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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.

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.

Compressed sensing (CS) model of complex-valued data can represent the signal recovery process of a large amount types of radar systems, especially when the measurement matrix is row-orthogonal. Based on debiased least absolute shrinkage and selection operator (LASSO), detection problem under Gaussian random design model, i.e. the elements of measurement matrix are drawn from Gaussian distribution, is studied by literature. However, we find that these approaches are not suitable for row-orthogonal measurement matrices, which are of more practical relevance. In view of statistical mechanics approaches, we provide derivations of more accurate test statistics and thresholds (or p-values) under the row-orthogonal design model, and theoretically analyze the detection performance of the present detector. Such detector can analytically provide the threshold according to given false alarm rate, which is not possible with the conventional CS detector, and the detection performance is proved to be better than that of the traditional LASSO detector. Comparing with other debiased LASSO based detectors, simulation results indicate that the proposed approach can achieve more accurate probability of false alarm when the measurement matrix is row-orthogonal, leading to better detection performance under Neyman-Pearson principle., Comment: 43 pages, 7 figures

Automotive radar is a key component in an ADAS. The increasing number of radars implemented in vehicles makes interference between them a noteworthy issue. One method of interference mitigation is to limit the TBP of radar waveforms. However, the problems of how much TBP is necessary and how to optimally utilize the limited TBP have not been addressed. We take CWS as an example and propose a method of designing the radar waveform parameters oriented by the performance of CWS We propose a metric to quantify the CWS performance and study how the radar waveform parameters (bandwidth and duration) influence this metric. Then, the waveform parameters are designed with a limit on the TBP to optimize the system performance. Numerical results show that the proposed design outperforms the state-of-the-art parameter settings in terms of system performance and resource or energy efficiency., Comment: 13 pages, 6 figures. Submitted to IEEE Transactions on Signal Processing

Modulations and diversities, including the Costas-ordered stepped-frequency and nonlinear stepped-frequency waveforms are widely used in linear frequency modulation (LFM) pulse trains to reduce the relatively high autocorrelation function (ACF) sidelobes. An efficient method was developed to optimize the interpulse frequency modulation to remove most of the ACF sidelobes about the mainlobe peak, with only a small increase in the mainlobe width. The genetic algorithm is used to solve the nonlinear optimization problem to find the interpulse frequency modulation sequence. The effects on the ACF sidelobes suppression and mainlobe widening are studied. The results show that the new design is superior to the corresponding stepped-frequency LFM signal and weighted stepped-frequency LFM signal in the terms of the ACF sidelobes reduction and mainlobe spread.

In the target tracking and its engineering applications, recursive state estimation of the target is of fundamental importance. This paper presents a recursive performance bound for dynamic estimation and filtering problem, in the framework of the finite set statistics for the first time. The number of tracking algorithms with set-valued observations and state of targets is increased sharply recently. Nevertheless, the bound for these algorithms has not been fully discussed. Treating the measurement as set, this bound can be applied when the probability of detection is less than unity. Moreover, the state is treated as set, which is singleton or empty with certain probability and accounts for the appearance and the disappearance of the targets. When the existence of the target state is certain, our bound is as same as the most accurate results of the bound with probability of detection is less than unity in the framework of random vector statistics. When the uncertainty is taken into account, both linear and non-linear applications are presented to confirm the theory and reveal this bound is more general than previous bounds in the framework of random vector statistics.In fact, the collection of such measurements could be treated as a random finite set (RFS).

A sparsity-driven algorithm of inverse synthetic aperture radar (ISAR) imaging is proposed. Based on the parametric sparse representation of the received ISAR signal, the problem of ISAR image formation is converted into the joint estimation of the target rotation rate and the sparse power distribution in the spatial domain. This goal is achieved by sequential minimization of L0 and L2 norms, which ensure the sparsest ISAR image and the minimum recovery error, respectively., Comment: This paper has been withdrawn by the authors

Space-time adaptive processing (STAP) is an effective tool for detecting a moving target in spaceborne or airborne radar systems. Statistical-based STAP methods generally need sufficient statistically independent and identically distributed (IID) training data to estimate the clutter characteristics. However, most actual clutter scenarios appear only locally stationary and lack sufficient IID training data. In this paper, by exploiting the intrinsic sparsity of the clutter distribution in the angle-Doppler domain, a new STAP algorithm called SR-STAP is proposed, which uses the technique of sparse recovery to estimate the clutter space-time spectrum. Joint sparse recovery with several training samples is also used to improve the estimation performance. Finally, an effective clutter covariance matrix (CCM) estimate and the corresponding STAP filter are designed based on the estimated clutter spectrum. Both the Mountaintop data and simulated experiments have illustrated the fast convergence rate of this approach. Moreover, SR-STAP is less dependent on prior knowledge, so it is more robust to the mismatch in the prior knowledge than knowledge-based STAP methods. Due to these advantages, SR-STAP has great potential for application in actual clutter scenarios., Comment: 28 pages, 11 figures, Submitted to the IEEE Transactions on Aerospace and Electronic Systems in April,2010

With iodine vapour treatment of various single crystals of Bi-Sr-Ca-Ln(Rare earth element)-Cu-O system, the stage-1 iodine-intercalation compounds of Bi2(Sr,Nd)2CuOy (2201), Bi2Sr2(Ca,Gd)Cu2Oy or Bi2Sr2CaCu2Oy (2212) and Bi8SrCaO14 have been prepared. x-ray diffraction and transmission electron microscope analysis indicate that iodine layer of thickness 3.2 similar 3.5 A is formed between two Bi2-O2 layers.

