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Random frequency and pulse repetition interval (PRI) agile (RFPA) signals bring excellent performance of electronic counter-countermeasures to radar systems and have been received considerable attention in recent years. However, the research on their ambiguity function (AF) is not comprehensive. In this article, the analytical expressions of the AF expectation and variance are given. According to the expressions, the direct relationships between the key metrics of the AF and the waveform parameters of RFPA signals are specified. The results in this article are verified by Monte Carlo simulations and provide some insights into RFPA waveform design.

This article addresses the problem of joint direction of departure (DOD) and direction of arrival (DOA) estimation with nested bistatic multiple input multiple output (MIMO) radar using tensor decomposition. We first employ the two-level nested transmit and receive arrays to develop the sum-difference coarray for constructing the Toeplitz and spatial smoothing matrices. We then generalize the three-way tensor model from DOD and DOA dimensions, and derive the optimized tensor by maximizing the number of detectable targets, where the existing COMFAC technique is exploited for angle estimation. We show that the proposed method can identify more targets and achieve better performance by enforcing the three-way structure information compared with the subspace-based algorithms. We also show that the conventional tensor model is just a special case. Finally, we derive the coarray Cramer–Rao Bound (CRB) for the nested MIMO radar, and also conduct a study for the conditions under which the CRB exists. Numerical simulations are provided to validate the theoretical analysis and demonstrate the performance improvement.

Radar mounted on a moving vehicle returns time-varying detections corresponding to unstable scattering points, unlike optical sensors, which produce relatively stable detections. We present an efficient extended Kalman filter-based simultaneous localization and mapping (EKF-SLAM) algorithm for radar, utilizing new techniques of clustering and sifting the time-varying detections. Velocity bias and yaw rate bias, which are inherent in any odometer are also estimated and compensated using the same EKF. For theoretical performance evaluation, the posterior Cramer–Rao bound (PCRB) for SLAM with odometer bias estimation is derived and compared with the root-mean-squared errors (RMSEs), with and without bias estimation. The simulation results show that the RMSE for SLAM with bias estimation is the closest to the PCRB. The proposed algorithm is also verified experimentally with field data. The comparison with the ground truth trajectory obtained from a differential global positioning system shows that the proposed algorithm yields an accurate trajectory estimate, even under large odometer bias. Also, the real-time capability of the proposed algorithm is verified.

The growing demand for radio frequency (RF) spectrum access poses new challenges for next-generation radar systems. To operate in a crowded electromagnetic environment, radars must coexist with other RF emitters while maintaining system performance. This work evaluates the performance of a spectrum sharing cognitive radar system, which predicts and avoids RF interference (RFI) in real time. The system applies a cognitive perception-action cycle that senses RFI, learns RFI behavior over time, and adapts the radar's frequency band of operation. Through cognition, the system learns a stochastic model describing RF activity. This model allows the cognitive radar to predict RF activity in real time and share the spectrum with emitters, such as communication systems. A set of synthetic and measured interference signals are used to evaluate the performance of this predictive spectrum sharing scheme. This work assesses the impact of RFI on the cognitive radar's range profile with respect to variation in RF environment. The system demonstrates accurate avoidance of deterministic RFI with a degradation in spectrum sharing efficiency as variability over time increases.

Multiple-input multiple-output (MIMO) radar systems are popular for their ability to mitigate the target radar cross section fluctuations and null Doppler. However, MIMO radar fundamentally suffers from a notable signal-to-noise ratio (SNR) loss compared to an ideal phased-array (PA) radar. In order to overcome this drawback, a distributed MIMO radar scheme with joint optimal transmit and receive signal combining method is proposed in this work. In view of the optimality, the proposed scheme outperforms the most popular combining methods of MIMO radar addressed in the literature. Also, the proposed scheme performs same as the ideal PA radar in case of the nonfluctuating target. In case of fluctuating targets, the proposed scheme outperforms PA radar at high SNR.

