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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.

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.

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: Extended target detection performance can be enhanced by using phase-modulated waveform designs in band-limited radar systems. Unlike waveforms designed for the total energy constraint, phase-modulated waveforms can fully exploit the transmit power in the pulse duration, which is more suitable for practical radar systems. An alternating iterative algorithm was developed to optimize the phase-modulated baseband waveform by maximizing the signal-to-noise ratio (SNR) at the receiver filter output. The output SNR increases continuously with the number of iterations and the algorithm is guaranteed to converge. Simulations validate the effectiveness of this approach. The waveforms designed by this method outperform other commonly used waveforms for extended target detection. [Copyright &y& Elsevier]

Abstract: Coupled map lattices (CML) have been recently used to construct ciphers. However, the complicated operations on real numbers make these CML-based ciphers difficult to analyze. This paper presents a new stream cipher with the discretized CML which operates on binary numbers. When discretizing the CML, the effective network structure is explicitly preserved to improve the confusion and diffusion. The proposed cipher can be easily analyzed using proper cryptographic techniques and has good security. The cipher has a strict lower bound on the period length of 264 and its key setup scheme achieves enough diffusion. The keystreams pass all of the NIST statistical randomness tests and have good randomness properties. In addition the cipher is resistant to the guess-and-determine attack. [Copyright &y& Elsevier]

Abstract: It is an important issue to study sea clutter suppression because it could interfere with the detection of targets above the sea surface severely. Spatial spectrum analyses show that the majority of sea clutter has low-frequency characteristics, compared to the high-frequency characteristics of the targets. This paper proposes a frequency-based spatial tracking filter to suppress sea clutter to facilitate targets identification. Experimental results show that the signal-to-clutter ratio can increase by more than 10 dB after filtering and the algorithm is feasible for practical use. In addition, the filtering equation can be optimized to maximize the signal-to-clutter ratio improvement. The equation parameters can also be adjusted to give a proper cut-off frequency for different targets and clutter. [Copyright &y& Elsevier]

Abstract: 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. [Copyright &y& Elsevier]

Abstract: State-space equations were applied to formulate the queuing and delay of traffic at a single intersection in this paper. The signal control of a single intersection was then modeled as a discrete-time optimal control problem, with consideration of the constraints of stream conflicts, saturation flow rate, minimum green time, and maximum green time. The problem cannot be solved directly due to the nonlinear constraints. However, the results of qualitative analysis were used to develop a first-phase signal control algorithm. Simulation results show that the algorithm substantially reduces the total delay compared to fixed-time control. [Copyright &y& Elsevier]

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.