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Complete complementary sequence (CCS) design has become an important topic in recent years due to its theoretically ideal zero sidelobe performance. However, the main obstacle for the widespread use of the CCS is its sensitivity to the Doppler shift. In this letter, a method for constructing the CCS with the desired matrix-valued ambiguity functions under a peak-to-average-power ratio (PAR) and an energy constraint is proposed. A gradient-based nonlinear iterative algorithm is used to solve the constrained sequence design problem. Numerical results are presented to show that, compared with the state-of-the-art methods, the proposed method can produce CCS with better correlation properties and the Doppler tolerance. Finally, indoor experiments are conducted to verify the superior performance of the designed CCS, and a peak sidelobe level under −45 dB is realized on the hardware system. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint optimization of transmit waveform and receive filter in colocated multiple-input multiple-output (MIMO) radar to enhance target detection performance in the presence of signal-dependent interference. It is noticed that in the detection stage, the output energy is mainly concentrated in the mainlobe region. Therefore, we decompose the receive filter as the cascade of a receive beamformer and a temporal filter. Then, under the peak-to-average ratio (PAR) constraint, a problem is formulated to realize a trade-off between the signal-to-interference-plus-noise ratio (SINR) and the integrated sidelobe level (ISL) at the pulse compression output of mainlobe synthesized signal. A non-decreasing algorithm, which is the combination of sequential optimization algorithm and minorization-maximization (MM) method, is developed to solve this problem. Besides, in order to reduce computation burden, a special case is proposed, where we fix the temporal filter as the mainlobe synthesized signal. Then, another non-decreasing algorithm based on the MM method is proposed to solve the special case. Numerical experiments show that the proposed algorithms can obtain high output SINR and low output ISL efficiently.

Simultaneous polarimetric radars can measure the polarization scattering matrix (PSM) of moving targets within one pulse. The key point is the orthogonal waveform design to reduce the interference caused by the simultaneous transmission and reception. Meanwhile, to ensure the measurement accuracy of moving targets, the Doppler tolerance of the transmitted waveforms is also important. In this article, first, a matched filter (MF) at the receiver is considered and a unified framework with a newly proposed objective function to design Doppler resilient sequences under several constraints is described. The proposed objective function contains the widely used peak sidelobe level (PSL) and the integrated sidelobe level (ISL) as special cases. Meanwhile, a series of constraint conditions, including the unimodular constraint, the uncertainty modulus constraint, and the PAR constraint, is considered. Thereafter, a mismatched filter is considered for further improving the correlation properties and a similar framework for designing the optimal receiving filter at the expense of a controllable signal-to-noise ratio (SNR) loss is proposed. Numerical results are presented to verify the performance of the proposed methods. The designed sequence pair is also implemented on a fully polarimetric system to measure the PSM of a canonical target in the anechoic chamber, and the effectiveness of the proposed framework is verified by using the simultaneous polarimetric radar. It is predicted that the framework has the potential to be applied for other multichannel systems. [ABSTRACT FROM AUTHOR]

Periodic/aperiodic sequence with good correlation and stopband properties has received widespread attention and been widely applied in radar and communication systems. To meet the hardware requirement and maximize the transmitter efficiency, the unimodular or peak-to-average ratio (PAR) constraint is always required in sequence design. In this paper, we consider the problem of designing PAR-constrained periodic/aperiodic sequence with good properties. After establishing the corresponding criterions for both correlation and stopband properties, the unified PAR-constrained problem is formulated and then transformed into an unconstrained minimization problem via sequence synthesis. To solve the problem, an efficient gradient-based algorithm is proposed to minimize the objective function directly. As the main steps can be implemented by fast Fourier transform (FFT) operations and Hadamard product, the whole algorithm is computationally efficient. In addition, the proposed algorithm can be applied to design both the periodic and aperiodic sequences by choosing proper parameters. Numerical experiments show that the proposed algorithm has better performance than the state-of-the-art algorithms in terms of the sequence quality and the running time. [ABSTRACT FROM AUTHOR]

This article deals with the joint design of inter-pulse amplitude, phase and frequency to detect coexisting strong and weak targets in the presence of interference for cognitive radar. First, an amplitude–frequency-phase agile (AFPA) signal model and the corresponding coherent processing method based on phase compensation and non-uniform discrete Fourier transform (NUDFT) are developed. Then, two waveform design criteria, namely the weighted integrated sidelobe level (WISL) and weighted peak sidelobe level (WPSL), are considered to reduce the high sidelobe level in Doppler domain. The waveform is subject to peak-to-average ratio (PAR) constraint to align with current hardware specification, as well as spectrum and signal-to-noise ratio (SNR) loss constraints to enhance interference suppression capabilities. Subsequently, two polynomial-time iterative optimization algorithms are proposed for each of the resulting two non-convex problems. One integrates coordinate descent (CD) and first-order Taylor expansion convex approximation (FTECA), while the other incorporates CD and alternating direction method of multipliers (ADMM), thus updating frequency code and amplitude-phased code alternatively. Finally, numerical simulations demonstrate that the proposed method exhibits significantly better detection performance in the scenario where strong targets are accompanied by weak targets compared to the compressive sensing (CS) approach. • Inter-pulse amplitude, frequency and phase agile waveform. • Inter-pulse agile design via CD-FTECA algorithm for minimizing WISL. • Developing a CD-ADMM framework to track the non-convex problem of minimizing WPSL. [ABSTRACT FROM AUTHOR]