How to utilize the limited antennas for tracking multiple targets plays a critical role in the collocated multiple-input multiple-output radar. A dynamic antenna selection strategy is proposed to address this problem. The basis of our strategy is to achieve the optimal antenna selection under the constraint of limited active antennas using the feedback information in the tracking recursion cycle, improving the worst case of estimate accuracy among multiple targets. Since the posterior Cramer–Rao lower bound quantifies the target tracking performance, it is derived and utilized as the optimization criterion. We then propose an efficient algorithm which integrates the convex relaxation technique with the local search to solve the problem. Simulation results show its superior performance compared with the random antenna selection strategy and the heuristic search algorithm. Moreover, the proposed strategy can provide the performance close to the exhaustive search method while maintaining reasonable runtime.

To perform 3-D inverse synthetic aperture radar (ISAR) imaging through interferometric processing, all ISAR images should be correctly registered such that the same scatterer appears at the same position along both the range and Doppler directions. However, this condition generally does not hold in most situations because different radar view angles introduce additional angular motion errors that must be compensated. Hence, we herein propose a new ISAR registration method based on joint-channel phase difference (JC-PD) operations. From the result of the JC-PD function, JC-PD profiles are obtained, in which the locations and phases of the maximum peak contain constant and time-varying angular motions, respectively. Using our proposed method, correct ISAR registrations can be achieved in both the range and Doppler directions. In particular, compared to conventional Doppler registration methods, time-varying angular motions are more accurately compensated without the problem of cross-term phase interference. Through simulations and experiments, we verified that the proposed method yielded accurate ISAR registration results; hence, an excellent 3-D target reconstruction through interferometric ISAR imaging was demonstrated.

This article considers target separation detection (TSD) in the presence of homogeneous Gaussian interference. The problem is formulated as a hypothesis test for two typical situations: 1) the use of a low range-resolution radar or fast-track update rate; 2) the use of a high-resolution radar or low-track update rate. At the design stage, TSD tests are devised according to the generalized likelihood ratio test criterion. The computation of each decision statistic requires the solution of a semidefinite programming problem obtained leveraging the relationship among linear matrix inequalities and nonnegative trigonometric polynomials. All the obtained decision rules ensure the bounded constant false alarm rate property. At the analysis stage, the performance of some benchmark detectors is given in terms of detection and false alarm probabilities. Finally, numerical examples are provided to show the performance of the proposed tests as compared with the benchmarks as well as the gain achievable over some counterparts devised to monitor a separation event.

This work addresses code division multiple access (CDMA) implementation of automotive multiple-input multiple-output (MIMO) radars. The tansec frequency modulation (TSFM) is proposed as a single intracoding scheme for CDMA-MIMO radars. The properties of the TSFM coding family are analyzed and two novel codeword selection algorithms are proposed. The TSFM is compared to a variety of coding families by evaluating their autocorrelation and cross-correlation properties. The considered coding families are based on phase coding: Hadamard, Gold, optimal peak to sidelobe; and frequency coding: piecewise linear frequency modulated. Properties of the coding families are analyzed with a variety of novel criteria, which are especially useful for CDMA-MIMO radars. The performance of the CDMA-MIMO radar with different coding families is evaluated via simulations of practical multitarget automotive scenarios. It is shown that the frequency-based TSFM coding family outperforms the other considered coding families in terms of their cross-correlation properties.

With increased degrees of freedom of the transmitter, a coherent waveform-agile radar system can change its interpulse waveform to enhance the capability against increasingly sophisticated jamming and spoofing threats. Furthermore, radar agile waveform can provide better detection performance than a single waveform. In this article, we consider discrete phase coded sequence set design (DPCSSD) problem based on range-Doppler discrete ambiguity function for coherent waveform-agile radar system. DPCSSD problem for optimizing discrete phase coded sequence set under constant module constraint with desired minimized sidelobes on range-Doppler plane, is a quartic function and the constraint is nonconvex. To solve this quartic optimization problem, we propose an effective algorithm based on alternating direction method of multipliers (ADMM), which is a powerful mathematical tool of the augmented Lagrangian scheme for dealing with separable objective functions. As the quartic optimization problem is hard to solve, majorization–minimization (MM) is also considered to introduce a quadratic auxiliary function which is the upperbound of the original function. By minimizing the auxiliary function, the original DPCSSD optimization problem can be solved through MM in an iterative way. Meanwhile, the problem of minimizing the quadratic auxiliary function with constraints can be solved through ADMM algorithm. In particular, fast implementation for the most computationally demanding step of matrix inversion is investigated. The effectiveness of the proposed approach is demonstrated via computer simulations.

