Search

Deep learning has attracted intensive attention in human activity classification based on the radar. Whereas, most methods use the images to classify the human activities, ignoring the phase information of the radar data. In this paper, the complex-valued convolutional neural network (Complex-valued CNN) is utilized to classify the human activity behind the wall. We developed several Complex-valued CNN models, which have the same structures as several classical convolutional neural network(CNN) models and use both the amplitude and phase information of the range profiles. Experiments on the real data validate the performance of the Complex-valued CNN models.

This paper considers the design of a general constant beamwidth receiving beampattern for narrowband/wideband array. Specifically, a novel non-convex optimization model is formulated relying on the minimization of the matching error between the desired template and the designed beampattern in the presence of array gain constraint. Then an iteration methodology based on the coordinate descent (CD) framework is introduced. In each iteration, it splits the original problem into multiple one-dimensional subproblems with closed-form solutions ensuring that the matching error monotonically decreases to convergence. Finally, numerical examples are provided to verify the capability of the proposed algorithm. The simulation results highlight that the proposed method is capable of synthesizing the desired constant beamwidth beampattern with the low sidelobe and nulling.

We proposed a robust real-time human activity recognition method based on attention-augmented Gated Recurrent Unit (GRU) using radar range profile, namely Attention-Augmented Sequential Classification (AASC). We use attention mechanism to capture the temporal relationships inherent in the range profile signatures. Therefore, our model can learn long-term temporal correlation of human activity without increasing the depth or width of recurrent neural network. The attention weights are adaptively generated using features extracted by the GRU recurrent neural network. Finally, attention-augmented features are classified by Multi-layer perceptrons. Real data of picking, boxing, rasing leg and rasing hand are collected to evaluate our model. It is shown that the proposed method outperforms the conventional GRU in recognition accuracy and robustness, demonstrating the superiority in real-time activity recognition task.

This paper considers the joint design of the Multiphase Shift Key (MPSK) waveform and its mismatched filter to achieve an excellent autocorrelation function. More specifically, a general optimization model that accounts for the minimization of Peak Sidelobe Level (PSL) of autocorrelation in the presence of the SNR, discrete phase, and constant modulus constraints, as well as the filter length requirement, is developed. In order to handle the resultant NP-hard problem, the Primal-dual Type (PDT) algorithm is introduced along with each iteration step required to solve four subproblems with closed-form solutions. Finally, the simulation examples are provided to evaluate the proposed optimization model and the effectiveness iteration procedure. The numerical results highlight that the devised waveform and filter can attain a lower PSL than the other design strategy while possessing low SNR loss.

This paper deals with the design of the constant modulus sequence set for MIMO radar in an effort to achieve the desired correlation properties and favorable Doppler tolerance. In particular, the phase perturbation terms are forced in LFM waveforms with different frequency modulation disturbance to provide the optimization degree of freedom to achieve low correlation sidelobe level. To this end, the Weighted Integrated Sidelobe Level (WISL) of auto-and cross-correlation functions is formulated as a figure of merit to minimize. Besides, to obtain good Doppler tolerance, the similarity constraint between the devised waveforms and these LFM waveforms is involved. To cope with the resultant NP-hard problem, the Coordinate Descent (CD) framework is introduced along with a quartic problem with a closed-form solution in each iteration. Numerical results are provided to assess the designed waveform set in terms of Doppler tolerance and compatibility of detecting weak targets and high speed targets.