Advanced waveform design schemes have been widely employed for radar and sonar remote sensing analysis such as target detection and separation, where significant range sidelobe is a main factor that limits the improvement of analysis performance. As an extensional type of Golay complementary waveforms, complementary sets are a waveform design scenario of concern that shows more diversity in the design of transmission order, and results in a different distribution of range sidelobes. This work proposes an oversampled generalized Prouhet–Thue–Morse (OGPTM) method for the transmitted signal design of complementary sets, with comprehensive analysis to the influence on the sidelobe distribution. Based on this idea and our previous work, we further put forward a pointwise multiplication processor (PMuP) to integrate two delay-Doppler maps of oversampled complementary sets, which achieve much better sidelobe suppression performance on high-speed target detection with range migration. [ABSTRACT FROM AUTHOR]

By simultaneously transmitting multiple different waveform signals, a multiple-input multiple-output (MIMO) radar possesses higher degrees of freedom and potential in many aspects compared to a traditional phased-array radar. The spatial–temporal characteristics of waveforms are the key to determining their performance. In this paper, a transmitting waveform design method based on spatial–temporal joint (STJ) optimization for a MIMO radar is proposed, where waveforms are designed not only for beam-pattern matching (BPM) but also for minimizing the autocorrelation sidelobes (ACSLs) of the spatial synthesis signals (SSSs) in the directions of interest. Firstly, the STJ model is established, where the two-step strategy and least squares method are utilized for BPM, and the L2p-Norm of the ACSL is constructed as the criterion for temporal characteristics optimization. Secondly, by transforming it into an unconstrained optimization problem about the waveform phase and using the gradient descent (GD) algorithm, the hard, non-convex, high-dimensional, nonlinear optimization problem is solved efficiently. Finally, the method's effectiveness is verified through numerical simulation. The results show that our method is suitable for both orthogonal and partial-correlation MIMO waveform designs and efficiently achieves better spatial–temporal characteristic performances simultaneously in comparison with existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we conduct the joint design of transmitting sequence(s) and receiving filters subject to the Peak-to-Average Ratio (PAR) constraint in radar and communications applications. We consider optimizing the worst-case performance and the resulting optimization problem takes a maximin format. We propose two algorithms based on the MM (Majorization–Minimization or Minorization–Maximization) method as opposed to the traditional epigraph-based smooth reformulation. On top of that, both algorithms are guaranteed to converge to a B(oulingand)-stationary solution, and B-stationarity is the appropriate stationarity condition for problems with a nonconvex constraint set. The proposed algorithms successively solve a series of simple convex problems that enjoy low computational complexity. Numerical simulations have shown that the proposed algorithms empirically achieve slightly higher objective values and converge faster in terms of CPU time than the existing methods. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the low autocorrelation sequence design problem. We optimize a unified metric over a general constraint set. The unified metric includes the integrated sidelobe level (ISL) and the peak sidelobe level (PSL) as special cases, and the general constraint set contains the unimodular constraint, Peak-to-Average Ratio (PAR) constraint, and similarity constraint, to name a few. The optimization technique we employ is the majorization–minimization (MM) method, which is iterative and enjoys guaranteed convergence to a stationary solution. We carry out the MM method in two stages: in the majorization stage, we propose three majorizing functions: two for the unified metric and one for the ISL metric; in the minimization stage, we give closed-form solutions for algorithmic updates under different constraints. The update step can be implemented with a few Fast Fourier Transformations (FFTs) and/or Inverse FFTs (IFFTs). We also show the connections between the MM and gradient projection method under our algorithmic scheme. Numerical simulations have shown that the proposed MM-based algorithms can produce sequences with low autocorrelation and converge faster than the traditional gradient projection method and the state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the Signal-to-Interference plus Noise Ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach based on two different uncertainty sets. The former is composed of a finite collection of TIRs, obtained by sampling the actual TIR at some aspect angles; the latter is a spherical uncertainty set. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method in different scenarios. [ABSTRACT FROM PUBLISHER]

