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This paper proposes a pulse compression method by using the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal without inserting any cyclic prefix (CP). A modified interference approximation-based pulse compression method (MIA-based PCM) is proposed, by exploiting the demodulated real weights of the OQAM-OFDM signal, the inter-symbol and inter-subcarrier interferences to enhance the performance. The MIA-based PCM achieves approximated inter-range-cell interference (IRCI)-free pulse compression and perfect Doppler estimation, which has both the benefits of power efficiency and good Doppler tolerance. Finally, numerical simulation results are provided and discussed.

Long time coherent integration (LTCI) technique enhances radar detection of maneuvering weak targets. Unfortunately, range migration (RM) and Doppler frequency migration (DFM) would occur during the integration period. Existing LTCI methods are all based on the assumption that the target is of single motion model (i.e., the target's motion model is fixed) during the long integration time. However, with increasing target's maneuverability and long observation time (needed to detect weak targets), the target's motion model is often changing (i.e., target is of multiple motion models) during the integration time, which would reduce the performance of existing LTCI methods significantly. In this paper, we address the LTCI problem for detecting a maneuvering weak target with multiple motion models, where the target signal model is first established. Thereafter, the short-time generalized Radon Fourier transform (STGRFT) based LTCI algorithm is proposed. The STGRFT method could not only eliminate RM and DFM but also estimate the model-changing point (i.e., the time when target's motion model changes) and accumulate the target energy distributed in different motion stages (corresponding to different motion models). Finally, numerical and real-data experiments are carried out to demonstrate its effectiveness. The results confirm that in terms of integration gain, input–output signal-to-noise ratio performance and detection ability, STGRFT-based method is superior to the existing popular LTCI algorithms.

In this paper, an optimal multistatic radar deployment problem is studied under the assumption of the non-connected deployment region. By dividing the non-connected deployment region as an union of multiple connected subregions, the deployment problem can be modeled as a mix-integer nonlinear programming problem (MINP). The optimization variables of this deployment problem consist of selection of subregion and location optimization within every subregion. To solved this deployment problem, we first construct the mathematical optimization model, whose optimization objectives include the effective coverage and distribution uniformity of radar. Then, to alleviate the shortage of solving MINP in the conventional method, a pertinent multi-objective particle swarm optimization (MOPSO) variant for MINP is developed by modifying the dynamics of the particle motion for MOPSO. Finally, numerical results are provided to verify the validity of the proposed algorithm.

In this paper, in order to improve the efficiency of tasks execution of multifunction radar network, a dynamic tasks scheduling problem is investigated. Considering the uncertainty of dynamic tasks request times, a dynamic tasks method that based on reinforcement learning is proposed. Firstly, we construct a Markov Decision Process (MDP) for the multifunction radar network executing tasks, and choose the dropped ratio of tasks as the evaluation criterion. Then, a model-free reinforcement learning framework for multifunction radar network executing tasks is formulated. Under the framework, we design the action space for this reinforcement learning question, and a method of tasks scheduling based on Q-learning is proposed. Finally, simulation results are provided to verify the validity of proposed method.

Considering the serious performance degradation of cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM) radar range reconstruction caused by chaotic Doppler disturbance, an inter-carrier interference (ICI) cancellation problem is investigated in the scene of multiple unknown moving targets. The ICI cancellation problem is solved by proposing a two-step subcarrier alignment method. Firstly, we construct the signal model and separate the intra-pulse and inter-pulse Doppler modulation term. Then, the two-step orthogonal alignment of the subcarrier method is put forward to against ICI by Fourier analysis on the converted signal model. Later, inter-range-and-carrier (IRCI) free range reconstruction is implemented with the assistance of the CP-OFDM pulse compression method. Finally, numerical results are provided to verify the validity of the proposed algorithm.