This paper deals with the design of the Doppler filter bank for non-uniform pulse repetition interval (PRI) radar in the presence of signal-dependent clutter. The worst-case Signal-to-Interference-Noise-Ratio (SINR) at the output of the Doppler filter bank is explicitly maximized along with the mainlobe gain restriction. To solve the resultant non-convex problem, a Parallel Block Improvement (PBI) algorithm is proposed to monotonically increase the worst-case SINR to convergence. In particular, each block involves a non-convex problem associated with the each filter, which is approximated through a series of convex problem. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

This paper considers the coherent integration problem for moving target detection in phased-Multi-In Multi-Out (MIMO) radar. An integration method based on entropy is proposed to realize the coherent accumulation of target signal energy of the multi-subarrays for the phased-MIMO radar. Firstly, the two dimension (fast time-slow time) signal model is established. Secondly, the differences of signal phase along the fast time dimension are eliminated via frequency shift operation after range alignment, while the Doppler frequency induced by target’s velocity is then compensated using slow time Fourier Transform (FT). Thirdly, the phase differences caused by the time delay are estimated and compensated by using an entropy-based approach. Finally, the Doppler-location searching is presented to achieve the coherent accumulation. Simulation examples are given to demonstrate the effectiveness of the proposed method.

In this paper, the direction of arrival (DOA) estimation in terms of weak target is addressed under strong mainlobe jamming. Specifically, a sum-difference beam signal model for multiple subarrays is introduced accounting for target echo and mainlobe jamming. For each subarray, the target and jamming signals are separated from the received sum-difference channel signals via blind source separation (BSS) algorithm. Further, monopulse technique is exploited to estimate DOA of weak target by combining all the separated target signals of each subarray. Finally, the effectiveness and performance of the proposed technique are analyzed via numerical simulations. The results show that the introduced procedure is capable of achieving rejection of the mainlobe jamming, while accurately estimating DOAs of target and jamming.

Non-line-of-sight (NLOS) target detection utilizing multipath plays an important role in anti-terrorism, urban warfare, indoor rescue and intelligent driving. In this paper, an imaging method based on single-channel ultra-wideband (UWB) radar and dual-view observation is proposed for detecting NLOS targets. First, the cause of producing ghosts from a single view in a previous study is analyzed in detail. On this basis, with focusing on dual-view observation, the ghost problem is handled at two levels. From the level of the radar echo, path partition is conducted with the utilization of dual-view observation, solving the problem of uneven energy among multipaths. After that, two images are generated by multipath imaging, respectively, which reduces the accumulation of ghosts. From the level of the image, owing to the distinct distribution of ghosts between the imaging result, the ghosts at different locations are eliminated by image fusion. Experimental results demonstrate that compared with single view and direct multipath imaging, the approach based on dual-view observation successfully eliminates most ghosts while retaining the targets in both single-target and double-targets scenarios, which verifies the effectiveness of the method.

Black-box quantum state preparation is a fundamental primitive in quantum algorithms. Starting from Grover, a series of techniques have been devised to reduce the complexity. In this work, we propose to perform black-box state preparation using the technique of linear combination of unitaries (LCU). We provide two algorithms based on a different structure of LCU. Our algorithms improve upon the existed best results by reducing the required additional qubits and Toffoli gates to 2log(n) and n, respectively, in the bit precision n. We demonstrate the algorithms using the IBM Quantum Experience cloud services. The further reduced complexity of the present algorithms brings the black-box quantum state preparation closer to reality., Comment: 13 pages, 3 figures

This paper focuses on the joint synthesis of constant envelope transmit signal and receive filter aimed at optimizing radar performance in signal-dependent interference and spectrally contested-congested environments. To ensure the desired Quality of Service (QoS) at each communication system, a precise control of the interference energy injected by the radar in each licensed/shared bandwidth is imposed. Besides, along with an upper bound to the maximum transmitted energy, constant envelope (with either arbitrary or discrete phases) and similarity constraints are forced to ensure compatibility with amplifiers operating in saturation regime and bestow relevant waveform features, respectively. To handle the resulting NP-hard design problems, new iterative procedures (with ensured convergence properties) are devised to account for continuous and discrete phase constraints, capitalizing on the Coordinate Descent (CD) framework. Two heuristic procedures are also proposed to perform valuable initializations. Numerical results are provided to assess the effectiveness of the conceived algorithms in comparison with the existing methods., Comment: Submitted to IEEE Transactions on Signal Processing

