In this paper, a passive multiple-input multiple-output (MIMO) radar system with widely separated antennas that estimates the positions and velocities of multiple moving targets by utilizing time delay (TD) and doppler shift (DS) measurements is proposed. Passive radar systems can detect targets by using multiple uncoordinated and un-synchronized illuminators and we assume that all the measurements including TD and DS have been known by a preprocessing method. In this study, the algorithm can be divided into three stages. First, based on location information within a certain range and utilizing the DBSCAN cluster algorithm we can obtain the initial position of each target. In the second stage according to the correlation between the TD measurements of each target in a specific receiver and the DSs, we can find the set of DS measurements for each target. Therefore, the initial speed estimated values can be obtained employing the least squares (LS) method. Finally, maximum likelihood (ML) estimation of a first-order Taylor expansion joint TD and DS is applied for a better solution. Extensive simulations show that the proposed algorithm has a good estimation performance and can achieve the Cramér-Rao lower bound (CRLB) under the condition of moderate measurement errors.

Utilization data (a kind of incomplete data) is defined as the fraction of a fixed period in which the system is busy. In computer systems, utilization data is very common and easily observable, such as CPU utilization. Unlike inter-arrival times and waiting times, it is more significant to consider the parameter estimation of transaction-based systems with utilization data. In our previous work [7], a novel parameter estimation method using utilization data for an Mt/M/1/K queueing system was presented to estimate the parameters of a non-homogeneous Poisson process (NHPP). Since NHPP is classified as a simple counting process, it may not fit actual arrival streams very well. As a generalization of NHPP, Markovian arrival process (MAP) takes account of the dependency between consecutive arrivals and is often used to model complex, bursty, and correlated traffic streams. In this paper, we concentrate on the parameter estimation of an MAP/M/1/K queueing system using utilization data. In particular, the parameters are estimated by using maximum likelihood estimation (MLE) method. Numerical experiments on real utilization data validate the proposed approach and evaluate the effective traffic intensity of the arrival stream of MAP/M/1/K queueing system. Besides, three kinds of utilization datasets are created from a simulation to assess the effects of observed time intervals on both estimation accuracy and computational cost. The numerical results show that MAP-based approach outperforms the exiting method in terms of both the estimation accuracy and computational cost.

Bluetooth is a common wireless technology that is widely used as a connection medium between various consumer electronic devices. The receivers mostly adopt the Viterbi algorithm to improve a bit error rate performance but are hampered by heavy hardware complexity and computational load due to a coherent detection and searching for the unknown modulation index. To address these challenges, a non-coherent maximum likelihood estimation detector with an eight-state Viterbi is proposed for Gaussian frequency-shift keying symbol detection against an irrational modulation index, without any knowledge of prior information or assumptions. The simulation results showed an improvement in the performance compared to other ideal approaches.

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 we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.

In this letter we develop a software reliability modeling framework by introducing the Burr XII distributions to software fault-detection time. An extension to deal with software metrics data characterizing the product size, program complexity or testing expenditure is also proposed. Finally, we investigate the goodness-of-fit performance and compare our new models with the existing ones through real data analyses.

Tracking capsule endoscope location is one of the promising applications offered by implant body area networks (BANs). When tracking the capsule endoscope location, i.e., continuously localize it, it is effective to take the weighted sum of its past locations to its present location, in other words, to low-pass filter its past locations. Furthermore, creating an exact mathematical model of location transition will improve tracking performance. Therefore, in this paper, we investigate two tracking methods with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem. One of the two tracking methods is finite impulse response (FIR) filter-based tracking, which tracks the capsule endoscope location by averaging its past locations. The other one is particle filter-based tracking in order to deal with a nonlinear transition model on the capsule endoscope. However, the particle filter requires that the particle weight is calculated according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated from the received wireless signal. Therefore, for the purpose of applying the particle filter to capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrate that the two tracking methods can improve the performance as compared with the conventional maximum likelihood (ML) localization. Furthermore, we confirm that the particle filter-based tracking outperforms the conventional FIR filter-based tracking by taking the realistic capsule endoscope transition model into consideration.

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.

We propose a maximum likelihood estimation approach for the recovery of continuously-defined sparse signals from noisy measurements, in particular periodic sequences of Diracs, derivatives of Diracs and piecewise polynomials. The conventional approach for this problem is based on least-squares (a.k.a. annihilating filter method) and Cadzow denoising. It requires more measurements than the number of unknown parameters and mistakenly splits the derivatives of Diracs into several Diracs at different positions. Moreover, Cadzow denoising does not guarantee any optimality. The proposed approach based on maximum likelihood estimation solves all of these problems. Since the corresponding log-likelihood function is non-convex, we exploit the stochastic method called particle swarm optimization (PSO) to find the global solution. Simulation results confirm the effectiveness of the proposed approach, for a reasonable computational cost.

MLPDA (Maximum Likelihood Probabilistic Data Association) has attracted a great deal of attention as an effective target track extraction method in high false density environments. However, to extract an accelerated target track on a 2-dimensional plane, the computational load of the conventional MLPDA is extremely high, since it needs to search for the most-likely position, velocity and acceleration of the target in 6-dimensional space. In this paper, we propose VG-MLPDA (Variable Gating MLPDA), which consists of the following two steps. The first step is to search the target's position and velocity among candidates with the assumed acceleration by using variable gates, which take into account both the observation noise and the difference between assumed and true acceleration. The second step is to search the most-likely position, velocity and acceleration using a maximization algorithm while reducing the gate volume. Simulation results show the validity of our method.