In a smart grid network, demand-side management plays a significant role in allowing consumers, incentivized by utilities, to manage their energy consumption. This can be done through shifting consumption to off-peak hours and thus reducing the peak-to-average ratio (PAR) of the electricity system. In this paper, we begin by proposing a demand-side energy consumption scheduling scheme for household appliances that considers a PAR constraint. An initial optimization problem is formulated to minimize the energy cost of the consumers through the determination of the optimal usage power and operation time of throttleable and shiftable appliances, respectively. We realize that the acceptance of consumers of these load management schemes is crucial to its success. Hence, we then introduce a multi-objective optimization problem which not only minimizes the energy cost but also minimizes the inconvenience posed to consumers. In addition to solving the proposed optimization problems in a centralized manner, two distributed algorithms for the initial and the multi-objective optimization problems are also proposed. Simulation results show that the proposed demand-side energy consumption schedule can provide an effective approach to reducing total energy costs while simultaneously considering PAR constraints and consumers' preferences. [ABSTRACT FROM PUBLISHER]

In this paper, we study the problem of code design to improve the detection performance of multi-static radar in the presence of clutter (i.e., a signal-dependent interference). To this end, we briefly present a discrete-time formulation of the problem as well as the optimal detector in the presence of Gaussian clutter. Due to the lack of analytical expression for receiver operation characteristic (ROC), code design based on ROC is not feasible. Therefore, we consider several popular information-theoretic criteria including Bhattacharyya distance, Kullback-Leibler (KL) divergence, J-divergence, and mutual information (MI) as design metrics. The code optimization problems associated with different information-theoretic criteria are obtained and cast under a unified framework. We propose two general methods based on Majorization-Minimization to tackle the optimization problems in the framework. The first method provides optimal solutions via successive majorizations whereas the second one consists of a majorization step, a relaxation, and a synthesis stage. Moreover, derivations of the proposed methods are extended to tackle the code design problems with a peak-to-average ratio power (PAR) constraint. Using numerical investigations, a general analysis of the coded system performance, computational efficiency of the proposed methods, and the behavior of the information-theoretic criteria is provided. [ABSTRACT FROM AUTHOR]

The transmit waveform with ambiguity function specifications in the range‐Doppler plane has been of great interest in traditional single antenna radar systems in recent years. This paper considers the design of waveform sets with a desired slow‐time ambiguity function for multiple‐input multiple‐output radar. Specifically, it is desirable that the ambiguity function has low sidelobes where clutter occurs, as this reduces the impact of the clutter on target detection. Instead of unimodular waveforms, the authors consider amplitude modulation signals and introduce the bound constraint on signal amplitudes into the optimisation for the consideration of transmitter efficiency. The design problem is formulated as minimising the weighted integrated sidelobes level of the ambiguity function over the range‐Doppler plane under bound constraints. The resulting problem has a highly non‐linear objective function and is constrained by bound constraints and quadratic equality (constant energy) constraints, making it difficult to solve. The authors develop an efficient algorithm based on the sequential quadratic programming technique and non‐linear conjugate gradient method to solve it. Simulation results show that the proposed algorithm is superior to the existing methods. [ABSTRACT FROM AUTHOR]

The water-filling (WF) algorithm is a widely used design strategy in the radar waveform design field to maximize the signal-to-interference-plus-noise ratio (SINR). To address the problem of the poor resolution performance of the waveform caused by the inability to effectively control the bandwidth, a novel waveform-related optimization model is established in this paper. Specifically, a corrected SINR expression is first derived to construct the objective function in our optimization model. Then, equivalent bandwidth and energy constraints are imposed on the waveform to formulate the waveform-related non-convex optimization model. Next, the optimal frequency spectrum is obtained using the Karush–Kuhn–Tucker condition of our non-convex model. Finally, the transmit waveform in the time domain is synthesized under the constant modulus constraint. Different experiments based on simulated and real-measured data are constructed to demonstrate the superior performance of the designed waveform on the SINR and equivalent bandwidth compared to the linear frequency modulated signal and waveform designed by the WF algorithm. In addition, to further evaluate the effectiveness of the proposed algorithm in the application of cognitive radar (CR), a closed-loop radar system design strategy is introduced based on our waveform design method. The experiments under real-measured data confirm the advantages of CR compared to the traditional open-loop radar structure. [ABSTRACT FROM AUTHOR]

Low-frequency bands are an important way to realize stealth target detection for airborne radars. However, in a complex electromagnetic environment; when low-frequency airborne radar operates over land, it will inevitably encounter a lot of unintentional communication and intentional interference, while effective suppression of interference can not be achieved only through the adaptive processing of the receiver. To solve this problem, this paper proposes optimizing an algorithm designed for sparse-frequency waveforms for use in airborne radars. The algorithm establishes a joint objective function based on the criteria of minimizing waveform energy in the spectrum stopband and minimizing the integrated sidelobe level of specified range cells. The waveform is optimized by a cyclic iterative algorithm based on the Fast Fourier Transform (FFT) operation. It can ensure the frequency domain stopband constraint to realize the effective suppression of main-lobe interference while forming lower-range sidelobes at specified range cells to improve the ability to detect dim targets. Theoretical analysis and simulation results have shown that the algorithm has good anti-interference performance. [ABSTRACT FROM AUTHOR]