Quantum-inspired neural network is one of the interesting researches at the junction of the two fields of quantum computing and deep learning. Several models of quantum-inspired neurons with real parameters have been proposed, which are mainly used for three-layer feedforward neural networks. In this work, we improve the quantum-inspired neurons by exploiting the complex-valued weights which have richer representational capacity and better non-linearity. We then extend the method of implementing the quantum-inspired neurons to the convolutional operations, and naturally draw the models of quantum-inspired convolutional neural networks (QICNNs) capable of processing high-dimensional data. Five specific structures of QICNNs are discussed which are different in the way of implementing the convolutional and fully connected layers. The performance of classification accuracy of the five QICNNs are tested on the MNIST and CIFAR-10 datasets. The results show that the QICNNs can perform better in classification accuracy on MNIST dataset than the classical CNN. More learning tasks that our QICNN can outperform the classical counterparts will be found., Comment: 12pages, 6 figures

MIMO through-wall-radar is traditionally utilized to perform forward-view imaging (FVI) for the hidden targets behind the wall. Due to the limited view angle and target occlusion, FVI usually suffers from the problem of target missing in the case of multiple targets. In this paper, a novel down-view imaging (DVI) mode is presented to obtain robust three-dimensional (3D) multiple target images without target missing. Specifically, the propagation characteristic is analyzed for the 3D DVI in an enclosed building space. Then the DVI algorithm is introduced. Specifically, the back-projection algorithm is applied to form a 3D image and the exponential phase coherence factor (EPCF) weighting is adopted to suppress the multi-path ghosts. Based on the gprMax simulation results, in the presence of target occlusion, it is demonstrated that the presented DVI method has the ability to implement robust imaging for multiple targets, while the FVI misses the obscured target.

This paper deals with the constrained design of the weighting coefficients to achieve a desired receiving beampattern for wideband sparse phased array. A novel optimization model that minimizes beampattern peak sidelobe level in a space-frequency region of interest (SFRI) along with mainlobe level and weight coefficients quantitative restrictions is proposed. An Alternating Direction Penalty Method (ADPM) formulation to tackle the resultant nonconvex problem is introduced. In each iteration, the original problem is executed via solving multiple tractable subproblems. The simulation results highlight that in comparison with the available approach, the proposed method realizes a deeper nulling in the given space-frequency region capable of rejecting the strong interference.

This paper deals with the joint design of transmit waveform and receive filter improving radar Signal to Interference plus Noise Ratio (SINR) in signal-dependent interference and spectrally contested-congested environments. The injected interference energy on each common band is precisely controlled for spectral compatibility. Besides, the transmit signal complies with constant envelop and similarity constraints to bestow some attractive waveform characteristics. To handle the resulting transceiver design problem, an iterative optimization procedure with a polynomial computational complexity is developed leveraging the Coordinate Descent (CD) framework. Finally, numerical results are provided to highlight the effectiveness of the proposed joint transmitter and receiver design technique.

Human activity recognition (HAR) has long been a research hotspot in anti-terrorism, patient monitoring and other applications. Throughout the current research progress, the realtime recognition of unknown start and end time has not been well solved. In addition, the recognition accuracy of blocking human in the wall-through scene needs to be improved. To tackle this issue, this paper proposed a novel range profile sequence driven end-to-end model, which specifically employed random cropping training method. Then, we carried out experiments on actual Through-the-Wall Radar(TWR), finally achieved an average accuracy of 97.6% on four common activities, and the most significant is that our method can immediately output recognition results, without waiting for the end of activity.