Analyzing radar signals is an important task in operating electronic support measure systems. The received signals in the real electromagnetic environment often originate from multiple emitters and must be separated for further processing. Pulses from important target emitters with known parameters should be picked out first. To solve the problem, time-of-arrival (TOA) deinterleaving may be performed to extract signals from a certain emitter by learning the pulse repetition interval (PRI) modulation that makes up the signal. However, conventional deinterleaving methods only work with simple PRI modulations; their performance degrades in noisy environments. A novel approach based on denoising autoencoders for TOA deinterleaving was developed in this article. The inner patterns of pulse-of-interest sequences were learned by the proposed denoising autoencoders to generate output sequences from well-trained autoencoders. Simulation results show that the proposed method outperforms conventional methods, especially in environments with high lost and spurious pulse ratios.

Accurate orbit prediction (OP) of space debris is vital in space situation awareness (SSA) related tasks, such as space collision warnings. However, owing to the sparse and low precision observations, unknown geometrical and physical features of debris, and effects of incomplete force models, OP based on the orbital mechanics theory or physics-based OP of space debris suffers from rapid error growth over a long duration, limiting the period of validity of debris OP for precise space applications. Considering that the tracking arcs of a debris object over a single station often share a similar temporal and spatial distribution in the inertial space, the resultant OP errors possibly have a coherent relationship with the temporal and spatial distribution of tracking arcs. This article proposes a machine learning (ML)-based approach to model the underlying pattern of debris OP errors from historical observations and apply it to modify the future physics-based OP results. The approach includes three steps: constructing a historical OP error set, training an ML model to fit the historical OP error set, and correcting the future physics-based OP with ML-predicted orbital errors. The ensemble learning algorithm of boosting tree is studied as the primary ML method for the error modeling and predicting process. Experiments with three low-Earth-orbit objects, tracked by a single radar station, demonstrate that the trained ML models can capture more than 80% of the underlying pattern of the historical OP errors. More importantly, the errors of physics-based OP over the future seven days reduce from thousands of meters to hundreds or even tens of meters through the error correction with the learned error pattern, achieving at least 50% accuracy improvement. Such dramatic OP improvements show the promising potential of ML for enhanced SSA capability.

The adaptive matched filter (AMF) uses a number of training samples observed by the radar to estimate the unknown disturbance covariance matrix of a cell under test. In general, as the number of homogeneous training samples increases, the detection performance of the AMF improves up to a theoretical limit (defined by the performance of a matched filter detector where the disturbance covariance is known). However, radar data are nonhomogeneous in practice. Consequently, a high number of training samples is typically undesirable, since nonhomogeneous training data cause detection performance to suffer. Thus, a decision maker (DM) must consider these tradeoffs when selecting this number of training samples, along with other decision parameters for the AMF. Using the concept of information elasticity, this tradeoff behavior is characterized for decisions that are relevant to a DM. A simple user defined constraint function is proposed, characterizing the relative cost of selecting different decisions. Using a multi-objective optimization (MOO) technique known as compromise programming, information overload is observed, in that increasing the cost of decisions improves performance up to a point, beyond which increasing the cost no longer provides meaningful benefit. Using this framework, a cost-efficient solution is selected.