The gross primary production (GPP) of the mangrove ecosystem determines the upper limit of the scale of its "blue carbon" sink. Tropical cyclones (TCs) are among the most important extreme events that threaten the subtropical mangrove ecosystem and have a serious impact on mangrove ecosystem GPP. However, there are somewhat insufficient scientific findings on regional-scale mangrove ecosystem GPP responding to large-scale weather events such as TCs. Therefore, we selected the subtropical Hainan Island mangrove ecosystem, where more than two TCs pass through per year, as the research area; selected direct-attack TCs as the research object; and took the mangrove vegetation photosynthesis light-use efficiency model established based on the eddy covariance observation data as the tool to evaluate the loss and recovery of mangrove ecosystem GPP after TCs attacked at a regional scale. We found that the TC impacted the mangrove ecosystem GPP through the photosynthetic area and rate, and the recovery of the rate occurred prior to the recovery of the area; the loss of mangrove ecosystem GPP is inversely proportional to the distance to the center of the TC and the distance to the coastline; and the canopy height, diameter at breast height, and aspect where the tree stands significantly influence the response of the mangrove ecosystem GPP to TCs. However, the response varies for different mangrove community compositions, soil conditions, and planting densities as well as different frequencies and intensities of TCs, and they should be analyzed in detail. This study is expected to provide technical and data support for the protection of blue carbon in a subtropical island mangrove ecosystem in response to extreme events and post-disaster recovery. [ABSTRACT FROM AUTHOR]

This paper addresses the waveform design problem of cognitive radar for extended target estimation in the presence of signal-dependent clutter, subject to a peak-to-average power ratio (PAR) constraint. Owing to this kind of constraint and the convolution operation of the waveform in the time domain, the formulated optimization problem for maximizing the mutual information (MI) between the target and the received signal is a complex non-convex problem. To this end, an efficient waveform design method based on minimization–maximization (MM) technique is proposed. First, by using the MM approach, the original non-convex problem is converted to a convex problem concerning the matrix variable. Then a trick is used for replacing the matrix variable with the vector variable by utilizing the properties of the Toeplitz matrix. Based on this, the optimization problem can be solved efficiently combined with the nearest neighbor method. Finally, an acceleration scheme is used to improve the convergence speed of the proposed method. The simulation results illustrate that the proposed method is superior to the existing methods in terms of estimation performance when designing the constrained waveform. [ABSTRACT FROM AUTHOR]

A new strategy to optimise the waveform of multiple‐input multiple‐output (MIMO) radar under the relative entropy constraint and signal‐to‐noise ratio (SNR) constraint is presented. The Kullback–Leibler divergence (KLD) is exploited between the two probability density functions of binary hypothesis testing as the design metric. A particular analysis of detection performance is made here to illuminate the balance between the false alarm and missing alarm probabilities. The results of the simulation demonstrate that the waveform design process constrained by relative entropy outperforms the process employing an explicit SNR constraint when the SNR is relatively low (i.e., within the interval [−2 dB, 0.5 dB]). The enhanced performance is attributable to the capability of the proposed optimisation process to focus the energy of the transmission waveform onto components where the eigenvalues of the target signal are large and the eigenvalues of the noise are small. [ABSTRACT FROM AUTHOR]

As the problem of spectral congestion is becoming severe, the coexistence between two primary spectrum users, radar and communications, has spurred extensive research interest. To reduce the mutual interferences between the two functions, MIMO radar waveform design needs to consider the compatibility in both spectral and spatial domains, where the former is achieved by null forming in the frequency domain and the latter is achieved by shaped beampattern synthesis. Additionally, high power efficiency and low system overhead are two desirable characteristics for MIMO radar system design. To this end, we first introduce a new realistic waveform constraint, peak-to-valley-power-ratio (PVPR) constraint per antenna to improve the power efficiency. Then, combined with PVPR constraint, we propose a switchable individual antenna power control scheme to jointly optimize waveforms and antenna locations. We adopt a max–min beampattern matching criterion and impose the ℓ 2 , 1 -norm penalty on the waveform matrix to promote the sparsity of the array. To solve the resultant non-smooth and non-convex problem, we develop a modified alternating directions method of multipliers, where a surrogate subproblem over primal variables is solved instead of the original problem, and its local convergence is analyzed. Finally, numerical experiments demonstrate the effectiveness and superiority of the proposed method over counterparts, especially obtaining the lowest sidelobe level and deeper spectral nulls using much fewer antennas. [ABSTRACT FROM AUTHOR]