Considering a multi-stage stochastic task, in which a multi-static radar system (MSRS) is applied to assist with missile interception, the authors study an optimisation problem of radar resource management. Specifically, under restriction of a fixed energy budget, the authors devote to minimise the loss, which is caused by the unsuccessfully intercepted missiles, through optimal power allocation (OPA) of MSRS within multiple stages. The design of OPA can be translated into a sequential decision-making problem. The authors formulate the problem through variable definition and modelling the missile interception procedure. As the authors need to consider the randomness of multiple coupled stages and jointly allocation power between multiple radar nodes, to solve the proposed problem is of huge computational load. The authors propose a solution that combines with reinforcement learning and particle swarm optimisation. Comparing with the uniform power allocation scheme, the simulation results demonstrate that the OPA scheme designed by the proposed method is capable to achieve preferable and more stable performance for the whole missile interception. The authors' contributions include a novel optimisation resource management model for a multi-stage stochastic task and an effective solution for the optimal resource management scheme.

The authors consider an optimisation problem of multistatic radar system (MSRS) deployment, which includes both the antenna placement and the transmitted power allocation. To improve the surveillance performance of MSRS that equipped with different detection methods, they mainly aim at two goals: (i) to improve the coverage ratio of a surveillance region; (ii) to get an even distribution of signal energy in the surveillance region. Through introducing two objective functions for the above two mentioned goals, respectively, they formulate a multi-objective optimisation problem. To overcome drawbacks caused by the significant difference between the values of these two objective functions, they propose a multi-objective particle swarm optimisation (MOPSO) algorithm with non-dominated relative crowding distance (MOPSO-NRCD). Specifically, through the non-dominative relationship comparison and normalisation of crowding distance, a novel method is proposed to select the global best solution. Moreover, a multi-swarm structure is applied to labour division of all particles. Comparing with the MOPSO with the traditional crowding distance, simulation results show that the MOPSO-NRCD can provide better candidate schemes, which are capable to satisfy more stringent performance requirements.

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.

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.

In this paper, a scheduling method of generalized tasks for multifunctional radar network is proposed. Considering the actual relationship between task execution performance and the length of task dwell time as well as the high correlation of tasks periods, in this paper, the membership functions between execution performance and dwell time of tasks are considered to be nonlinear and the periods of tasks are arbitrary. Firstly, a radar task model with random period for multifunctional radar is built, and the exponential membership functions are set to ensure the generality of tasks. Then, the slots and common periods of radars in network are determined. Next, the periods of tasks are adjusted and tasks are assigned to corresponding radars respectively. Furthermore, tasks are scheduled in corresponding radars in network by greedy algorithm and heuristic algorithm. Finally, the numerical simulations are presented to evaluate the validity of the method.

In this study, a signal design algorithm for radar–communications system is investigated. An arbitrary pulse length orthogonal frequency-division multiplexing with sufficient cyclic prefix (CP-based OFDM) signal is designed as the radar–communications signal, the communication symbols are embedded by proposing an interval linear phase compression (ILPC) modulation. Firstly, the authors construct the model of CP-based OFDM signal, and analyse the impact of the communication symbols embedding. Then, to decrease the impact caused by the communication symbols embedding, the ILPC modulation is proposed. To guarantee the performances of the CP-based OFDM radar–communications signal, a time/frequency domain iterative filtering and clipping method is used. This algorithm can be used to design a CP-based OFDM signal that has a balanced performance in communications and radar. Finally, numerical results are provided to verify the validity of the proposed 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.

In this paper, considering a multi-stage electronic countermeasure against a Fire Control Radar (FCR) with multiple working modes, an optimal multi-stage jamming power allocation for smart noise jamming is investigated with unknown environment model. The optimal jamming power allocation problem is solved by proposing a multi-stage jamming power allocation method based on model-free reinforcement learning. Firstly, we construct a Markov decision process (MDP) with unknown environment model for multi-stage jamming power allocation. To evaluate the performance of multi-stage smart noise jamming, we choose the expected of the search to lock-on time of the FCR as an evaluation criterion. Then, to overcome the challenge of the unknown environment model, a reinforcement learning framework for multi-stage jamming power allocation is formulated. Under the framework, a method of multi-stage jamming power allocation based on Q-learning is proposed. Finally, numerical results are provided to verify the validity of the proposed method.

