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The irrigation water price is an indispensable element for the modernization of agriculture and the realization of rural revitalization. As a pilot initiative for agricultural water price reform, Hebei Province has explored various effective patterns. This study analyzed 714 first-hand data points obtained from agricultural irrigation areas in Hebei Province. The ‘two lines and three grades’ water price policy is difficult to implement in practice. The ‘overuse-charge’ includes two levels of water use is standards, the water price and reward standards in each level are inconsistent, especially, actual irrigation water use is far more than Individual Allowable Water Volume (IAWV). Similarly, there is a negative relationship between irrigation water use and water fees at the critical point of IAWV, that is, the more water used, the less water fees are paid. The ‘one line and two grades’ water price policy is proposed. The water volume less than the irrigation quota should be purchased by the government or rewarded farmers; on the contrary, the water price should be increased. It is suggested to improve the installation of irrigation metering facilities, popularization of field water-saving technology and quota management in the groundwater water irrigation region. HIGHLIGHTS The price of agricultural water is the primary means to coordinate ecological and economic use of water.; Agricultural water price is an indispensable element for the modernization of agriculture.; It is crucial for the government to repurchase the agricultural water which could recover groundwater level.; Establishing an incentive mechanism can improve farmers’ enthusiasm to transfer surplus water to the government.;

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]

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]

The use of heteropolyanions (HPAs) as starting materials can be an interesting strategy for preparing highly active bimetallic catalysts. In this paper, an Anderson-type Co 2 Mo 10 -HPA was synthesized by precipitation method, and was further used as starting precursor to prepare a Co 2 Mo 10 /Al 2 O 3 sulfur-tolerant water-gas shift (WGS) catalyst. It is shown that the close proximity of cobalt and molybdenum atoms in Co 2 Mo 10 -HPA precursor leads to a strong interaction between Co and Mo species and an easy second-step reduction of Mo oxides on the obtained catalyst, and thus remarkably improves the catalytic activity in sulfur-containing WGS reaction. [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]