This paper deals with the joint design of transmit waveform and receive filter to improve the clutter rejection capability for non-uniform pulse repetition interval (NUPRI) airborne radar. Specifically, a multipulse echo model accounting for a moving point-like target and signal-dependent clutter is first established. Then the echo is processed via the matching filter and the windowed Doppler filter bank to obtain the two-dimensional range-Doppler plane. Further, the integrated sidelobe level of clutter (ISLC) that spreads the region of the target of interest in the plane is considered to minimize forcing constant modulus constraint on the waveform. To solve the resultant nonconvex problem, the Sequential Greedy Optimization Algorithm (SGOA) through alternately updating the receive filter and transmit waveform is proposed to monotonically decrease ISLC to converge. In each iteration, the iterative algorithm based on coordinate descent (CD) framework and the sequential convex approximation algorithm are, respectively, explored to obtain the transmit waveform and receive filter. Finally, the performance of the proposed algorithm is assessed through numerical simulations showing its capability to suppress signal-dependent clutter.

This paper studies the unimodular sequence design with the finite alphabet case for a cognitive radar in order to achieve the desired auto-correlation. An iteration algorithm based on Inexact Alternating Direction Penalty Method (IADPM) framework is developed to minimize the Weighted Integrated Sidelobe Level (WISL). In each iteration, it splits the NP-hard problem into two subproblems via an introduced auxiliary variable, while locally increasing the penalty factor involved in the IADPM framework. The proposed algorithm is shown to converge for any initialization under some mild conditions and avoids the non-convergence problem of ADMM when handling the NP-hard problems. Finally, the auto-correlation performance and convergence speed of the proposed algorithm are evaluated against the state-of-the-art methods. Results show that our proposal outperforms the state-of-the-art competing methods while providing the favorable performance-complexity balance.

In this paper, the reconstruction of building layout and interior object is considered only utilizing the attenuation power of transmitted narrow-band signal. More specifically, bistatic radars scan the entire unknown area in the form of tomographic imaging to complete the reconstruction issue. Based on the idea of knowledge assistance, we propose a priori information constrained algebraic reconstruction technology (PIC-ART) algorithm, which presents better performance than current state-of-the-art algorithms in multi-media area. Besides, we first utilize the electromagnetic (EM) simulation software to verify the feasibility of the established model under omnidirectional antennas. The EM simulation results have validated the effectiveness of the proposed algorithm.

In this chapter, the non -polynomial (NP) -hard quadratic optimization problem under the constant modulus and similarity constraints is considered. Low -complexity algorithms for both the continuous and finite alphabet phase cases are developed. Specifically, an iterative algorithm for the continuous phase case referred to as iterative algorithm for continuous phase case (IA -CPC) is introduced to successively optimize the objective function with closed -form solutions and its convergence is proven analytically. The computational complexity of IA -CPC is only linear in the number of iterations and polynomial with the size of the design variables. Moreover, for the discrete phase case, the design variables are divided into K blocks. In each iteration, the exhaustive search is leveraged to sequentially optimize each block assuming the remaining K - 1 blocks are fixed. The computational burden is related to the size of the design variables, the number of design variables in each block and the number of discrete phases. Finally, the performance of the new techniques in comparison with the existing approaches available in the open literature are assessed by considering radar code design applications. Numerical results are provided to show the effectiveness of the proposed algorithms in terms of the achieved objective function value and computational complexity. More examples related to radar applications can be found.

In this chapter, the authors cope with the joint optimization of fast-time LAF and waveform spectrum. A novel objective function with a weighting factor to trade-off the WISL with respect to range-Doppler sidelobes and the spectral stopband energy is proposed. The energy and PAR constraints are forced on the designed signal, which further generalize the problem we developed. To handle the resulting non-convex quadratic problem, a new iterative sequential quadratic optimization (ISQO) algorithm is proposed [30]. In each iteration, it transforms the optimization problem into a tractable quadratic subproblem that is further converted to a linear optimization problem with a closed-form solution by resorting to the first-order Taylor expansion. The proposed algorithm has polynomial time complexity that is linear with the number of iterations and polynomial with the size of the designed waveform and the number of the considered range-Doppler bins. The simulation results highlight the superiority of the proposed algorithm contrast to AISO algorithm in terms of the objective value, computational time, and the capability of prohibiting the sidelobes of a strong return and narrowband spectral interferences from masking weak targets. The experimental results show that the hardware limitation influences the level suppressed in both energy spectral density (ESD) and WISL and confirms the effectiveness of spectral coexistence in prohibiting the sidelobes suppressed in the LAF rising.

