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Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars. [ABSTRACT FROM AUTHOR]

This paper studies the problem surrounding distributed passive arrays (sensors) locating multiple emitters while performing self-calibration to correct possible errors in the assumed array directions. In our setting, only the angle-of-arrival (AoA) information is available for localization. However, such information may contain bias due to array directional errors. Hence, localization requires self-calibration. To achieve both, the key element behind our approach is that the received signals from the same emitter should be geometrically consistent if sensor arrays are successfully calibrated. This leads to our signal model, which is built on a mapping directly from emitter locations and array directional errors to received signals. Then we formulate an atomic norm minimization and use group sparsity to promote geometric consistency and align 'ghost' emitter locations from calibration errors. Simulations verify the effectiveness of the proposed scheme. We derive the Cramér Rao lower bound and numerically compare it to the simulations. Furthermore, we derive a necessary condition as a rule of thumb to decide the feasibility of joint localization and calibration. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is a serious malignant disease with high incidence, high mortality and poor prognosis. This study aimed to establish a novel signature based on apoptosis-related genes (ARGs) to predict the prognosis of HCC.Methods: Expression data of HCC from TCGA database and the list of 160 ARGs from MSigDB were downloaded. The genes included in apoptosis-related signature were selected by univariate Cox regression analysis and lasso Cox regression analysis. Subsequently, a prognostic risk model for scoring patients was developed, and then separates patients into two groups. Kaplan-Meier and receiver operating characteristic analysis were performed to evaluate the prognostic value of the model in TCGA, GEO and ICGC databases. The characteristics of immune cell infiltration between two groups of HCC were investigated. Finally, a nomogram was plotted to visualize the prognosis prediction.Results: Nine genes (CDC25B, DAP3, ETF1, GSR, LGALS3, MGMT, PPP2R5B, SQSTM1 and VDAC2) were included in the prognostic risk model. Survival was lower in the high-risk group. Surprisingly, the high-risk group was significantly more in immune cell infiltration and with higher immunoscore and stromalscore than in the low-risk group. In addition, the risk score was an independent prognostic factor for HCC.Conclusions: Prognostic signature comprising nine ARGs could be used as a potential prognostic factor for HCC. It also provides an important idea for further understanding the immunotherapy of HCC. [ABSTRACT FROM AUTHOR]

The problem of high‐resolution direction finding is considered using a distributed array network with array position errors, called partly calibrated array. These errors are common in practice, and significantly affect high angular resolution. Existing calibration methods rely on centralized data transmission, which requires all the measurements to be transmitted to a computation center. This centralized mode leads to communication bottlenecks and system inflexibility. Here, a decentralized mode is designed, where each sensor only communicates locally with their neighboring sensors. Based on this mode, a corresponding direction‐finding approach is proposed using sequential methods that process data one by one, which corresponds to the decentralized signal processing sensor by sensor. Simulations verify that the proposed method achieves decentralization with minor cost in estimation accuracy.We consider high‐resolution direction finding using distributed arrays with position errors between subarrays. Existing methods are based on centralized mode, which has communication bottlenecks and system inflexibility. Here, we propose a decentralized algorithm to solve this problem. [ABSTRACT FROM AUTHOR]

Nowadays, mutual interference among automotive radars has become a problem of wide concern. In this article, a decentralized spectrum allocation approach is presented to avoid mutual interference among automotive radars. Although decentralized spectrum allocation has been extensively studied in cognitive radio sensor networks, two challenges are observed for automotive sensors using radar. First, the allocation approach should be dynamic as all radars are mounted on moving vehicles. Second, each radar does not communicate with the others so it has quite limited information. A machine learning technique, reinforcement learning, is utilized because it can learn a decision-making policy in an unknown dynamic environment. As a single radar observation is incomplete, a long short-term memory recurrent network is used to aggregate radar observations through time so that each radar can learn to choose a frequency subband by combining both the present and past observations. Simulation experiments are conducted to compare the proposed approach with other common spectrum allocation methods such as the random and myopic policy, indicating that our approach outperforms the others. [ABSTRACT FROM AUTHOR]

Frequency agile radar (FAR) is known to have excellent electronic counter-countermeasures performance and the potential to realize spectrum sharing in dense electromagnetic environments. Many compressed sensing (CS) based algorithms have been developed for joint range and Doppler estimation in FAR. This paper considers theoretical analysis of FAR via CS algorithms. In particular, we analyze the properties of the sensing matrix, which is a highly structured random matrix. We then derive bounds on the number of recoverable targets. Numerical simulations and field experiments validate the theoretical findings and demonstrate the effectiveness of CS approaches to FAR. [ABSTRACT FROM AUTHOR]