An adaptive ranging technique for maintaining high-accuracy ranging between nodes in coherent distributed antenna arrays is presented. Coherent distributed antenna arrays are networks of wireless systems coordinated coherently at the level of the wavelength of the wireless signal. Enabling coherent operation between separate mobile nodes for active and passive microwave remote sensing requires accurate knowledge of the relative positions of the nodes in the array. In this article, a novel adaptive ranging technique based on the near-optimal waveform for high-accuracy ranging, a two-tone waveform, is designed and demonstrated in software-defined radio platforms representing array nodes. Ranging accuracy is dependent on both signal-to-noise ratio and the separation of the two tones in the waveform; however, in realistic environments, factors such as attenuation or antenna misalignment are not easily predicted, which can lead to degradation of the ranging measurement. Selecting one appropriate waveform for range measurements is, thus, not feasible unless the bandwidth assigned to it is always higher than required for the needed range accuracy. Rather than allocating such an unnecessarily wide bandwidth, this article presents a controller that regulates the spectral resources adaptively to meet the desired reference accuracy while minimizing the total occupied bandwidth. The controller continuously monitors the statistical parameters of the received signal, such as signal-to-noise ratio, in a perception stage and adapts the spectral characteristics of the transmitted waveform in an action stage. The adaptive action, based on a Cramer–Rao lower bound analysis, maintains the signal statistical characteristics below a specified bound to maintain high coherent gain. Experimental results demonstrate the ability to maintain ranging standard deviation of 1.5 mm (standard deviation of time delay estimates equals $10^{-11}$ s), which yields 90% or more of the possible achievable coherent gain at carrier frequencies up to 13.33 GHz.

We consider the design of sets of discrete-phase, including binary/quaternary, sequences for phase-modulated continuous-wave multiple-input multiple-output radar in the presence of nonnegligible Doppler shift. We consider the peak sidelobes of auto- and cross-correlations in a desired interval of the Doppler shift as the performance metric of the design. Then, the design problem is formulated via imposing the discrete-phase constraint for each element of the sequences, with emphasis on binary as well as quaternary cases. This problem is nonconvex and hence hard to solve. We introduce a method based on the block coordinate descent framework to solve the optimization problem. We also modify the proposed method to efficiently update the subproblem associated with each iteration. Also, we extend it to account for only a low-correlation zone in the presence of Doppler shift. Numerical examples are provided to illustrate the effectiveness and performance of the proposed algorithm in comparison with the existing methods.

In this article, 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 (which in turn is a function of the transmit waveform) against an idealized one while enforcing the waveform elements to be constant modulus and in the presence of spectral restrictions. This leads to a hard nonconvex optimization problem primarily due to the presence of the constant modulus constraint (CMC). In this article, we exploit the geometrical structure of CMC, i.e., we redefine this constraint as an intersection of two sets (one convex and other nonconvex). This new perspective allows us to solve the nonconvex design problem via a tractable method called iterative beampattern with spectral design (IBS). In particular, the proposed IBS algorithm develops and solves a sequence of convex problems such that constant modulus is achieved at convergence. Crucially, we show that at convergence the obtained solution satisfies the Karush–Kuhn–Tucker conditions of the aforementioned nonconvex problem. Finally, we evaluate the proposed algorithm over challenging simulated scenarios, and show that it outperforms the state-of-the-art competing methods.

We consider a multiple-input multiple-output (MIMO) radar that works through one-bit sampling of received radar echoes. The application of one-bit sampling significantly reduces the hardware cost, energy consumption, and systematic complexity, but it also poses serious challenges to extracting highly accurate target information from one-bit quantized data. In this article, we propose a maximum likelihood (ML)-based method that first iteratively maximizes the likelihood function to recover a virtual array data matrix and then jointly estimates the angle and Doppler parameters from the recovered matrix. Because the ML problem is convex, we can successfully apply a computationally efficient gradient descent algorithm to solve it. Based on our analysis of the Cram $\acute{\text{e}}$ r–Rao bound of the ML-based method, a pre-estimation-assisted threshold (PET) strategy is developed to improve the estimation performance. Numerical experiments demonstrate that the proposed ML-based method, combined with the PET strategy, can provide highly accurate parameter estimation performance, close to that of the classic MIMO radar.

