Search

We consider the problem of direction finding using partly calibrated arrays, a distributed subarray with position errors between subarrays. The key challenge is to enhance angular resolution in the presence of position errors. To achieve this goal, existing algorithms, such as subspace separation and sparse recovery, have to rely on multiple snapshots, which increases the burden of data transmission and the processing delay. Therefore, we aim to enhance angular resolution using only a single snapshot. To this end, we exploit the orthogonality of the signals of partly calibrated arrays. Particularly, we transform the signal model into a multiple-measurement model, and show that there is approximate orthogonality between the source signals in this model. We then use blind source separation to exploit the orthogonality. Simulation and experiment results both verify that our proposed algorithm achieves high angular resolution as distributed arrays without position errors, inversely proportional to the whole array aperture.

Compressed sensing (CS) model of complex-valued data can represent the signal recovery process of many types of radar systems, especially when the measurement matrix is row-orthogonal. Based on debiased least absolute shrinkage and selection operator (LASSO), the detection problem under the Gaussian random design model, i.e. the elements of the measurement matrix are drawn from a Gaussian distribution, is studied by literature. However, these approaches are unsuitable for the 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 a detector can analytically provide the threshold according to a 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. Compared with other debiased LASSO based detectors, simulation results indicate that the proposed approach can achieve a more accurate probability of false alarm when the measurement matrix is row-orthogonal, leading to better detection performance under the Neyman-Pearson principle.

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. [ABSTRACT FROM AUTHOR]

Spatiotemporal geomagnetic variation is a significant research topic of geomagnetism and space physics. Generated by convection and flows within the fluid outer core, latitude dependence and asymmetry, as the inherent spatiotemporal properties of geomagnetic field, have been extensively studied. We apply and modify an extension of existing method, Hidden Markov Model (HMM), which is an efficient tool for modeling the statistical properties of time series. Based on ground magnetic measurement data set in mainland China, first, we find the parameters of HMM can be used as the geomagnetic statistical signature to represent the spatiotemporal geomagnetic variations for each site. The results also support the existence of the geomagnetic latitude dependence more apparently. Furthermore, we provide solid empirical evidence for geomagnetic asymmetry relying on such ground magnetic measurement data set. The parameters of HMM can be used as the geomagnetic variations' statistical signatureThe results support the existence of the geomagnetic latitude dependence more apparentlyWe provide empirical evidence for geomagnetic asymmetry using ground magnetic measurement data

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.

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.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.

Dynamic all-red extension (DARE) has recently been reported as a countermeasure to safety hazards caused by red-light running (RLR). DARE dynamically extends an all-red interval by a few seconds when detection has determined that an RLR hazard has a high probability of occurring. Previous studies of RLR prediction methods were usually based on high-quality sensors for vehicle detection. This study addresses the issues of (a) the feasibility of the use of existing configurations of inductive-loop detectors (ILDs) for RLR prediction and (b) the achievable performance of ILDs in this capacity. One challenging problem is the limited resolution and type of measurements that ILDs can provide. To overcome this problem, an innovative set of driver behavior parameters was developed, and two arrival time estimators with car-following characteristics were designed to realize the prediction of RLR. The empirical data from the field intersection were used to validate the performance of the RLR predictors. The results showed that a reasonable prediction of RLR could be achieved for an operational DARE from existing ILD vehicle detection systems that contain at least two ILDs: the advance loop closest to the stop bar and the leading presence loop.

Abstract: 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 1 norm algorithm and the FOCUSS algorithm.

Abstract: Weak key analysis is a key issue in the design of chaotic ciphers. While most of the existing research focusing on the degradation of the chaotic sequences which causes weak keys, we point out that the parameters for which the chaotic sequences do not degrade are still possible to be weak keys. In this paper, we propose a new approach based on the rigorous statistical test to improve the weak key analysis. The weak keys of a specific chaotic cipher are investigated by using our method and a large number of new weak keys are detected. These results verify that our method is more effective. On the other hand, although statistical tests are now widely adopted to test the chaos-based bit sequences, there are few reports of analysis results on the weak keys or weak sequences of chaotic cipher. Thus our work may be helpful for current research on statistical tests of chaotic cipher.

The traditional CO2 removal method with 5A zeolite molecular sieve will lose efficacy because of the strong affinity with the water vapor. A hydrophobic ZSM-5 adsorbent was prepared by a mixture of ZSM-5, silica sol, water and additives, followed by processes of extrusion, drying and then hydrophobic modification. The structures and properties of the adsorbent were analyzed by N2, H2O/CO2 adsorption isotherms and XRD analysis methods. The prepared adsorbent has multilevel pore structure, high BET specific surface area (296 m²/g) and better CO2 adsorption performance, as well as excellent hydrophobicity, and the H2O equilibrium adsorption capacity is about 1% of weight even under the condition of 90% relative humidity. [ABSTRACT FROM AUTHOR]

A new sub-array asynchronous recursive least square algorithm (SARLS) is proposed. The number of elements of the smallest sub-array can be less than the number of interferers. The computational load of SARLS can be reduced to o(N) while similar array performance to that of RLS is obtained. When the sub-array size is small, SARLS will converge faster than the conventional RLS.

Abstract: The construction of cryptographically strong substitution boxes (S-boxes) is an important concern in designing secure cryptosystems. The key-dependent S-boxes designed using chaotic maps have received increasing attention in recent years. However, the key space of such S-boxes does not seem to be sufficiently large due to the limited parameter range of discretized chaotic maps. In this paper, we propose a new key-dependent S-box based on the iteration of continuous chaotic maps. We explore the continuous-valued state space of chaotic systems, and devise the discrete mapping between the input and the output of the S-box. A key-dependent S-box is constructed with the logistic map in this paper. We show that its key space could be much larger than the current key-dependent chaotic S-boxes. [Copyright &y& Elsevier]

Radar is an important sensor for various types of intelligent agents. The increasing number of radars implemented in agents makes interference between them a noteworthy issue. One method of interference mitigation is to limit the electromagnetic resources in the frequency and time domain used by a radar. However, the problems of how many resources are necessary and how to optimally utilize the limited resources have not been addressed. In this paper, we take the collision warning (CW) function as an example and propose a method of designing the radar waveform parameters oriented to the CW performance. We propose a metric to quantify the CW 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 resources to optimize the CW performance. Numerical results show that the proposed design outperforms the state-of-the-art parameter settings in terms of CW performance and resource efficiency. • We design radar waveform for better functionality of the agent that radar serves. • Waveform parameters are optimized to improve collision warning performance. • The proposed design outperforms conventional design in terms of resource efficiency. [ABSTRACT FROM AUTHOR]