This letter addresses the detection of a subspace distributed target signal obscured by disturbance. The disturbance consists of a clutter component with an unknown subspace structure and a white noise component with unknown noise power. A detection strategy is proposed based on the volume cross-correlation function, which provides a metric that measures the linear (in) dependency between two subspaces. Simulation results indicate that the proposed detector can achieve better performance than several peer methods, without resorting to secondary data and a priori knowledge about the clutter subspace including its rank. [ABSTRACT FROM AUTHOR]

This paper concerns the sparse Bayesian learning (SBL) problem for group sparse signals. Group sparsity means that the signal coefficients can be divided into groups and that the entries in one group are simultaneously zero or nonzero. In SBL, each group is controlled by a hyperparameter, which is estimated by solving the marginal likelihood maximization (MLM) problem. MLM is used to maximize the marginal likelihood of a given hyper-parameter by fixing all others. The main contribution of this paper is to extend the fast marginal likelihood maximization (FMLM) method to the group sparsity case. The key process of realizing FMLM in the group sparsity case is solved by finding the roots of a polynomial. Hence, the local maximum of the marginal likelihood can be found exactly, and the final estimation is obtained more efficiently. Furthermore, most large matrix inverses involved in MLM are replaced with the eigenvalue decompositions of much smaller matrices, which substantially reduces the computational complexity. Numerical results with both simulated and real data demonstrate the excellent performance and relatively high computational efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

With the wide raw material sources, low price and no secondary pollution, colleseed oil residue, as the organic waste residue of industry, is worth expanding and applied. However, there is no an economical and efficient method to realise circulating utilisation of oil residue. In this paper, the amount of oil residue added into the liquid medium of six treatments in trial 1 was 0, 5, 10, 15, 20 and 40%, respectively. The effect of the different adding amounts and incubation time on the mycelial growth of different strains, like Flammulina velutipes and Auricularia polytricha, was comparatively studied. The results showed that the optimum culture medium to add the oil residue is as follows: 15% of the oil residue in Flammulina velutipes and 40% of oil residue in Auricularia polytricha. The effect of incubation time on mycelial growth and pH value was correlated to different strains. The incubation time of the liquid culture medium of Flammulina velutipes added oil residue was about 7 days, and the pH value was about 5.0 as the best culture condition, compared with 8 days and 4.3 in culture medium of Auricularia polytricha. [ABSTRACT FROM AUTHOR]

Time difference of arrival (TDOA) is widely used in wireless localization systems. Among the enormous approaches of TDOA, high resolution TDOA algorithms have drawn much attention for its ability to resolve closely spaced signal delays in multipath environment. However, the state-of-art high resolution TDOA algorithms still have performance weakness on resolving time delays in a wireless channel with dense multipath effect. In this paper, we propose a novel TDOA algorithm with super resolution based on a multi-dimensional cross-correlation function: the volume cross-correlation function (VCC). Theoretical analyses are provided to justify the feasibility of the proposed TDOA algorithm, and numerical simulations also show an excellent time resolution capability of the algorithm in multipath environment. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) provides a new perspective for data reduction without compromising performance when the signal of interest is sparse or has intrinsically low-dimensional structure. The theoretical foundation for most of the existing studies on CS is based on the stable embedding (i.e., a distance-preserving property) of vectors that are sparse or in a union of subspaces via random measurement matrices. To the best of our knowledge, few existing literatures of CS have clearly discussed the stable embedding of linear subspaces via compressive measurement systems. In this paper, we explore a volume-based stable embedding of multidimensional signals based on Grassmann manifold, via Gaussian random measurement matrices. The Grassmann manifold is a topological space, in which each point is a linear vector subspace, and is widely regarded as an ideal model for multidimensional signals generated from linear subspaces. In this paper, we formulate the linear subspace spanned by multidimensional signal vectors as points on the Grassmann manifold, and use the volume and the product of sines of principal angles (also known as the product of principal sines) as the generalized norm and distance measure for the space of Grassmann manifold. We prove a volume-preserving embedding property for points on the Grassmann manifold via Gaussian random measurement matrices, i.e., the volumes of all parallelotopes from a finite set in Grassmann manifold are preserved upon compression. This volume-preserving embedding property is a multidimensional generalization of the conventional stable embedding properties, which only concern the approximate preservation of lengths of vectors in certain unions of subspaces. In addition, we use the volume-preserving embedding property to explore the stable embedding effect on a generalized distance measure of Grassmann manifold induced from volume. It is proved that the generalized distance measure, i.e., the product of principal sines between different points on the Grassmann manifold, is well preserved in the compressed domain via Gaussian random measurement matrices. Numerical simulations are also provided for validation. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous-wave (FMCW) methods and switched antenna arrays (SAAs) are very attractive for application in automotive radar because of their low hardware complexity. In traditional SAA radar, the receiving antenna array elements are successively switched according to their natural spatial order [referred to as sequential SAAs (SSAAs)]. For radars using both SSAA and FMCW methods, the phase difference caused by the target azimuth and time delay will be difficult to distinguish, thus resulting in delay–space coupling, which may lead to degradation of the road target observation. In this paper, randomized SAA (RSAA) FMCW radar is proposed. The new radar scheme has high delay/space resolution for target location detection and character extraction, and can also solve the coupling problem. The signal model of RSAA FMCW radar is derived and compared with that of SSAA radar. The influence of the antenna-array switching mode on the radar performance is also analyzed. A conventional zero-padding algorithm and a new clustering FOCUSS algorithm are both introduced for the signal processing of the new radar scheme. The numerical results illustrate the performance of RSAA FMCW radar in high-resolution target imaging and delay–space decoupling. [ABSTRACT FROM PUBLISHER]

