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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 paper deals with the robust waveform design of multiple-input multiple-output radar to improve target detectability embedded in signal-dependent interferences. Two iterative algorithms with ensuring convergence properties are introduced to maximize the worst case signal-to-interference-plus-noise ratio (SINR) over steering matrix mismatches under the constant modulus and similarity constraints. Each iteration of the proposed algorithms splits the high-dimensional problem into multiple one dimensional problems, to which the optimal solutions can be found in polynomial times. Numerical examples are provided to assess the capabilities of the proposed techniques in comparison with the existing methods in terms of the SINR and the computational times for both the continuous and discrete phase cases of the probing signal.

In this paper, a computationally efficient coherent detection and parameter estimation algorithm via symmetric autocorrelation function (SAF) and scaled Fourier transform (i.e., SAF-SFT) is proposed, involving range cell migration (RCM) and Doppler spread (DS) within the coherent integration (CI) time. In particular, the first SAF and SFT operations are applied to achieve the range and velocity estimations after the generalized keystone transform. With the estimations, the remaining RCM induced by target’s velocity could be removed and the target signal could be extracted along the range cell. Then the second SAF and SFT operations are performed on the extracted signal, where the target energy could be coherent integrated and the acceleration estimation can be obtained. Cross term of SAF-SFT is also analyzed and its characteristic indicates the applicability in the scenario of multi-targets. Detailed comparisons of SAF-SFT with several typical algorithms with respect to computational cost, detection probability and parameter estimation ability show that the SAF-SFT could strike a balance between computational cost and detection probability as well as the estimation performance. Simulation results and real test experiment are given to verify the SAF-SFT based approach.

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.

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.

We study the spectral constrained design of Multiple-Input Multiple-Output (MIMO) radar beampattern in an effort to the coexistence with the communication systems. We develop an optimization model relying on the minimization of beampattern Integrate-Sidelobe-Level (ISL) along with spectral, mainlobe width, Peak-to-Average Ratio (PAR) and energy constraints. To cope with the resultant non-convex problem, we introduce a novel polynomial-time iterative procedure that requires solving a series of constrained convex problems that evolve with each iteration. In particular, the proposed algorithm ensures that the monotonic decrease of ISL and the convergent solution is a KarushKuhnTucker (KKT) point. Numerical results are provided to assess the proposed algorithm in terms of the beampattern and spectral compatibility.

To solve the degradation of clutter suppression effect of traditional radar signal processing method in the complex battlefield environment, especially for the airborne radar with more complex clutter background, in this paper, we use Conditional Generative Adversarial Nets (CGAN) to train a generator which can find interested targets in a complex clutter distribution. It exploits the distribution of interested targets as supervised information so as to assist the extraction of high-resolution features. Moreover, for the time-varying battlefield environment, only by updating model parameters can our method adapt the change of distribution of radar echo data. The effect of our method on clutter suppression is validated based on the experiments on the radar datasets.

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.

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.

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 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 multipath modeling and exploitation problems for multiple input multiple output through-the-wall imaging radar. A multipath propagation model referring to two parallel walls is established, and the position distribution characteristics of the multipath ghosts in radar image are analyzed by means of geometric approximation. Therein, we find a feature that the locations of the walls can be obtained using the geometric relationship between the true target and its multipath ghosts. After utilizing constant false alarm rate technique and clustering approach, the coordinates of the multipath ghosts and the concealed target are achieved, and the positions of the two walls are obtained. Finally, simulation and experimental results validate the correctness of the multipath model and the validity of the multipath-exploitation-based wall localization method.

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 paper considers the robust transmit–receive design for the detection of a moving point-like target embedded in signal-dependent clutter. In particular, we focus on the phase-only waveforms, sharing either the continuous or the finite alphabet phases and present iterative sequential optimization (ISO) algorithms to sequentially improve the worst-case signal-to-interference-plus-noise ratio (SINR) over clutter statistics. At each iteration, ISO involves multiple fractional programming problems that can be efficiently solved by either Dinkelbach's procedure with closed-form solutions for the continuous phases or the one-dimensional searching method for the discrete phases case. The ISO algorithms enjoy a polynomial complexity in terms of computational burdens. The effectiveness of ISO is confirmed experimentally in terms of the achieved SINR and computational efficiency. Our results also highlight that the design is robust against possible inaccuracies in the prior knowledge of the signal-dependent clutter and even velocity deceptive jamming.