This paper considers a quadratic optimization problem in radar discrete-phase waveform design under similarity and constant modulus constraints. A computationally efficient iterative algorithm based on the Alternating Direction Penalty Method (ADPM) framework is proposed. In each iteration, it converts the considered problem into two subproblems with closed-form solutions via an introduced auxiliary variable, while locally increasing the penalty factor involved in the ADPM framework. The proposed algorithm is ensured to converge for any initialization under some mild conditions and avoids the non-convergence problem of the Alternating Direction Method of Multipliers (ADMM) when handling the NP-hard problems. Finally, numerical simulations demonstrate that the proposed algorithm can outperform their counterparts by providing better objective values.

Localization for moving multi-target in urban environment is difficult because of the ambiguities of targets’ different multipaths. This paper considers the targets localization problem via non-line-of-sight (NLOS) signals in an L-shaped corner environment using the time-division multiple-input-multiple-output (TD-MIMO) imaging radar. We first analyse the propagation characters of the multipath signals and establish the signal model of targets hiding behind corner for TD-MIMO. Then, a novel multi-target localization algorithm by exploiting the multipath ghosts after applying back-projection (BP) imaging algorithm is proposed. Finally, we illustrate the validation the proposed algorithm via several simulation results.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique.

Long-time coherent accumulation can effectively improve the detection and motion parameters estimation ability of radar for maneuvering weak target. Nevertheless, the range cell migration (RCM) and Doppler spread caused by high-speed maneuvering target may make the detection and estimation performance to degrade seriously. In this paper, a new coherent integration (CI) approach based on modified axis rotation transform (MART) and Lv’s transform (LVT), i.e., MART-LVT, is proposed to achieve the detection and parameter estimation of target with high velocity and acceleration motion. The MART-LVT method can correct RCM via rotating the coordinate locations of echo data and obtain the CI and parameters estimation of target signal via LVT. In comparison with the existing representative coherent detection methods, MART-LVT can achieve better integration performance and detection probability. Simulations and real data demonstrate the effectiveness of MART-LVT.

This study deals with the problem of range-velocity joint deception jamming suppression for a single-input multiple-output (SIMO) radar system. The Doppler diversity and spatial geometric correlation are exploited to distinguish the jamming and multiple moving targets. First, the echoes in each receiver are mapped into range-Doppler plane by pulse compression and slow-time Fourier transform operations. Then, in each receiver, the range and Doppler frequency of the targets are extracted. Third, based on differences between the physical target echoes and the jamming signals in both range and Doppler dimensions, two identification algorithms are developed to discriminate the false targets in fusion centre. Finally, through numerical simulations, the performance of the proposed suppression scheme is evaluated, showing its capability to suppress the range-velocity joint deception jamming and locate the multiple physical targets with high precision.

For multi-input multi-output (MIMO) through-wall radar imaging (TWRI), multipath ghosts and side/grating lobe artifacts degrade the imaging quality of the obscured targets inside an enclosed building, therein hindering target detection. In this paper, an approach based on two stacked generative adversarial nets (GAN) is proposed to achieve multipath and side/grating lobe suppression with regard to MIMO TWRI. Specifically, the Stage-I GAN constructs a spatial structure mapping from the original input images to the Stage-I GAN output images with the suppressed multipath ghosts. However, the side/grating lobe artifacts are intentionally preserved in the stage-I GAN as additional constraint information to prevent uncontrollable over-fitting. Then, the Stage-II GAN takes the output images of Stage-I GAN as input to suppress the side/grating lobe artifacts. Extensive electromagnetic simulations and comparisons demonstrate that the proposed approach achieves better suppression of multipath ghosts and side/grating lobe artifacts and other significant superiorities, including priori wall information not being required, the preservation of weak targets, and robustness for different array deployments and building layouts.

