it is closely related to the human intention. There are two main challenges in dialogue act recognition. Firstly, multimodal features should be taken into consideration, which include lexical, syntactic, prosodic cues, even facial appearance and gesture. Secondly, samples distribution in the dialogue act corpus is highly imbalanced. Thus traditional classification algorithms produce poor performance when they are applied on these imbalanced multi-modal tasks. In this paper, the multi-kernel SVM model is investigated to deal with these problems. Multi-kernel SVM is an effective technique for leaning from multi-modal data, but it is sensitive to imbalance. So an improved multi-kernel SVM model is proposed. To show the effectiveness of the proposed model, we test it on some open classification tasks and a Chinese dialogue act recognition task. Significant improvements are observed from the experimental results.

In the last decade, 2q -MUSIC, an extension of MUSIC algorithm to an arbitrary even order 2q, has been proposed to process direction-finding problems. Despite of its better performance compared with MUSIC, it suffers from the high computational complexity, thus limiting its practical application. This letter proposes a method called Non-Redundant-2q-MUSIC using 2qth order statistics for uniform linear arrays and uniform rectangular arrays. The proposed method lowers the computational complexity effectively by removing the redundancy of the virtual array and the 2q th-order cumulant matrix. And root-MUSIC can be applied to avoid calculating the pseudo-spectrum. It is illustrated in both theoretical proof and computer simulations that the proposed method performs properly and effectively.

Because of the long period and high chip rate of GPS P-code, direct acquisition is challenging. In the letter, the widely used zero-padding scheme (ZPS) for direct GPS P-code acquisition is generalized to investigate the effects of the ZPS on detection performance, parallel searching capability, and mean acquisition time. It is shown that, by adjusting the zeros padded to received signal according to signal to noise ratio, the generalized zero-padding scheme (GZPS) makes a better tradeoff between detection performance and parallel searching capability and further reduces mean acquisition time. The generalized zero-padding scheme can be easily applied to the previously proposed zero-padding method (ZPM) and the direct average method (DAM) to improve their mean acquisition time performance and it does not increase the implementation complexity of a receiver.

For long PN-code, rapid acquisition is difficult due to large search space. To speed up the search process, extended replica folding acquisition search technique (XFAST), which directly reduces the code phases to be searched by folding local signal, provides an efficient approach to rapid acquisition. Nevertheless, after folding the correlation properties of PNcode are degraded; hence, the detection performance of XFAST to weak signal is worse than that of nonfolding methods. To improve the detection performance, a dual-folding acquisition method (DF) is proposed. By folding both incoming signal and local signal, DF extends coherent integration time to enhance detection performance and indirectly reduce mean acquisition time. Numerical results demonstrate the enhancement of the proposed method with respect to other methods such as serial search (SS), zero-padding method (ZP), and XFAST.

Optimum combining (OC) is a powerful means for combatting co-channel interference. Its performance, however, is difficult to analyze. The prevailing methodology in current use is to neglect the influence of additive white Gaussian noise (AWGN). Inclusion of AWGN into the analysis usually leads to mathematical intractability, except for some extreme cases. In this letter, we formulate the problem in the framework of hypergeometric functions, so that their good properties can be exploited to enable the analysis to account for the influence of AWGN and co-channel interference simultaneously. We obtain closed-form solutions to the characteristic function (CHF) of the signal-to-interference-plus-noise ratio at the OC output for three important operational scenarios in Rayleigh fading, although the general solution still needs further effort to simplify. The assumptions made here are that both signal and interferers suffer from Rayleigh fading, and the number of interferers is not less than the number of antennas. The use of resulting CHF formulas is illustrated through their application to the bit-error and outage performance evaluation of OC.

Due to the functional complexity of its signal-to-interference (SIR) ratio, the outage probability of maximum ratio combining is available only for certain independent and identically distributed (i.i.d.) fading environments. In this paper, we partially relax this restriction by allowing the signal channel-gain vector to follow a general fading distribution with arbitrary spatial correlation. The problem is tackled in a novel framework. The key step is to represent the probability density of the reciprocal of SIR, conditioned on signal vector, as the higher-order derivative of a simple exponential function in signal power whereby a generic formula for outage probability can be determined. The application of the generic formula to Rayleigh, Rician, and Nakagami faded signals is elaborated. Numerical results are also presented for illustration.

No systematic procedure for tightly bounding the average capacity of multiple-input-multiple-output (MIMO) correlated Rician fading channels is available in the literature. In addition to the involvement of a highly nonlinear log-determinant operator in the conditional capacity expression, the difficulty arises from the complicated noncentral Wishart distribution of channel sample matrix. In this paper, we tackle the problem with arbitrary antenna correlation existing either at the transmitter or at the receiver, but allowing for the numbers of the transmit and receive antennas to be arbitrary. By introducing an exact determinant expansion and by finding an explicit expression for the general moment of the determinant of the channel sample matrix, we obtain a general upper bound for the average channel capacity. To obtain a general lower bound, we construct and prove a multivariate convex function with each of its variables being the log-determinant function of a complex noncentral Wishart-distributed matrix. We further show that the general bounds so obtained can be simplified to explicit expressions for Rician fading channels with arbitrary semicorrelation and a mean matrix of rank one. The new results are simple, easy to be used, and superior in tightness as evidenced by intensive numerical examples.

The error performance of M-ary differential phase-shift keying on Rayleigh fading channels is widely analyzed in the framework originated by Pawula, and the results usually take the form of an integral. In this letter, we formulate the same problem in a different framework, and solve it by using a linear prediction-based technique resulting in a simple solution in closed form.

Due to the difficulty in its exact analysis, the ergodic (average) channel capacity of correlated multiple-input multiple-output (MIMO)-fading channels is evaluated mainly by resorting to bounding techniques. Most of bounding techniques, however, are focused on the upper bound, and exploit the information in a Wishart-distributed sample covariance matrix solely in the form of its determinant or mean value. In this paper, we rigorously represent the determinant of form det(I + /spl gamma/S) in terms of all possible principal submatrices of S, thereby allowing us to exploit the fine structure of S to derive an upper bound for the channel capacity. To obtain a lower bound, we carefully construct a multivariate function and verify its multivariate convexity. Besides their simplicity in mathematics, the new bounds show superior tightness, as evidenced by various numerical examples.

No systematic procedure for tightly bounding the average capacity of MIMO correlated Rician fading channels is available in the literature. The difficulty arises from the complicated non-central Wishart distribution of channel sample matrix, in addition to the involvement of a highly nonlinear log determinant operator in the conditional capacity expression. In this paper, we tackle the problem with arbitrary antenna correlation existing either at the transmitter or at the receiver, but allowing for the numbers of the transmit and receive antennas to be arbitrary. By introducing an exact determinant expansion and by finding the explicit expressions for the expected values of the determinant and log-determinant of the complex non-central Wishart-distributed matrix, we obtain a simple but very tight upper and lower bounds for the average channel capacity. Both bounds are simple, in closed form, easy to be used, and superior in tightness as evidenced by intensive numerical examples.