In this paper, considering mutual interference between radar and communication signals, the closed form performance bounds of radar-communication system are investigated. Firstly, we construct a localization estimation rate for radar application. Bounds on performance of radar-communication system are measured in terms of communication data rate for the communication and localization estimation rate for the radar. Then, we derive performance bounds of the three coexistence situations, which are divided according to the coexistence manners between radar communication band. To process the received radar-communication compound signal, we operate the successive interference cancellation (SIC) scheme. Finally, numerical results are provided to demonstrate the performances of the three coexistence situations.

This paper derives the performance of the joint radar and communication system (JRCS). Firstly, for the radar application of the JRCS, we derive the mutual information (MI) between target and received signal in additive Gaussian noise. For communication, we derive the communication data rate that the JRCS can achieve. Then, under partial coexistence between radar and communication bands, the MI and communication data rate for the JRCS are derived. In terms of the overlap between the received radar and communication signals, the successive interference cancellation (SIC) scheme is adopted. Finally, the provided numerical results demonstrate the performances for the JRCS.

In this paper, we propose a range reconstruction method based on the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal without adding any cyclic prefix (CP). Firstly, we establish the system model for range reconstruction based on the OQAM-OFDM signal. Then, the detailed procedures of range reconstruction are derived by applying the interference approximation method (IAM). In this way, inter-range-cell interference (IRCI) free range reconstruction and ideally zero range sidelobes are implemented. Besides, due to the non-requirement of CP, the spectrum utilization is improved compared with the range reconstruction exploiting the CP based OFDM signal. Simulation results demonstrate that the proposed method achieves the same ideally zero range sidelobes with that obtained by the approach using the CP based OFDM signal, and outperforms the conventional range reconstruction method that exploits the linear frequency modulated signal (LFM). Additionally, it shows good parameter estimation performance in different scenarios.

A moving target may enter a radar coverage area unannounced and leave after an unspecified period, which implies that the target's entry time and departure time are unknown. In the absence of these time information, target detection and parameter estimation (DAPE) will be severely impacted. In this paper, we consider the coherent detection and parameters estimation problem for a radar moving target with unknown entry time and departure time (that is, the time when the target appears-in/leaves the radar detection field is unknown), involving across range cell (ARC) and Doppler spread (DS) effects within the observation period. A new algorithm, known as window Radon Fractional Fourier transform (WRFRFT) is proposed to detect and estimate the target's time parameters (i.e., entry time and departure time) and motion parameters (i.e., range, velocity and acceleration). The observation values of a maneuvering target are first intercepted and extracted by the window function and searching along the motion trajectory. Then these values are fractional Fourier transformed and well accumulated in the WRFRFT domain, where the DAPE of target could be accomplished thereafter. Experiments with simulated and real radar data sets prove its effectiveness., 30 pages, 10 figures

In this paper, a pulse compression method is proposed with the offset quadrature amplitude modulation based orthogonal frequency division multiplexing (OQAM-OFDM) signal. By establishing the received OQAM-OFDM signal as a linear model form and without inserting any cyclic prefix (CP), we propose a concise and easily implemented pulse compression method. The proposed method realizes the inter-range-cell interference (IRCI) free pulse compression perfectly, which improves the power efficiency, and the scope of observation will not be restrained by the number of subcarriers. Besides, the signal-to-noise ratio (SNR) after the pulse compression is derived. It is proved that the SNR gain for the proposed pulse compression method can reach the same as that of the method using the matched filter. Finally, numerical simulation results are provided and discussed.

In this paper, we consider an optimal antenna deployment problem for distributed multistatic radar under the situation of multiple surveillance regions in a dynamic environment. The problem is solved by proposing a dynamic multi-objective particle swarm optimization (DMOPSO) with the assistance of the proposed Kalman filtering prediction strategy. Firstly, we construct a dynamic multi-objective optimization problem for antenna deployment by choosing the effective coverage rates of different non-fixed surveillance regions as objective functions. Then, to avoid the unnecessary computational load for solving optimization problem and improve the real-time property of antenna deployment, a DMOPSO algorithm based on the Kalman filtering prediction strategy is proposed by extracting the relationships among adjacent optimization time segments. Finally, numerical results are provided to verify the validity of the proposed DMOPSO algorithm.