For an indoor area where a target node is tracked with anchor nodes, we can calculate the priori probability density functions (pdfs) on the distances between the target and anchor nodes by using its shape, three-dimensional sizes and anchor nodes locations. We call it “the area layout information (ALI)” and apply it for two indoor target tracking methods with received signal strength indication (RSSI) assuming a square location estimation area. First, we introduce the ALI to a target tracking method which tracks a target using the weighted sum of its past-to-present locations by a simple infinite impulse response (IIR) low pass filter. Second, we show that the ALI is applicable to a target tracking method with a particle filter where the motion of the target is nonlinearly modelled. The performances of the two tracking methods are evaluated by not only computer simulations but also experiments. The results demonstrate that the use of ALI can successfully improve the location estimation performance of both target tracking methods, without huge increase of computational complexity.

In this Letter, the maximum likelihood (ML) estimator for the parameters of a real sinusoid in additive white Gaussian noise using irregularly-spaced samples is derived. The ML frequency estimate is first determined by a one-dimensional search, from which optimum amplitude and phase estimates are then computed. It is shown that the estimation performance of the ML method can attain Cramér-Rao lower bound when the signal-to-noise ratio is sufficiently large.

Many existing software reliability models (SRMs) are based on the assumption that fault correction activities take a negligible amount of time and resources, which is often invalid in real-life situations. Consequently, the estimated and predicted software reliability tends to be over-optimistic, which could in turn mislead management in related decision-makings. In this paper, we first make an in-depth analysis of real-life software testing process; then a Markovian SRM considering fault correction process is proposed. Parameter estimation method and software reliability prediction method are established. A numerical example is given which shows that by using the proposed model and methods, the results obtained tend to be more appropriate and realistic.

For a group of wirelessly networked robots, called "a robot swarm," to accomplish a unified task as a group, it is necessary to generate a set of common coordinates among all member robots and to notify each member robot of its heading direction in the generated common coordinates. However, when the member robots are not equipped with sensors to identify their locations or bearings, they can use only a ranging capability based in the wireless communication protocol being used to network them as a tool to generate a set of common coordinates among them. This paper presents the detailed principles of a method for generating a set of common coordinates/heading direction for a robot swarm with only ranging capability which we have proposed so far. After showing the theoretical Cramer-Rao lower-bound on the location estimation error variance, we demonstrate several computer simulation results for the proposed method with Received Signal Strength Indication (RSSI)-based ranging.

In this paper, a novel intra-die spatial correlation extraction method referred to as MLEMTC (Maximum Likelihood Estimation for Multiple Test Chips) is presented. In the MLEMTC method, a joint likelihood function is formulated by multiplying the set of individual likelihood functions for all test chips. This joint likelihood function is then maximized to extract a unique group of parameter values of a single spatial correlation function, which can be used for statistical circuit analysis and design. Moreover, to deal with the purely random component and measurement error contained in measurement data, the spatial correlation function combined with the correlation of white noise is used in the extraction, which significantly improves the accuracy of the extraction results. Furthermore, an LU decomposition based technique is developed to calculate the log-determinant of the positive definite matrix within the likelihood function, which solves the numerical stability problem encountered in the direct calculation. Experimental results have shown that the proposed method is efficient and practical.

The situations where several population parameters need to be estimated simultaneously arise frequently in wide areas of applications, including reliability modeling, survival analysis and biological study. In this paper, we propose Bayesian methods of estimation of the ordered parameters of the two exponential populations, which incorporate the prior information about the simple order restriction, but sometimes breaks the order restriction. A simulation study shows that the proposed estimators are more efficient (in terms of mean square errors) than the isotonic regression of the maximum likelihood estimators with equal weights. An illustrative example is finally presented.

Frequency offset estimation is an important technique in receiver design of wireless communications. In many applications, sampled single frequency tone is selected as training symbol/sequence for frequency synchronization. Under this assumption, frequency offset estimation can be regarded as the problem of single carrier frequency offset estimation. In this Letter, an approximate maximum likelihood frequency estimator is proposed. This estimator is efficient at moderate and high SNR's. Compared with other estimators, the proposed estimator is less sensitive to the variance threshold and offers feasible levels of computation complexity. The proposed estimator is suitable for high frequency offset cases and coarse/fine frequency synchronization applications.

In a centralized localization scenario, the limited throughput of the central node constrains the possible number of target node locations that can be estimated simultaneously. To overcome this limitation, we propose a method which effectively decreases the traffic load associated with target node localization, and therefore increases the possible number of target node locations that can estimated simultaneously in a localization system based on received signal strength indicator (RSSI) and maximum likelihood estimation. Our proposed method utilizes a threshold which limits the amount of forwarded RSSI data to the central node. As the threshold is crucial to the method, we further propose a method to theoretically determine its value. We experimentally verified the proposed method in various environments and the experimental results revealed that the method can reduce the load by 32-64% without significantly affecting the estimation accuracy.

Target location estimation is one of many promising applications of wireless sensor networks. However, until now only few studies have examined location estimation performances in real environments. In this paper, we analyze the effect of walking people on target location estimation performance in three experimental locations. The location estimation is based on received signal strength indicator (RSSI) and maximum likelihood (ML) estimation, and the experimental locations are a corridor of a shopping center, a foyer of a conference center and a laboratory room. The results show that walking people have a positive effect on the location estimation performance if the number of RSSI measurements used in the ML estimation is equal or greater than 3, 2 and 2 in the case of the experiments conducted in the corridor, foyer and laboratory room, respectively. The target location estimation accuracy ranged between 2.8 and 2.3 meters, 2.5 and 2.1 meters, and 1.5 and 1.4 meters in the case of the corridor, foyer and laboratory room, respectively.