In this paper, we study the joint design of transmitted sequence and receiving filter to improve the performance of radar in the presence of Doppler shifts. The mean-square error (MSE) of the estimates of the scattering coefficients of the point-like targets is considered to be the figure of merit. The design problem is cast as a joint optimization problem to minimize the MSE in the presence of target and clutter Doppler shifts. To tackle the design problem, we devise a new data model with respect to the Doppler shifts and propose a cyclic iterative approach to obtain optimized pairs for receiving filters and transmitted sequences. The proposed approach is based on fractional programming and iterations of the power method, and this approach is computationally efficient. Moreover, this approach can handle unimodularity constraints, quantization constraints, and peak-to-average power ratio constraints on the transmitted sequence. Simulation examples show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Optimizing the array structure or emission waveform of a multiple-input multiple-output (MIMO) radar system is an effective method to improve the performance in practical applications. In this study, the joint optimization of array structure and the corresponding emission waveform under interference and noise conditions was investigated. When compared with the waveform or array structure optimization alone, this method allowed the MIMO radar system to obtain a higher degree of freedom. By considering the practical limitations of the MIMO radar system, a waveform with good properties, such as orthogonality or pulse compression performance, was selected as the reference waveform. Subsequently, based on the similarity constraint and constant modulus constraint, a bivariate joint optimization problem of array structure and waveform was formulated, and an iterative optimization algorithm was proposed to solve it. The array composition was determined using a combinatorial search algorithm while the emission waveform was obtained by solving the similarity model. Eventually, it effectively converged to form quasi-optimal match variables after limited iterations. The proposed method can be expanded to the optimal launch of a specific target and an environment with a proper or minimum number of antennas, as well as implement array optimization with the desired waveform. The simulation results prove the effectiveness of the proposed method. This method provides an ideal choice for real-time construction, flexible launch, and signal processing of MIMO radar systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the joint design of transmit waveform and receive filter for colocated Multiple‐Input Multiple‐Output radar equipped with one‐bit digital‐to‐analogue converters (DACs). The problem is formulated as maximising the output signal‐to‐interference‐plus‐noise ratio in the presence of signal‐dependent interferences, subject to a discrete constraint imposed by the waveform quantised with one‐bit DACs. To cope with the challenging non‐convex problem, an alternating maximisation framework is developed to optimise the transmit waveform and receive filter vectors in an iterative manner. More specifically, for a given transmit waveform vector, the analytical expression of the receive filter vector is first derived. Then, by fixing the receive filter, a biconvex relaxation method is employed to tackle the non‐convex problem with respect to the transmit waveform vector. The performance of the proposed approach is demonstrated by numerical simulations in different circumstances. [ABSTRACT FROM AUTHOR]

This article deals with the multiple-input–multiple-output (MIMO) radar beampattern design in an effort to the coexistence with multiple communication systems. A waveform optimization model accounting for the minimization of the beampattern integrated sidelobe level (ISL) along with the mainlobe width, peak-to-average power ratio, and energy constraints, as well as multispectral requirements where the interference energy injected by the MIMO radar in each shared frequency band in a particular direction, is precisely controlled to ensure the desired quality of service at each communication system. Through an equivalent reformulation of the original nonconvex problem, a polynomial-time sequential convex approximation (SCA) procedure that involves the tackling of a series of constrained convex problems is proposed to monotonically decrease the ISL with the convergence guaranteed to a Karush–Kuhn–Tucker point. Herein, to speed up the convergence, a fast iterative algorithm based on the alternating-direction-method-of-multipliers framework is introduced to globally solve the convex problems during each iteration of the SCA procedure. Numerical results are provided to assess the proposed algorithm in terms of the computational complexity, the achieved beampattern, and spectral compatibility with some competitive counterparts available in the open literatures. [ABSTRACT FROM AUTHOR]

Training signals for OFDM channel estimation should possess low PAR to avoid nonlinear distortions at the transmit amplifier and at the same time they should be robust against frequency offsets. In this letter, we show that the above two requirements of the training signals are conflicting. We propose two new training signal designs for MIMO OFDM frequency-selective channel estimation. Our proposed training signals achieve more robust channel estimation performance against frequency offsets while satisfying the PAR constraints compared to training signals designed to achieve a fixed low PAR but without any consideration for robustness to frequency offsets. [ABSTRACT FROM PUBLISHER]