Sea-surface small target detection is always a difficult problem in high-resolution maritime ubiquitous radars for complex characteristics of sea clutter, weak target returns, and diversity of targets. Multiple features extracted from radar returns in different domains have ability but not enough to solely distinguish radar returns with target from sea clutter. Joint exploitation of multiple features becomes the key to improve detection performance. In this article, the K-nearest neighbor (KNN) algorithm and anomaly detection idea are cooperated to develop a novel sea-surface target detection method in the feature space spanned by the eight existing salient features. The detection is realized by the anomaly detection followed by a specially designed KNN-based classifier with a controllable false alarm rate. In the anomaly detection, a decision region is determined by the hyper-spherical coverage of the training set of sea clutter that is sufficient and ergodic in the feature space. The KNN-based classifier is designed based on the training sample set of sea clutter and the training sample set of simulated target returns plus sea clutter that is sufficient but nonergodic, by joint usage of feature weighting, neighbor weighting, and distance weighting. The novel method is validated by the two open and recognized IPIX and CSIR radar databases for sea-surface small target detection. The results show that it provides significant performance improvement in comparison with the existing multiple-feature-based detection methods, owing to the fact that the novel method avoids the dimension restriction and feature compression loss in the existing methods.

Recognition of multifunction radar (MFR) is an open problem in the field of electronic intelligence. Parameters of MFR pulses are generally agile and difficult to distinguish statistically. A prospective way to realize credible MFR recognition is mining and exploiting more distinguishable high-dimensional patterns buried in pulse groups, which may be designed for implementing infrequently used radar modes such as target tracking. A high-dimensional pattern is defined according to the agile range and switching law of sequential pulse repetitive intervals within a pulse group. This article establishes deep recurrent neural networks (RNN) to discriminate and coarsely cluster different pulse groups hierarchically with respect to their sequential structures. Afterwards, RNN-based classifiers are trained to extract and exploit features within different pulse group clusters. Distinct degrees of confidence are then attached to these classifiers to indicate the discriminabilities of the corresponding pulse group clusters. The pulse group clustering and classifying models are finally cascaded to form an integrated classification model, which mines distinguishable patterns from sequentially arriving pulse groups of the same radar and accumulate them to realize MFR recognition. Simulation results demonstrate the much improved performance of the proposed method over existing counterparts in different scenarios.

The effect of jammer on radar or jamming performance has been and is being assessed on the basis of range reduction where consistency in tracking target ability is more important than range reduction in a tracking radar. A new criterion known as relative radar functionality destruction time is defined and introduced as the relative of functionality destruction time of radar to one period of jammer, where jammer signal and target echo power are of concern. The effective parameters in relative time of the receiver functioning destruction are assessed. Next, this criterion is applied in the assessment of simple conical scan radar receiver against a conventional jamming (sweep noise jamming). This criterion is modeled and simulated on a search radar in the jamming environment where the minimum required standard deviation of noise for destroying the radar function yields. By implementing the structure of a frequency modulated continuous wave tracking radar structure, a simple target based on digital radio frequency memory method and one type of jamming against this radar in a simultaneous manner, the functionality destruction is extracted for different radar parameters. This new criterion on outperforms its counterparts.

Recent developments in electronics have pushed miniaturized satellites to the femto-scale, with masses between 10 and 100 g. Although femtosatellites have been proven as a feasible concept, most designs are limited in mission capacity and lifetime due to the lack of environmental protection and onboard propellant. In this article, a novel concept for femtosatellites for earth remote sensing is proposed. In particular, a swarm of femtosatellites are used as elements of a sparse array in orbit to receive radar echoes. They also feature active orbit control enabled by solar radiation pressure to extend their lifetime. A simple active orbit control algorithm has been demonstrated. A mission concept based on a sun-synchronous circular orbit is proposed to maximize the benefit for both earth remote sensing and active orbit control. A synthetic aperture radar mission has been used to characterize their performance.