This paper deals with the robust joint design of a radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case signal-to-interference-plus-noise-ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

In this paper, we propose a new algorithm that designs a transmit beampattern for Multiple-Input Multiple-Output (MIMO) radar considering coexistence with other wireless systems. This design process is conducted by minimizing the deviation of the generated beampattern against an idealized one while enforcing the waveform elements to be constant modulus and in the presence of spectral restrictions, which translates to a quadratic waveform constraint. This leads to a hard nonconvex optimization problem due to simultaneous presence of the constant modulus constraint (CMC) and spectral constraint (SpecC). In this work, we employ the geometrical structure of CMC, that is we redefine this constraint as an intersection of two sets. This definition allows us to handle the design problem in more tractable way. The proposed Iterative Beampattern with Spectral design (IBS) algorithm solves a sequence of convex problems such that constant modulus is achieved at convergence. Furthermore, we show that at convergence the obtained solution satisfies the Karush-Kuhn-Tucker (KKT) conditions of the aforementioned non-convex problem. Finally, we evaluate the proposed algorithm over challenging simulated scenarios, and show that it outperforms the state of the art competing methods.

This study proposes a new radar-based human body and limb motion recognition method that exploited the temporal sequentiality of the motions. A stacked gated recurrent units network (SGRUN) is adopted to extract the dynamic sequential human motion patterns. Since the time-varying Doppler and micro-Doppler signatures can commendably represent such motion patterns, the spectrogram is utilised as the input sequence of the SGRUN. Numerical experiments verify that an SGRUN with two 34-neuron gated recurrent unit layers well classifies and recognises six distinct human body and limb motion types.

This study deals with the target detection problem embedded in multiple range jamming which is usually employed to mask the useful signal or deceive the radar systems. Exploiting the observation that the waveform of the range jamming would be not identical to the probing signal of the radar system, the authors propose a coupled sequential estimation algorithm to obtain simultaneously the range profiles of target and jamming. Each iteration of the proposed algorithm designs a set of linear filters for each range bin based on the minimisation of the output average power by utilising the obtained range profiles of target and jamming at the last iteration. The proposed algorithm is capable of eliminating the jamming in the target range profiles and shares very low range sidelobes. The numerical simulations are provided to prove the effectiveness of the proposed algorithm also in comparison with the matched filter.

This paper is focused on the design of the shared signal for a dual-use system with radar detection and communication functions. To achieve a good aperiodic autocorrelation property of the shared signal for radar detection, the $l_{p}-$ norm of the autocorrelation sidelobes is minimized. For a communication function, the nulling of one or more frequency bands enables information to be embedded and an energy minimization detection method is introduced to demodulate communication information. In addition, electromagnetic spectral compatibility and unimodular constraints on the designed signal are considered. The resulting problem is a higher order, multi-dimensional, and multi-objective constrained optimization, which is efficiently handled through an iteration direct search algorithm. Finally, the performance of the proposed method is assessed and analyzed through numerical simulations in terms of the peak sidelobe level, average sidelobe level, and bit error rate.

This paper deals with the problem of local ambiguity function shaping in the presence of narrow-band spectral interferences (e.g., the communication signal and jamming), which has significant applications in the radar detection field. An objective function involving the weighted integrated sidelobe level and the spectral stopband energy is developed as a figure of merit to minimize. Besides, the energy and the peak-to-average ratio restrictions are forced on the probing waveform. To handle the resulting non-convex quartic optimization problem, an iterative sequential quartic optimization algorithm sharing polynomial time complexity is proposed. In each iteration, it decomposes the original problem into two alternately tractable quadratic subproblems both of which can be converted to linear optimization problems with closed-form solutions. Finally, the numerical simulations are provided to assess the effectiveness of the proposed algorithm. The results show that the proposed algorithm outperforms some available counterparts in the aspect of the convergence speed and the capability to prohibit the narrow-band spectral interferences and the sidelobes of a strong return from masking weak targets.