The transmit beampattern design with constant modulus (CM) constraint is a key technical issue in Multiple-Input Multiple-Output (MIMO) radar system. The problem is non-convex and direct solution is impossible in general, due to the CM constraint. Usually, most existing methods indirectly solve this problem by relaxing it to a more tractable form. These methods usually either degrade the performance due to relaxation or consume expensive computational cost. Different from the relaxation methods, a direct method is proposed to solve the non-convex problem without relaxation. In the proposed method, the original problem is first converted to an Unconstrained Quadratic Lagrange Multiplier Problem (UQLMP) over the non-convex complex circle manifold. Then, by efficiently finding the descent direction and step size to ensure the objective function to be non-increasing, a novel Manifold Optimization-based Conjugate Gradient (MOCG) approach is derived to directly solve UQLMP. Compared with current methods, the proposed method has better performance in terms of nulling depth. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R2 = 0.64) than the terrestrial model (RMSE = 0.0220, R2 = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m2/8 days in 2012, and from 33.76 to 19.51 g C/m2/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales. [ABSTRACT FROM AUTHOR]

The problem of data-driven joint design of transmitted waveform and detector in a radar system is addressed in this article. We propose two novel learning-based approaches to waveform and detector design based on end-to-end training of the radar system. The first approach consists of alternating supervised training of the detector for a fixed waveform and reinforcement learning of the transmitter for a fixed detector. In the second approach, the transmitter and the detector are trained simultaneously. Various operational waveform constraints, such as peak-to-average-power ratio and spectral compatibility, are incorporated into the design. Unlike traditional radar design methods that rely on rigid mathematical models, it is shown that radar learning can be robustified to uncertainties about environment by training the detector with synthetic data generated from multiple statistical models of the environment. Theoretical considerations and results show that the proposed methods are capable of adapting the transmitted waveform to environmental conditions while satisfying design constraints. [ABSTRACT FROM AUTHOR]

This study presents a privacy-aware stochastic multiobjective optimization framework that considers the objectives of both consumers and utility companies (UCs) in a demand response (DR) scheme. This framework enables a fair optimization of all the objectives together, and can be used to observe their effects on one another. Using this framework, we performed a comprehensive exploration of the relationships between the objectives of consumers and UCs. The uncertainty of photovoltaic energy sources is modeled by using a stochastic optimization approach. The objectives of the households include minimizing monetary cost and maximizing privacy and comfort. The UC's objectives include minimizing the peak demand and variation of the power demand. This study is the first to present a holistic analysis of privacy-aware DR by incorporating all five objectives of both supply and demand sides. The results reveal that near-optimal privacy performance is achievable with small compromises from the other objectives. [ABSTRACT FROM AUTHOR]

This letter considers the robust design of radar waveform with discrete phase codes under constant modulus constraint to enhance target detectability. To solve the resultant max‐min non‐convex optimization problem, a robust discrete phase coding algorithm via iterative technology (R‐DPCA‐IT) is developed which monotonically improves the worst‐case SNR and has an upper bound ensuring to converge a finite value. At each iteration, the original multidimensional quadratic optimization problem is turned into multiple one‐dimensional optimization problems solved globally via line search. Finally, the computational time and objective value of the proposed algorithm are assessed in comparison with the existing methods. [ABSTRACT FROM AUTHOR]

This paper studies the detection problem for multistatic passive radar. We consider scenarios where prior knowledge about the spectrum and peak-to-average ratio (PAR) of the non-cooperative illuminators of opportunity (IOs) is available. We develop several knowledge-aided (KA) detectors within the framework of the generalized likelihood ratio test (GLRT) to exploit such prior knowledge. Particularly, the knowledge about the bandwidth of the transmitted signal is employed to suppress the out-of-band noise, and the knowledge about the PAR constraint is exploited to eliminate the remaining high-power noise. The challenge of unknown spectrum condition is also addressed, where block sparse Bayesian learning (BSBL) is exploited to derive the maximum-likelihood estimates (MLEs) of the unknown, temporally correlated signal. The numerical results indicate that the proposed KA detectors offer significant performance improvements compared with the traditional detectors, which do not exploit such prior information.

To improve the detection performance of the radar transmit waveform while enabling the transmitter to perform at its maximal efficiency, a joint design method is proposed for the transmit and receive filter in the presence of signal-dependent clutter with a Peak-to-Average-power Ratio (PAR) constraint of the transmit waveform. First, an optimized model of the radar’s output Signal-to-Interference-plus-Noise Ratio (SINR) for range-extended target detection is established. Second, the analytic expression of the receiver is obtained by converting the optimization problem into the Rayleigh quotient model. The optimal matrix solution is then obtained by transforming the non-convex problem into a convex problem via the semi-definite matrix of the waveform. Finally, the optimal vector solution of the waveform is extracted from the optimal matrix solution by combining the rank-one approximation method combined with the nearest neighbor method. An optimal waveform with a maximal output SINR for a given PAR range is obtained using the proposed method. The SINR value of the waveform when PAR = 2 is the same as the SINR value of the optimized waveform under the energy constraint and is about 0.5 dB higher than the waveform when PAR = 1. Simulation results demonstrate the effectiveness of the proposed method.