Shipborne or airborne maritime surveillance radars at scan mode work at a complex scene consisting of land, sea, and islands. Sea–land segmentation provides a two-class classification of sea clutter versus ground clutter to the surveillance scene as a precondition of adaptive target detection. It is a difficult problem because only a few coherent pulses are available at scan mode. Moreover, due to moving radar platforms and wide dynamic range of ground and sea clutter in power, average amplitude, Doppler offset, and initial phase of radar, the returns vector fails to distinguish sea clutter and ground clutter. In this article, a similarity measure of two radar returns vectors, which is invariant to amplitude, Doppler offset, and initial phase, is constructed, which is closely relevant to the Doppler bandwidth of a returns vector. Based on the similarity measure, a K-nearest neighbor classifier is proposed to yield a pixel-level sea–land segmentation of the scene. Further, the morphological filtering is operated on the pixel-level segmentation to obtain a region-level segmentation. Moreover, the discrete Frechet distances of the main boundaries in successive scan periods are used to assess segmentation quality. The proposed method is verified by measured data from an airborne radar and an island-based radar. The results show that it behaves better than the methods using thresholding phase linearity degree of radar returns and the support vector machine, and back propagation neural network in a three-dimensional feature space.

In this article, 1 1 Portions of this work were presented at the 2018 IEEE Radar Conference [1] . we examine a cognitive radar that must coexist with communication systems. Specifically, we model a cognitive radar's adaptation to the communication system as a Markov decision process (MDP) and then apply reinforcement learning to solve the resulting optimization problem. More specifically, we learn the environment model and then apply policy iteration to determine the optimal policy. The radar environment consists of a single moving target and a communication system that uses the same bands as the radar. The communication system is modeled using several different transmission behaviors: constant, intermittent, triangular frequency sweep, sawtooth frequency sweep, frequency hop, pseudorandom frequency hop, and direction-dependent interference. We demonstrate how the MDP framework and reinforcement learning can be used to help the radar determine actions (i.e., transmission strategies) to maximize its own performance.

This article focuses on the employment of an inertial measurement unit (IMU) and a radar altimeter and velocimeter as navigation sensors for Mars landing navigation. At the beginning of the parachute descent phase, high dynamic oscillatory motion may saturate the gyroscope and produce large attitude estimation errors, thereby producing a high landing risk. To address this problem, an online navigation initialization algorithm is presented by combining data from the IMU and the radar. By defining a new inertial frame, attitude, and Mars-related velocity are conveniently initialized. A key contribution of this article is the computation of gravitational acceleration using IMU and velocimeter's measurements for the approximation of the nadir vector. Given the nadir vector, the inertial position is initialized in conjunction with the altimeter's measurement, the gravitational acceleration is modeled such that the inertial navigation equation can be propagated and the propagated altitude and velocity can be corrected by radar radar-derived measurements.

We present an ultrashort range IEEE 802.11ad-based automotive joint radar-communications (JRC) framework, wherein we improve the radar's Doppler resilience by incorporating Prouhet–Thue–Morse sequences in the preamble. The proposed processing reveals detailed microfeatures of common automotive objects verified through extended scattering center models of animated pedestrian, bicycle, and car targets. Numerical experiments demonstrate 2.5% reduction in the probability of false alarm at low signal-to-noise-ratios and improvement in the peak-to-sidelobe level dynamic range up to Doppler velocities of $\pm 144$ km/h over conventional 802.11ad JRC.

In modern antenna systems, beam broadening of subarrayed arrays provides continuous coverage of a wide angular extent in a cost-effective manner. While many methods have been published that address beam broadening of traditional (non-subarrayed) arrays, there is a knowledge gap in the published literature with respect to efficient beam broadening of contiguous uniform subarrayed arrays. This article presents efficient methods for beam broadening of contiguous uniform subarrayed arrays where the excitation of each element is not individually controlled, but the elements of the array are grouped together as subarrays to have the same element excitations. Particularly, this article focuses on phase-only optimization to preserve maximum power output. Three modified iterative Fourier transform (IFT) methods and one genetic algorithm (GA) are presented to efficiently search the vast solution space of possible phase settings for a solution that satisfies the desired broadened pattern. These methods are evaluated on idealized $1 \times 40$ and $1 \times 80$ linear arrays with five element subarrays and $40 \times 40$ and $80 \times 80$ element rectangular arrays with $5 \times 5$ element subarrays. The proposed modified IFT methods are found to be faster than the GA approach, while the GA approach only offers a few percentage points of better effectiveness.