A stepped-frequency (SF) waveform is effective in achieving high-range resolution (HRR) in modern radars. In this paper, we determine various fundamental limits of the SF waveform regarding the ambiguity, stability, and accuracy of stationary target profiling and the velocity compensation accuracy for moving targets. The investigation reveals that by using the information contained in both the phase and envelope of the echo signal, SF radars can achieve HRR profiles without ambiguity under a looser criterion and can compensate the range shift caused by a target's radial velocity. The results of this paper can aid in SF waveform design and in the development of processing algorithms for HRR profiling and velocity compensation. [ABSTRACT FROM PUBLISHER]

Based on the linear relationship between the chirp rate of cross-range inverse synthetic aperture radar (ISAR) signal and the slant range, a parametric sparse representation method is proposed for ISAR imaging of rotating targets. The ISAR echo is formulated as a parametric joint-sparse signal and the chirp rates at all range bins are estimated by maximizing the contrast of sparse ISAR image. Comparing with homologous algorithms, the computational complexity of the proposed method is significantly reduced. [ABSTRACT FROM AUTHOR]

Random stepped frequency (RSF) radar, which transmits random-frequency pulses, can suppress the range ambiguity, improve convert detection, and possess excellent electronic counter-countermeasures (ECCM) ability [1]. In this paper, we apply a sparse recovery method to estimate the range and Doppler of targets. We also propose a cognitive mechanism for RSF radar to further enhance the performance of the sparse recovery method. The carrier frequencies of transmitted pulses are adaptively designed in response to the observed circumstance. We investigate the criterion to design carrier frequencies, and efficient methods are then devised. Simulation results demonstrate that the adaptive frequency-design mechanism significantly improves the performance of target reconstruction in comparison with the nonadaptive mechanism. [ABSTRACT FROM AUTHOR]

A parametric sparse representation model of the inverse synthetic aperture radar (ISAR) signal has been proposed recently, and the ISAR signal is decomposed as a summation of many basis-signals determined by the target rotation rate. Based on the parametric sparse representation model, several sparsity-driven algorithms are proposed to retrieve both the target rotation rate and the ISAR image. In this paper, four parametric sparse recovery algorithms are compared mainly in three aspects: the accuracy of the rotation rate estimation, the ISAR image quality and the computational load. Numerical examples are presented to show the advantages and disadvantages for each method. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of the performance bound of a nonlinear filtering problem corresponding to tracking an extended target in cluttered environments (i.e., with false alarms and missed detections). The high resolution sensor obtains the measurements of the position and the extent of the extended target whose shape is modeled by an ellipse. The posterior Cramer-Rao lower bound (PCRLB) provides a useful tool to evaluate the best achievable performance of the nonlinear filtering problem. The bounds of the traditional kinematic state estimation are calculated using the point and the extended target model. It is shown in this paper that the bound of extended target tracking is smaller than that of point model because more information is utilized. The bounds are calculated to examine the influence of the measuring accuracy, the geometry between the sensor and the target, the prior knowledge of the target, and the environmental circumstance. In a cluttered environment, the PCRLB is calculated by IRF (information reduction factor), MSC (measurement sequence conditioning), and MESC (measurement existence sequence conditioning) approaches. The simulation results also illustrate the relationship of the three methods. [ABSTRACT FROM AUTHOR]