A design technique is developed for the probing signals of a Multi‐Input Multi‐Output (MIMO) radar. The concentration of the energy of the signal in its essential duration and essential bandwidth is achieved through the use of a class of time‐frequency concentrated functions called the WLJ functions as the synthesizing signal set. The goal is to design a signal vector having a pre‐specified desired covariance (CoV) matrix while ensuring that the side‐lobes of the ambiguity functions are small. Since CoV matrices are structurally constrained, they form a manifold in the signal space. Hence, we argue that the difference between these matrices should not be measured in terms of the conventional Euclidean distance (ED); rather, the distance should be measured along the surface of the manifold, that is, in terms of a Riemannian distance (RD). In either case, the signal optimisation problem is non‐convex in the design variables, involving, respectively, a quartic and a square‐root objective function. An efficient algorithm based on successive convex approximation is developed in which the original non‐convex problems are transformed so that they can be approximated by a convex quadratically constrained quadratic problem at each stage, resulting in good approximate solutions. Comparing the designs using ED and RD, we find that the convergence of the algorithm can be significantly faster when optimising over the manifold (RD) than when optimising over the whole space (ED). More importantly, for tight constraints, the use of RD yields solutions which satisfy the constraints far better than the use of ED. [ABSTRACT FROM AUTHOR]

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable. [ABSTRACT FROM AUTHOR]

Models and estimates of Earth's human carrying capacity vary widely and assume, rather than solve for, binding environmental constraints (the process or resource in shortest supply relative to human biological needs). The binding constraint, and therefore the true upper bound on the number of humans that Earth could sustain indefinitely, remains unknown. We seek to resolve this uncertainty by considering a full range of technological possibilities and incorporating a potential stoichiometric constraint not previously explored. We find that limits to photosynthesis constrain population before micronutrients become limiting unless technological capabilities for utilizing nutrient resources lag far behind other technologies. With ideal technology, human carrying capacity runs into the tens of trillions, while with currently demonstrated technology Earth could support more than 200 billion humans. These numbers reflect neither a desirable nor a natural equilibrium population level, but represent a rough estimate of the maximum number of humans Earth could sustain. [ABSTRACT FROM AUTHOR]

Information technology (IT) services providers are exposed to exogenous risks faced by the industry as a whole, and endogenous risks from their current portfolio of IT contracts. This exposure may lead to cost overruns or legal responsibility for service-level breeches. Providers can leverage information about their risk positions implied by their IT services contract portfolios to gain strategic advantage over their competitors. We build theory in support of a new construct, profit-at-risk, for evaluating the trade-offs between contract profitability and service-level risk, stemming from financial economics theory and models. We simulate an IT services contract portfolio, and show how managers can reduce organizational risk by forgoing profit-maximizing contracts in lieu of more conservative service-level agreements, yet still achieve high returns. Our approach provides decision support for ex ante contract evaluation and negotiation, and a means to conduct ex post efficiency evaluation. It also aligns IT service management with best practices in financial management. [ABSTRACT FROM AUTHOR]

We present a signaling scheme for information embedding into the illumination of radar using frequency-hopping pulses. A frequency-hopping-based joint radar-communication system enables implementing a primary radar operation and a secondary communication function simultaneously. Then, we consider the problems of radar codes optimization under a peak-to-average-power ratio and an energy constraint. These radar codes design problems can be converted into non-convex quadratic programs with a finite or an infinite number of quadratic constraints. All problems are proved to be NP-hard optimization problems. Therefore, we develop optimization approaches, resorting to semi-definite programming relaxation technique along with to the idea of trigonometric polynomials, offering expected approximate solutions with a polynomial time calculation burden. We assess the capability of the proposed schemes, considering both the detection probability and the robustness in correspondence of Doppler shifts offered by the Neyman–Pearson detector. Simulation results show an improvement in detection performance as the average signal-to-noise ratio value increases, while still maintaining low symbol error rates between the proposed system nodes. [ABSTRACT FROM AUTHOR]

The topic of sequence design has received considerable attention due to its wide applications in active sensing. One important desired property for the design sequence is the spectral shape. In this paper, the sequence design problem is formulated by minimizing the regularized spectral level ratio subject to a peak-to-average power ratio constraint. Then, two algorithms are proposed by combining both the Dinkelbach's algorithm and the majorization–minimzation (MM) method organically. Specifically, by using the Dinkelbach's algorithm, the challenging fractional programming problem can be handled by solving a series of subproblems, which are further solved via the MM method. The numerical experiments verify the effectiveness of the optimization metric and demonstrate the performance of the proposed algorithms compared with the benchmark. [ABSTRACT FROM AUTHOR]