The linear velocity of an arbitrary moving object comprises two orthogonal components with reference to the observing radar, which are radial velocity and transversal velocity. Simultaneous measurement of both the radial and transversal velocities can provide a complete 2-D movement information for further target detection, tracking, and classification. The interferometric radar is capable of measuring the 2-D velocity of an arbitrary moving object simultaneously, regardless of the trajectory. However, the radial velocities and transversal velocities are coupled in the interferometric measurement due to the nonlinear processing when there are multiple moving objects in the radar field of view (FoV), thus the conventional time–frequency analysis fails to measure the transversal velocities. In this article, we first derive and analyze the signal distortion caused by the nonlinear interferometric operation. We then propose two new methods of combining the inverse Radon transform (IRT) and short-time Fourier transform (STFT), which can concentrate a sinusoidally frequency-modulated waveform from time–frequency domain into a point in the parameter domain, thus separating the transversal velocity measurements of different objects with rigid-body rotations. Simulation and experimental results are provided to validate the effectiveness of the proposed methods.

Lots of detectors for high resolution range profile (HRRP) data are mainly based on the intensities of target echoes. When the intensities of outliers are as large as those of targets of interest, some false alarms will emerge during the detection stage. In this article, we propose a rejection algorithm for HRRP data between the detection stage and recognition stage to eliminate the false alarms occurring during the detection stage. In this method, the intensities and positions of the dominant scatterers are extracted as a joint feature. Then, the feature is used to develop the K-center one-class classifier based on Hausdorff distance. Experimental results on the measured HRRP data indicate that the proposed rejection algorithm can eliminate the false alarms remarkably well, and keep most of the target data as well. Meanwhile, the proposed method has better performance under different values of signal-to-noise ratio and different values of parameters than some other rejection methods.

Waveform diversity, which can be used to obtain synthetic bandwidth or mitigate range ambiguity, interference, and folded clutters, has attracted a great deal of attention in recent years. However, the range sidelobe modulation effect between agile pulses yields high range-Doppler sidelobes in pulse-Doppler (PD) radar applications, because of which traditional window functions or mismatched filters (MMFs) do not apply. In this article, a Doppler-tuned matched filter is used to equivalently obtain the PD result. Then, to mitigate the high range-Doppler sidelobes, a two-dimensional MMF (2-D MMF) is proposed, and the relevant signal-to-noise ratio (SNR) loss is deduced. A cyclic algorithm is also designed to calculate the 2-D MMF for suppressing sidelobes over desired range-Doppler regions with constrained SNR loss and mainlobe width. Several simulations and an experiment based on real measured data of an airborne platform are conducted to illustrate the effectiveness of the proposed 2-D MMF.

Dynamic spectrum allocation will require cognitive radar transmitters to change operating frequency and bandwidth in real time. This will require high-power reconfigurable circuitry to improve radar performance by simultaneously increasing 1) the output power of the transmit waveform and 2) the power-added efficiency of the power amplifier. This circuitry is also used to mitigate cochannel interference by maintaining sufficiently linear performance so that the output waveform conforms to a given spectral mask. In this approach, a 90-W evanescent-mode cavity tuner is reconfigured using a specially designed gradient search to find the best combination of resonant cavity position numbers. This approach will be much more flexible for field use than typical Smith-chart-based load-pull searches, which require a characterization that is susceptible to drift. Experimental results are presented showing that the efficiency, output power, spectral performance, and estimated maximum radar detection range are improved significantly by retuning the matching network at each operating frequency. Additionally, this article discusses innovations to reduce or eliminate time bottlenecks in a cognitive radar system for impedance tuning, reducing the time needed for complete impedance tuning searches from minutes to seconds. This significant timing reduction makes tunable power amplifiers a feasible option for future use in spectrum sharing by cognitive radar systems.