High-speed target often suffers from the range walk (RW) problem within the process of long-time coherent integration (CI). The aim of this paper is to present a new coherent detection algorithm, i.e., modified location rotation transform (MLRT), which could effectively correct the RW and estimate the velocity of the high-speed target. In particular, the MLRT algorithm could rotate the coordinate locations of echo data to remove the RW and estimate the velocity via searching the rotation angles. Then, the CI of target's energy is obtained via the Fourier transform along the slow-time direction. The advantage of the MLRT is that it could achieve superior integration and detection performance and avoid the blind speed sidelobe effect without increasing the computational complexity, in comparison with other representative algorithms. Simulations and real data processing are provided to evaluate the efficacy and feasibility of the proposed algorithm.

In this letter, we consider the problem of multipath ghost suppression for through-the-wall imaging radar. Exploiting the fact that these locations of the multipath ghosts depend on while the target location is independent of the array configuration, we present a novel framework via array rotating to eliminate the multipath ghosts. Specifically, we first rotate the array with multiple different array rotation angles. Then, multiple images are derived using back-projection imaging algorithm. Finally, the incoherent arithmetic fusion method is applied to yield a ghost-free image. The proposed approach has two advantages for multipath ghost suppression. First is the simplicity of the operation, and the second is that it will not be affected by the incorrect wall parameters. Ghost suppression performance of the proposed approach is evaluated via numerical simulations.

This paper considers the constrained waveform design for Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integration Sidelobe Level (ISL) to optimize the waveform covariance matrix enforcing a uniform elemental power restriction as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we present the existing Cyclic Algorithm (CA) and a new Sequential Iterative Algorithm (SIA) to devise the waveform under a constant modulus constraint, and a similarity constraint to allow the designed waveform sharing the similarity feature with a given reference waveform. In particular, the proposed SIA directly optimizes the objective function and its each iteration turns the multidimensional optimization problem into multiple one-dimensional optimization problems with closed-form solutions. Finally, we assess the effectiveness of the proposed technique through numerical simulations in comparison with CA.

This paper considers the problem of multipath ghosts elimination for the through-wall radar imaging. We formulate a discrete signal model in the presence of first-order multipath, and propose an imaging dictionary based algorithm to suppress the multipath ghosts. Specifically, first, several estimated images are obtained via two different kinds of imaging dictionaries for the focus delays related to the direct propagation path and the multipaths, respectively. We find that all the estimated images possess large values around the genuine target positions and the multipath ghosts of them do not overlap with each other. Then, based on this feature, these images are fused via incoherent multiplication image fusion method to yield a multipath ghosts free image. Finally, the simulation and real data are used to prove the validity of the proposed approach.

This paper devises a new constant false alarm rate (CFAR) detection scheme to deal with the problem of radar target detection in heterogeneous environment. The proposed scheme, called “clustering-CFAR detector,” is data dependent and composed of three stages: an adaptive clustering procedure that, exploiting the recorded measurements of the clutter environment, divides the detection area into different classes to provide auxiliary information, a dynamic reference cell selector that chooses appropriate secondary data according to the classes, and a conventional CFAR processor to make the final decision about the target presence. The performance of “clustering-CFAR detector” is analyzed by computer simulation and public radar measured data (IPIX data and MSTAR data), and compared with existing CFAR detectors. The results show that the new detector achieves a better performance in the aspects of terrain classification, control of false alarm points, and probability of detection.

In this paper, considering multiple regions for interference simultaneously, an optimal antenna deployment problem for distributed multistatic radar is investigated. The optimal antenna deployment problem is solved by proposing an antenna deployment method based on Multi-Objective Particle Swarm Optimization (MOPSO). Firstly, we construct a multi-objective optimization problem for multistatic radar antenna deployment by choosing the interference power densities of different regions as objective functions. Then, to obtain the optimal deployment result without wasting time and computational resources, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the MOPSO optimization process efficiently when the optimal antenna deployment algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

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.