A new airborne MTI scheme that applies to non-homogeneous clutter environment is presented. Results of simulation have proved that the scheme has a superior ground clutter suppression performance and can effectively prevent the whitening of a target.

Electronic devices are getting more and more sensitive to the electro-static discharge (ESD) because of their smaller size and higher frequency. An electronic dictionary device studied here has failed in the ESD immunity test according to the IEC61000-4-2 standard. Many factors which could impact on the ESD immunity performance of the device are investigated in the test and some suggestions are also given in this paper for improving its performance. The improving suggestions are listed and analyzed combining the device in detail in this paper: 1) improving the ESD immunity performance of the dictionary's PCB; 2) changing the outer package material for the dictionary, protecting the LED; and 3) building a complete ESD test system. The device has gained a significant improvement on the ESD immunity by using merely part of our suggestions. We hope that this could be a reference to the manufactures of similar devices as well

This paper advances an Electrostatic Discharge Displacement Current (ESDDC) which will ?flow? through the space to all the victim devices on the EUT when discharge occurs and the ESDDC contributes to the failure of the equipment besides the discharge current and the electromagnetic field radiated from the ESD. Experiments were performed to test the ESDDC. The results show that the ESDDC is one of the major characters of the electrostatic discharge..

A method for evaluating the robustness of constant false alarm rate (CFAR) detectors is presented, which is based on the powerful methodology of influence function (IF) developed in the literature on robust statistics. It can evaluate and compare the robustness of different kinds of CFAR detectors by calculating the first derivative of false alarm probability (FAP) at an underlying distribution, which is named FAP-IF. The FAP-IF of cell averaging (CA) and ordered statistics (OS) CFAR detectors are presented. The computational simulation of radar detection in exponential clutter is also presented to prove the theoretical result.

In this paper, the relationship of subarray size, snapshot number and interference number is studied for different gradient computing methods. New forward and backward CGM (FBCGM) and multilayer FBCGM (MFBCGM) methods are proposed. Their advantages are summarized as follows: (1) they can be applied with a small number of snapshots. (2) They can be applied with a weaker constraint for the number of the array elements than that Mandyam (1997). (3) They perform well in coherent receiving environments. (4) They have less computational load compared to SMI and the subspace projection method.

In order to improve the clearness of images captured at night, a new enhancement algorithm is proposed in this paper. Its characters are: (1) enhancing the dark image with contrast enhancement and histogram equalization in series, so both the local and global information can be used; (2) a contrast transforming function which is suited to deal with dark image is proposed; (3) it can be realized in real time. The new method shows good performance in dealing with night vision images.

Conventional tracking radars are often embarrassed when targets being tracked are flying at low altitude over a flat surface. The basic idea for conventional low-angle tracking techniques is to avert the multipath interference by shielding the antenna from the surface reflection or by eliminating the reflection component from the received signal. This is the most direct way to deal with the well-known multipath effect. A new algorithm using a different idea is presented, with which the signal from the reflecting surface can be used to enhance that directly from the target, resulting in extended accuracy, applicability and efficiency. The estimation of the target elevation angle is expressed as the geometric mean of the target and its image's angles relative to the boresight axis. Implementation of the algorithm is quite simple.

Based on the fact that the result of noncoherent integration of the amplitudes of independent samples of noise is approximately identical to nonzero-mean Gaussian distribution, a Gaussian biparametric constant false alarm rate (GB-CFAR) processor is presented, which is a novel CFAR detector independent of original clutter plus noise distribution. Then the conclusion is presented that its detection performance relates only to the false alarm rate, the number of integration and the mean-to-standard-deviation ratio of original noise. Eliminating the mean of integrated noise amplitude during the detection process is equivalent to taking out partial noise energy, therefore, the detection performance of the GB-CFAR processor exceeds that of the traditional CA-CFAR (cell-averaging CFAR) processor.

In this paper, the leading characters, parallelism, programmability, scalability and reconfigurability of next generation high speed real-time radar signal processing systems are discussed in detail. Important design issues are studied. An experimental system underway is introduced.

Moving target detection (MTD) is a high-performance signal processing approach for pulse Doppler radar. High-performance radar calls for sliding MTD (SMTD). Traditional SMTD processing massively increases computational load, resulting in difficulty of implementation. A modified moving DFT algorithm and a new SMTD structure are proposed in this paper. The new SMTD structure adopts both batch-mode signal channel estimation and the modified moving DFT algorithm, which leads to a dramatic reduction of the computational load. The new SMTD structure can be regarded as an adaptive filter for other applications as well.

In this paper, the leading characters, parallelism, programmability, scalability and reconfigurability of next generation high speed real-time radar signal processing systems are discussed in detail. An experimental system underway is introduced.

A new tracking algorithm is proposed. It treats the target acceleration as a nonrandom term, and consists of a constant velocity filter an input estimator and a maneuver detector implemented in parallel. The new method has the same advantages as the two-stage Kalman estimator which requires a lesser amount of computation and provides even a better performance when compared with an augmented state Kalman filter. At the same time, the new method uses a better tuning parameter and removes a difficulty in implementation of the two-stage Kalman estimator. It is shown that the new filter is a better alternative to the two-stage Kalman estimator on tracking maneuvering targets.