This paper presents recursive performance bounds for dynamic estimation and tracking problems in both single and multiple target cases within the framework of finite set statistics. Because the targets can appear and disappear when the detection probability of a sensor is less than unity (P_d < 1), we must determine the existence or nonexistence of the state as well as its value. Following the possible detection/miss sequences within the framework of a random vector, possible observation sets sequences are first defined. Based on these sequences, the performance bounds can be represented by a multivariate function of some time-based auxiliary elements. Recursive relations for all the auxiliary elements are derived rigorously. For the multitarget case, this recursive estimated bound can be applied without data association. Applications are analyzed through simulations to verify our theoretical results and show that our bounds are tighter than all other bounds for the case where detection probability P_d < 1. [ABSTRACT FROM PUBLISHER]

It has been shown in the literature that, the inverse synthetic aperture radar (ISAR) echo can be seen as sparse and the ISAR imaging can be implemented by sparse recovery approaches. In this paper, we propose a new parametric weighted L1 minimization algorithm for ISAR imaging based on the parametric sparse representation of ISAR signals. Since the basis matrix used for sparse representation of ISAR signals is determined by the unknown rotation parameter of a moving target, we have to estimate both the ISAR image and basis matrix jointly. The proposed algorithm can adaptively refine the basis matrix to achieve the best sparse representation for the ISAR signals. Finally the high-resolution ISAR image is obtained by solving a weighted L1 minimization problem. Both numerical and real experiments are implemented to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Trans-jurisdictional conflict in both water quantity and quality is a general concern in large river basins. In this paper, the relative utility function combined with the asymmetric Nash bargaining method was established to analyze the trans-jurisdictional conflict between water quantity and water quality in the Zhangweinan Canal Basin in China. The basin was divided into four conflict stakeholders, namely, Shanxi, Hebei, Henan, and Shandong Provinces, based on administrative unit. The water usage and pollutant discharge scheme for multiple stakeholders was optimized using the established model to satisfy the environmental flow and water quality objectives at the identified conflict sections. The results indicated that the total water consumption was reduced from 4.38 × 10 m in 2007 to 1.97 × 10 m, and that the allowable COD and NH-N discharged into the river was less than 3.8 × 10 t and 4.3 × 10 t, respectively. About 81.1 % of COD and 76.1 % of NH-N should be further reduced compared with the values in 2007. [ABSTRACT FROM AUTHOR]

Thirty surface sediments, corresponding overlying water and porewater samples were collected for analysis of organochlorine pesticides (OCPs) from Taihu Lake, China in May 2010. Altogether six OCPs, i.e. hexachlorobenzene (HCB), β-hexachlorohexane (β-HCH), p,p′-DDE, p,p′-DDD, p,p′-DDT, and o,p′-DDT were detected, and the total OCPs varied from 7.84 to 32.23 ng g in sediments, from 136.97 to 2,185.14 ng L in porewater and from 24.27 to 154.07 ng L in overlying water, respectively. The highest levels of contamination in sediment was observed in southeast of the lake, while in water and porewater samples highest levels were found in Zhushan Bay, the seriously polluted area in the north part of Taihu Lake. The partition coefficient of measured OCPs between sediment-overlying water was five and six fold higher than that between sediment-porewater, indicating the different distribution patterns of OCPs among sediment, overlying water and porewater matrices. The ratios of DDT/(DDD + DDE) ranged from 0.57 to 2.28 in sediments, 0.93-13.02 in porewater and 0.84-15.98 in overlying water, respectively, suggesting the potential new source of DDTs into the lake. The ratios of o,p′-DDT/ p,p′-DDT in the three matrices were centered around 0.2-0.3, indicating the o,p′-DDT in the lake was originated from historical usage of technical DDTs. [ABSTRACT FROM AUTHOR]

The high resolution imaging of distributed targets in millimeter-wave radar system is studied in this paper. We use Gaussian Mrakov random field (GMRF) to represent the clustering property of the targets. Our novel method, called Clustering FOCUSS, incorporate an additional cluster constraint into the process of the focal underdetermined system solver (FOCUSS) algorithm. Simulation results indicate that the novel algorithm has a higher imaging accuracy than the methods of Capon beamforming, the l norm algorithm and the FOCUSS algorithm. [ABSTRACT FROM AUTHOR]