This study deals with the multipaths suppression problem in a shielded environment with a through-wall imaging radar. The authors propose a similarity-based algorithm to eliminate the multipath ghosts for the scenario where some targets overlap with other targets’ multipath ghosts. Specifically, the authors first extract the connected domains of the radar image after binarisation, and then put them into a vector in ascending order based on the distances between their average coordinates and the array centre. Second, they divide the vector into two sub-vectors and calculate the distances between each sub-vector and all sensor locations along different paths. Third, they construct the similarity matrix using these two sub-vectors and further derive the matrix elements larger than the similarity threshold. Finally, decision rules are designed to eliminate the multipath ghosts without losing true targets. At the analyse stage, the authors assess the effectiveness of the proposed approach through simulation results showing its ability of well removing multipath ghosts.

This paper deals with constant modulus waveform design in spectrally dense environments assuming a discrete phase code alphabet. The goal is to optimize the radar detection performance while rigorously controlling the injected interference energy within each shared band and enforcing a similarity constraint to manage some relevant signal features. To tackle the resulting NP-hard optimization problem, an iterative procedure characterized by a polynomial computational complexity, is introduced leveraging the coordinate descent method. Numerical results are provided to show the effectiveness of the technique in terms of detection performance, spectral shape and autocorrelation features.

Quantum arithmetic in the computational basis constitutes the fundamental component of many circuit-based quantum algorithms. There exist a lot of studies about reversible implementations of algebraic functions, while research on the higher-level transcendental functions is scant. We propose to evaluate the transcendental functions based on a novel methodology, which is called qFBE (quantum Function-value Binary Expansion) method. This method transforms the evaluation of transcendental functions to the computation of algebraic functions in a simple recursive way. We present the quantum circuits for solving the logarithmic, exponential, trigonometric and inverse trigonometric functions based on the qFBE method. The efficiency of the circuits is demonstrated on a quantum virtual computing system installed on the Sunway TaihuLight supercomputer. The qFBE method provides a unified and programmed solution for the evaluation of transcendental functions, and it will be an important building block for many quantum algorithms., Comment: 21 pages, 7 figures

This letter deals with the robust joint design of radar transmit waveform and receive filter bank in a background of range unambiguous signal-dependent clutter. Assuming an unknown Doppler shift for the target, the worst-case Signal-to-Interference-plus-Noise-Ratio (SINR) at the output of the receive filter bank is considered as the figure of merit. The transceiver design is pursued considering a max-min optimization problem with some constraints on the transmit energy, similarity, and signal dynamic range. Hidden convexity is shown and a procedure to derive optimal waveform and filters is given. Simulation results highlight the effectiveness of the devised method.

This paper considers the design of multiple-input multiple-output (MIMO) radar waveforms to approximate a desired beampattern. The problem is formulated as minimizing the weighted squared-error between the synthesized beampattern and the desired beampattern, and the spacial cross-correlation sidelobes with the energy and peak-to-average ratio (PAR) restrictions on the waveforms, which thus leads to a non-convex quartic optimization problem. An iterative sequence quartic optimization (ISQO) algorithm with adaptively changing weights to match the template is proposed. The numerical results are provided to demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art method.

This paper focused on a beampattern synthesis problem with the constrained subarray layout constrained optimization. We first propose a thinned array architecture, and establish a beampattern model constrained by subarray layout. In order to achieve the optimal layout of subarrays and the optimal design of weight vector, we establish an optimal model. Its constraints include beamforming constraints and subarray layout constraints. For the latter, we use a new approximation method to constrain the subarray layout effectively. Next, we use the alternating direction method of multipliers (ADMM) framework to decompose this optimization problem into three optimization sub-problems which are easy to be solved, and solve them iteratively. Finally, several numerical simulations show the results of beampattern synthesis for the proposed array structure under the different beampattern constraints, and assess the validity of the proposed method.

