Genomic heterogeneity of multiple synchronous lung cancer

Multiple synchronous lung cancers (MSLCs) present a clinical dilemma as to whether individual tumours represent intrapulmonary metastases or independent tumours. In this study we analyse genomic profiles of 15 lung adenocarcinomas and one regional lymph node metastasis from 6 patients with MSLC. All 15 lung tumours demonstrate distinct genomic profiles, suggesting all are independent primary tumours, which are consistent with comprehensive histopathological assessment in 5 of the 6 patients. Lung tumours of the same individuals are no more similar to each other than are lung adenocarcinomas of different patients from TCGA cohort matched for tumour size and smoking status. Several known cancer-associated genes have different mutations in different tumours from the same patients. These findings suggest that in the context of identical constitutional genetic background and environmental exposure, different lung cancers in the same individual may have distinct genomic profiles and can be driven by distinct molecular events

An Efficient Direct Position Determination Method for Multiple Strictly Noncircular Sources

This paper focuses on the localization methods for multiple sources received by widely separated arrays. The conventional two-step methods extract measurement parameters and then estimate the positions from them. In the contrast to the conventional two-step methods, direct position determination (DPD) localizes transmitters directly from original sensor outputs without estimating intermediate parameters, resulting in higher location accuracy and avoiding the data association. Existing subspace data fusion (SDF)-based DPD developed in the frequency domain is computationally attractive in the presence of multiple transmitters, whereas it does not use special properties of signals. This paper proposes an improved SDF-based DPD algorithm for strictly noncircular sources. We first derive the property of strictly noncircular signals in the frequency domain. On this basis, the observed frequency-domain vectors at all arrays are concatenated and extended by exploiting the noncircular property, producing extended noise subspaces. Fusing the extended noise subspaces of all frequency components and then performing a unitary transformation, we obtain a cost function for each source location, which is formulated as the smallest eigenvalue of a real-valued matrix. To avoid the exhaustive grid search and solve this nonlinear function efficiently, we devise a Newton-type iterative method using matrix Eigen-perturbation theory. Simulation results demonstrate that the proposed DPD using Newton-type iteration substantially reduces the running time, and its performance is superior to other localization methods for both near-field and far-field noncircular sources

Lagrange Programming Neural Network for TOA-Based Localization with Clock Asynchronization and Sensor Location Uncertainties

Source localization based on time of arrival (TOA) measurements in the presence of clock asynchronization and sensor position uncertainties is investigated in this paper. Different from the traditional numerical algorithms, a neural circuit named Lagrange programming neural network (LPNN) is employed to tackle the nonlinear and nonconvex constrained optimization problem of source localization. With the augmented term, two types of neural networks are developed from the original maximum likelihood functions based on the general framework provided by LPNN. The convergence and local stability of the proposed neural networks are analyzed in this paper. In addition, the Cramér-Rao lower bound is also derived as a benchmark in the presence of clock asynchronization and sensor position uncertainties. Simulation results verify the superior performance of the proposed LPNN over the traditional numerical algorithms and its robustness to resist the impact of a high level of measurement noise, clock asynchronization, as well as sensor position uncertainties

Novel frequency of arrival‐based emitter localisation methods based on multiple moving observers

Abstract It is well known that the Doppler frequency shift contains the emitter location information. The problem of emitter location based on frequency of arrival (FOA) measurements of multiple moving observers is focussed. In contrast to most relevant works, the newly proposed positioning methods consider the influence of signal frequency deviation due to source instability and present two mathematical models of the frequency deviation. The first model assumes that the frequency offset is a small random parameter. A two‐stage closed‐form location method (called method a) is proposed. In stage 1, a set of pseudo‐linear equations are established according to the geometric relationship between the observers and emitter, so as to obtain an intermediate estimate of source position. Stage 2 returns to the original FOA measurement model and uses the estimation result in stage 1 to construct the second set of pseudo‐linear equations, yielding the final positioning result. The second model assumes that the signal frequency deviation is a large deterministic parameter. We propose an alternative two‐stage localisation method (called method b). In stage 1, a decoupling optimisation algorithm is developed based on the pseudo‐linear equations derived in stage 1 of method a, and intermediate estimates of the source position and frequency deviation are obtained successively. Stage 2 returns to the original FOA measurement model again and forms the second set of pseudo‐linear equations, yielding the final closed‐form solutions of emitter position and frequency offset. Both methods are proved analytically to achieve the Cramér–Rao bound (CRB) under the moderate noise level. Simulation results demonstrate the superior performance of the new methods

A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation

The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications

Bias reduction for TDOA localization in the presence of receiver position errors and synchronization clock bias

Abstract Time difference of arrival (TDOA) localization does not require time stamping of the source signal and is playing an increasingly important role in passive location. In addition to measurement noise, receiver position errors and synchronization clock bias are two important factors affecting the performance of TDOA positioning. This paper proposes a bias-reduced solution for passive source localization using TDOA measurements in the presence of receiver position errors and synchronization clock bias. Like the original two-step weighted least-squares solution, the new technique has two stages. In the first stage, the proposed method expands the parameter space in the weighted least-squares (WLS) formulation and imposes a quadratic constraint to suppress the bias. In the second stage, an effective WLS estimator is given to reduce the bias generated by nonlinear operations. With the aid of second-order error analysis, theoretical biases for the original solution and proposed bias-reduced solution are derived, and it is proved that the proposed bias-reduced method can achieve the Cramér–Rao lower bound performance under moderate Gaussian noise, while having smaller bias than the original algorithm. Simulation results exhibit smaller estimation bias and better robustness for all estimates, including those of the source position, refined receiver positions, and clock bias vector, when the measurement noise or receiver position error increases

Robust direct position determination methods in the presence of array model errors

Abstract Direct position determination (DPD) methods are known to have many advantages over the traditional two-step localization method, especially for low signal-to-noise ratios (SNR) and/or short data records. However, similar to conventional direction-of-arrival (DOA) estimation methods, the performance of DPD estimators can be dramatically degraded by inaccuracies in the array model. In this paper, we present robust DPD methods that can mitigate the effects of these uncertainties in the array manifold. The proposed technique is related to the classical auto-calibration procedure under the assumption that prior knowledge of the array response errors is available. Localization is considered for the cases of both unknown and a priori known transmitted signals. The corresponding maximum a posteriori (MAP) estimators for these two cases are formulated, and two alternating minimization algorithms are derived to determine the source location directly from the observed signals. The Cramér-Rao bounds (CRBs) for position estimation are derived for both unknown and known signal waveforms. Simulation results demonstrate that the proposed algorithms are asymptotically efficient and very robust to array model errors