In this paper, considering multiple surveillance regions in a dynamic environment, we investigate an optimal antenna deployment problem for distributed multistatic radar. The problem is solved by proposing a prediction based particle swarm optimization (PBPSO) algorithm. Unlike the traditional particle swarm optimization (PSO) method, which needs completely recomputation when the environment changes, the proposed method uses the information about the previous optimal deployment schemes to predict and optimize the current one. So it greatly increases the solving efficiency and computational load of the optimal problem. Firstly, by dividing a continuous time period into several scenarios, we use the previous optimal scheme and a Kalman prediction model to predict an approximate optimal scheme for the current scenario. Then, to obtain the real optimal scheme, we can use the predicted optimal scheme and PSO method to optimize. Numerical results are provided to verify the advantages of PBPSO in optimizing the antenna deployment scheme in a dynamic environment.

In this paper, considering timeliness constrain, a multifunction radar network resource allocation problem is investigated. The radar network resource allocation problem is solved by proposing an allocation method based on timeliness constrained combinatorial double auction (TCCDA). Firstly, we choose overall revenue maximization as allocation criterion considering timeliness of task value and task start time (TST) of each radar. Then, to obtain the optimal allocation scheme without task failure, a constant task completion ratio (CTCR) algorithm by adjusting the TST of radars is proposed. Finally, numerical results are provided to verify the validity of the proposed TCCDA.

In this paper, under the situation of multiple interference regions, an optimal antenna placement problem for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple interference regions, we solve the antenna placement problem by utilizing antenna placement method based on Multi-Objective Particle Swarm Optimization (MOPSO). However, it is not clear when to stop the iteration for which no knowledge about the optimum result is available. Hence, computational resource may be wasted over iterations. Nevertheless, time and computational resource is limited in real application. Therefore, to obtain the optimal placement result with limited time and computational resource, an iteration convergence criterion based on interval distance is proposed. The iteration convergence criterion can be used to stop the optimization process efficiently when the optimal antenna placement algorithm reaches the desired convergence level. Finally, numerical results are provided to verify the validity of the proposed algorithm.

This paper focuses on the Ambiguity Function design for the unimodular sequence, which is of great interest in radar and communication systems. An Accelerated Iterative Sequential Optimization (AISO) algorithm is proposed to minimize the average value of the weighted integrated side-lobe level (WISL) over specific Doppler bins and range bins of interest. At the analysis stage, we evaluate the effectiveness of the proposed algorithm in terms of the achieved WISL and computation time with respect to the gradient method using numerical simulations.

This paper considers the constrained waveform design of Multiple-Input Multiple-Output (MIMO) radar to synthesize a desired beampattern. Specifically, resorting to SemiDefinite Programming (SDP) related technique, we first minimize Integrate Sidelobe Level (ISL) to optimize the waveform covariance matrix forcing a uniform elemental power requirement as well as a 3dB bandwidth constraint. Then, based on Least Square (LS) approach, we devise the waveform accounting for constant modulus and similarity constraints by using cyclic algorithm (CA). Finally, we assess the effectiveness of the proposed technique through numerical simulations.

This paper considers the unimodular quadratic optimization problem for synthesizing radar codes forcing a similarity constraint to allow the designed code sharing the similarity feature with a given reference code. A computationally efficient Iterative Sequence Optimization (ISO) algorithm is proposed which sequentially improves the objective function. At each iteration, the multidimensional optimization problem is reduced to multiple one-dimensional trigonometric function optimization problems whose closed-form solutions are developed. Finally, we evaluate the performance of the proposed technique based on a radar code design example. Results highlight that ISO algorithm enjoys a significantly lower computational burden and holds higher gain at the same similarity level with respect to existing Semi-Definite Relaxation (SDR) technique [1].

This paper deals with the robust transmit-receive design for a moving point-like target detection embedded in signal-dependent clutter to against the uncertain Doppler shift of clutter. Specifically, we devise an Iterative Sequential Optimization (ISO) algorithm to sequentially improve the worst-case Signal to Interference plus Noise Ratio (SINR) over clutter statistics considering a constant modulus requirement on transmit signal. Each iteration involves multiple fractional programming problems which can be efficiently solved by Dinkelbach's procedure. At the analysis stage, we assess the performance of the proposed algorithm in terms of the achieved SINR and computational time. Results also highlight that the robust design shares the capability of resisting to the possible knowledge inaccuracies on signal-dependent clutter.