This paper deals with the joint design of Multiple-Input Multiple-Output (MIMO) radar transmit waveform and receive filter to enhance multiple targets detectability in the presence of signal-dependent (clutter) and independent disturbance. The worst-case Signal-to-Interference-Noise-Ratio (SINR) over multiple targets is explicitly maximized. To ensure hardware compatibility and the coexistence between MIMO radar and other wireless systems, constant modulus and spectral restrictions on the waveform are incorporated in our design. A max-min non-convex optimization problem emerges as a function of the transmit waveform, which we solve via a novel polynomial-time iterative procedure that involves solving a sequence of convex problems with constraints that evolve with every iteration. We provide analytical guarantees of monotonic cost function improvement with proof of convergence to a solution that satisfies the KarushKuhnTucker (KKT) conditions. By simulating challenging practical scenarios, we evaluate the proposed algorithm against the state-of-the-art methods in terms of the achieved SINR value and the computational complexity.

In this paper, we consider the problem of phase-only beampattern synthesis with nulling for a Linear Antenna Array systems. It involves shaping the radiation pattern using fixed-magnitude excitation signals. Imposing the fixed-magnitude,i.e. phase only, constraint on the excitation signals makes the problem an NP-hard nonconvex. We propose an iterative solution, which is based on ADMM and Proximal frameworks, to tackle the challenging problem. The proposed solution has a significant contribution in providing fast synthesizing method for the radiation pattern that is capable of mitigating interferences coming from other radiators. Finally, we provide simulation results to validate the effectiveness of the solution. Based on the simulation results, our solution performs better than a state-of-the-art solution in terms of null generation and convergence speed.

This paper addresses a target imaging method for non-line-of-sight (NLOS) target shielded by surrounding in the L-junction scenario, exploiting ultra wideband (UWB) multiple-input multiple-output (MIMO) radar. We analyze the electromagnetic wave diffraction and reflection propagation in the L-junction scenario and establish the echo model. An NLOS target imaging method based on multipath exploitation of the diffraction and first-order reflection is proposed. Moreover, we also present a multiplication fusion method to suppress the high-order multipath ghosts. Finally, simulation results validate the validity of the multipath-exploitation-based target imaging method.

In this paper, we consider the reconstruction problem of building layout and interior objects only using the attenuation power. Different from the traditional imaging mode, we introduce a novel scanning mechanism based on the bistatic radar. According to the attenuation characteristics of electromagnetic waves penetrating the objects, the received signal power is utilized to complete the reconstruction. We propose a fast building layout reconstruction algorithm which combines the filtered back projection (FBP) and sub-regional optimization. The algorithm not only can greatly improve operational efficiency, but also effectively solve the star-shaped tail problem of the projection algorithm. The numerical results have validated the effectiveness of the algorithm.

Under the circumstance of simultaneously scanning multiple regions in an environment which can change as time goes by, the authors study an optimal algorithm to solve antenna deployment problem for MIMO radar systems here. It is solved by multi-objective particle swarm optimisation algorithm (MOPSO) combining an autoregressive (AR) prediction model (MOPSO-AR). In a time period and dynamic environment, the MOPSO-AR method uses the previous optimal information to calculate the current deployment schemes before PSO optimisation starts up. It greatly reduces computational load and the error of the solutions. First, by discretising the time period into several time intervals, the problem in each time interval can be seen as a static problem. However, there may be relationship between these time intervals. Second, use the previous information and an AR model to predict temporally optimal solutions. Then, to get the exact optimal solutions, the predicted solutions and PSO method was applied to compute. Simulations show that the prediction strategy improves algorithm performance.