We address the problem of designing a transmit beampattern for multiple-input multiple-output (MIMO) radar considering coexistence with wireless communication systems. The designed beampattern is able to manage the transmit energy in spatial directions as well as in spectral frequency bands of interest by minimizing the deviation of the designed beampattern versus a desired one under a spectral constraint as well as the constant modulus constraint. While unconstrained beampattern design is straightforward, a key open challenge is jointly enforcing the spectral constraint in addition to the constant modulus constraint on the radar waveform. A new approach is proposed in this paper, which involves solving a sequence of constrained quadratic programs such that constant modulus is achieved at convergence. Furthermore, we show that each problem in the sequence has a closed form solution leading to analytical tractability. We evaluate the proposed beampattern with interference control (BIC) algorithm against the state-of-the-art MIMO beampattern design techniques and show that BIC achieves closeness to an idealized beampattern along with desired spectral shaping. [ABSTRACT FROM AUTHOR]

This paper develops a detailed and sequential procedure for short term operation of an aggregator to minimize the cost of the consumer and risk of the aggregator, which includes day-ahead (DA) and real-time (RT) operation. DA operation has two stages: consumer load scheduling and risk-based energy procurement. Consumer load scheduling implemented over a radial distribution network by a cost minimization objective function which considers electricity price and solar photovoltaic (PV) uncertainty, peak-to-average ratio, and phase unbalance. This model is formulated as a mixed integer nonlinear program. In the second stage, a risk-based energy procurement is formulated. Here, the aggregator has the choice of energy procurement from wholesale market, either in DA market or RT market, to meet the scheduled power. In RT operation, the aggregator takes a decision on share of RT purchases and battery schedules to minimize the cost of scheduled load power deviations. In order to realize this, a rolling window optimization is implemented, which is modeled as a mixed integer problem. The framework is demonstrated with a detailed case study of 15-node radial active distribution network consisting of 420 residential customers. [ABSTRACT FROM AUTHOR]

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting, day-ahead scheduling, and actual operation. In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets. [ABSTRACT FROM AUTHOR]

A novel optimization framework for resource allocation in wireless networks and radar systems is proposed, which merges the methods of maximum block improvement (MBI) and of sequential optimization. A detailed convergence proof is provided, showing that the proposed algorithm is able to monotonically increase the objective value while ensuring that every limit point of the generated variable sequence fulfills the problem first-order optimality conditions under very mild hypothesis. These results extend available convergence results on MBI and sequential optimization, significantly widening the range of applications that can be handled by the proposed framework compared to available approaches. This point is illustrated in detail presenting relevant applications from both the cellular and radar context, which fall under the umbrella of the developed optimization method. [ABSTRACT FROM AUTHOR]

Designing the radar waveform, which ensures spectral compatibility with the communication systems, is known to be challenging. In addition to having the desirable transmitter efficiency, it is also anticipated to share the good pulse compression property of the reference waveform. In this paper, we investigate the problem of waveform design for multiple-input multiple-output radar with good transmit beampattern under certain practical constraints (e.g., peak to average power ratio and similarity constraints) in coexistence with communication systems. The waveform is designed by minimizing a weighted summation of the beampattern integrated sidelobe-to-mainlobe ratio and waveform energy over the space-frequency bands. Since the resulting problem is NP-hard, two approximated primal-dual algorithms based on the alternating direction method of multipliers algorithm are proposed. Concretely, the proposed algorithms update the primal variable by minimizing the linearized and quadratic approximations of the augmented Lagrangian function of the original problem, respectively. The closed-form solution of each primal variable is provided. Moreover, both the convergence and computational complexities of these two algorithms are discussed. Numerical simulations are provided to demonstrate the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

In this paper, we consider the joint design of both transmit waveforms and receive filters for a colocated multipleinput- multiple-output (MIMO) radar with the existence of signaldependent interference and white noise. The design problem is formulated into a maximization of the signal-to-interference-plusnoise ratio (SINR), including various constraints on the transmit waveforms. Comparedwith the traditional alternating semidefinite relaxation approach, a general and flexible algorithm is proposed based on the majorization-minimization method with guaranteed monotonicity, lower computational complexity per iteration and/or convergence to a B-stationary point. Many waveform constraints can be flexibly incorporated into the algorithm with only a few modifications. Furthermore, the connection between the proposed algorithm and the alternating optimization approach is revealed. Finally, the proposed algorithm is evaluated via numerical experiments in terms of SINR performance, ambiguity function, computational time, and properties of the designed waveforms. The experiment results show that the proposed algorithms are faster in terms of running time and meanwhile achieve as good SINR performance as the the existing methods. [ABSTRACT FROM AUTHOR]

With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption. [ABSTRACT FROM AUTHOR]