In this paper, we propose a range reconstruction method for a frequency-band shared multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) radar with sufficient cyclic prefix (CP) by using real orthogonal designs. Compared with the application of complex orthogonal designs in our previous work, the application of real orthogonal designs can significantly reduce the number of the all-zero-valued pulses in a coherent processing interval (CPI) for each transmitter and increase the efficiency of radar transmitters. Meanwhile, it still maintains the advantages of full spatial diversity without inter-range-cell interference (IRCI). We also apply the rate-$1$ real orthogonal designs for different numbers of transmitters and pulses for range reconstruction without any idleness of radar transmitters. Simulation results are presented to illustrate the performances of the OFDM pulse design and the CP-based MIMO OFDM radar using real orthogonal designs.

In this paper, we consider the near field source location problem for a nonuniform sparse linear array (non-USA) in the presence of sensor gain and phase errors. We present a minimum variance based iterative self-calibration method to simultaneously estimate the gain and phase error parameters as well as the calibration sources locations including ranges and azimuthes exploiting some imprecise a-priori knowledge of calibration sources. The source locations and the gain/phase errors are obtained iteratively with known range or azimuth, and continue the procedure until they are converged. Finally, at the analysis stage, we evaluate the effectiveness of the proposed technique using some numerical simulations.

This paper presents a radar tasks scheduling method for multistatic radar network. Rather than assuming the task periods to be highly correlated to each other, the task periods in this paper are considered to be arbitrary. A three-step strategy is employed to design the scheduling method. Firstly, we build a radar task model with variable period. Then, we slot each radar timeline with different sizes and refresh periods of tasks according to the minimum deviation. Finally, a heuristic method is adopted to schedule tasks to each radar. The numerical simulations are presented to evaluate the validity of the method.

In this paper, a preferential optimization problem of antenna placement for a distributed Multi-Input Multi-Output (MIMO) radar is studied. Considering multiple surveillance regions, the radar performances of preferential regions need to be improved and more choices of placement schemes for desired performance need to be provided. Therefore, we use the Particle Swarm Optimiztion (PSO) with reference point to solve the antenna placement problems. By setting reference points, the performance values of preferential surveillance regions will converge to reference points and reach our requirement. Then, we analyse the performance of proposed algorithm. Finally, numerical results are provided to verify the validity of the proposed algorithm.

This paper introduced the wide-band OFDLFM waveform to improve the supersonic target detection performance in the deception jamming environment. Given the waveform model, a mixed integration technique, which combines both coherent integration in the same channels and multi-channel fusion among different channels, is introduced to enhance integration gain and overcome different kinds of deception jamming. At last, the numerical experiments results prove its effectiveness.

This paper considers the sparse linear array (SLA) design problem of coherent unambiguous transmit for the distributed aperture coherence-synthetic radar (DACR), accounting for the antenna size constraint and geography constraint in which elements can not be placed in a given aperture. We develop the peak sidelobe level (PSL) based cost function, which considers the sum of the PSL when the mainlobe located at all scanning angles. Then, we derive the antenna element locations by minimizing the cost function through PSO algorithm. Finally, several numerical simulations verify the effectiveness of the proposed SLA design method.

This paper addresses the problem of adaptive multiple-input multiple-output (MIMO) radar detection in heterogeneous environment with compound-Gaussian clutter. The clutter covariances are assumed to be random and different from one transmit/receive pair to another with a priori knowledge about the environment. A two-step strategy is employed to design adaptive detector. Firstly, we obtain the generalized likelihood ratio test (GLRT) detector by assuming the known covariance matrices. Then, we derive the maximum a posteriori (MAP) estimator of the matrices by exploiting the Bayesian technique, and replace the given covariance matrices in the obtained GLRT detector with MAP estimates. Finally, we evaluate the proposed adaptive Bayesian detector via numerical simulations.