Discriminant analysis is a technique used in statistics and machine learning to separate two or more classes of objects or events. We introduce linear, quadratic, and mixture discriminant analysis methods into radar signal classification. However, the selection of an appropriate discriminant analysis method can be difficult and no comparison study of these discriminant analyses for radar signal classification can be found in the open literature. This article presents a theoretical analysis and practical comparison of these three discriminant analysis methods for radar signal classification. The theoretical analysis is derived from Bayes’ theorem. The practical comparison study is performed on a dataset consisting of five radars emissions. The advantages and drawbacks of each discriminant analysis method are highlighted. This study demonstrates that quadratic discriminant analysis (QDA) is predominantly a better method for radar signal classification using our radar dataset. On average, it demonstrates 95%, 93%, and 86.6% classification accuracy for three, four, and five radar emitters in our radar dataset, respectively. Linear discriminant analysis (LDA) achieves on average 88.7%, 84.9%, and 79.2% classification accuracy for three, four, and five radar emitters, respectively. Mixture discriminant analysis (MDA) also achieves the same classification performance as QDA. Theoretical analysis shows that both LDA and QDA are a special case of MDA and that MDA can set up more decision boundaries than LDA and QDA if the feature distribution in the dataset is an ensemble of Gaussian distributions. Therefore, MDA is recommended for radar signal classification.

The range resolution of radar is inversely proportional to the swept bandwidth. However, this bandwidth may not be available due to a variety of reasons, including spectrum congestion and lack of instantaneous bandwidth despite access to a larger tunable bandwidth. Existing approaches to achieving high-range resolution include bandwidth extrapolation, ultrawideband coherent processing, and spectral splicing of narrowband stepped-frequency pulses. In this article, we achieve high-range resolution of a wideband chirp from a sequence of narrowband pulses using coherent-phase extension of the beat tones of an FMCW dechirper. By phase matching the beat tones across pulse boundaries, the algorithm trades a longer observation interval for a smaller swept bandwidth. Phase matching of the short beat tones is applied in two steps to first obtain the coarse estimate of the beat frequencies which are then used as the starting point in the formation of the range-Doppler map that now enjoys the two-dimensional fast-Fourier transform (2-D-FFT) gain. With this approach, a fraction of the total bandwidth to obtain a range resolution of a wideband chirp is used. Using both simulations and experimental data, the proposed high-resolution algorithm is applied to a narrowband chirp sequence with bandwidth $B$ to generate range-Doppler maps with range resolution corresponding to a wideband chirp sequence of bandwidth $NB$ .

Compared with conventional two-dimensional space-time adaptive processing (2D-STAP) methods, the elevation-azimuth-Doppler three-dimensional space-time adaptive processing (3D-STAP) method has the advantage of suppressing nonstationary clutter. Thereby, it is suitable for nonsidelooking airborne radar (non-SLAR) applications. However, its huge training data requirements and computational load are often beyond radar's ability in practical clutter environments. In this correspondence, we develop a simple but efficient reduced-degree-of-freedom (DOF) 3D-STAP method that significantly reduces the required training data and the computational complexity while maintaining the suboptimal clutter suppression performance. The proposed method transforms the planar array data into linear array data in azimuth and in elevation, respectively, thereby beamforming an equivalent cross-shape array prior to STAP. In consequence, only a few spatial DOFs, including azimuth and elevation dimension, are used for STAP directly resulting in the potential advantage for nonstationary clutter suppression and drastically reducing training data requirements and computational load. Furthermore, the clutter rank estimation rules of the planar array and the transformed cross-shape array are derived, and the required elevation DOFs of the proposed method are further discussed in detail. Simulations for clutter suppression of non-SLAR show that the proposed STAP method outperforms the state-of-the-art 3D-STAP method in terms of convergence and computational complexity.

Deep neural networks have recently received a great deal of attention in applications requiring classification of radar returns, including radar-based human activity recognition for security, smart homes, assisted living, and biomedicine. However, acquiring a sufficiently large training dataset remains a daunting task due to the high human costs and resources required for radar data collection. In this article, an extended approach to adversarial learning is proposed for generation of synthetic radar micro-Doppler signatures that are well adapted to different environments. The synthetic data are evaluated using visual interpretation, analysis of kinematic consistency, data diversity, dimensions of the latent space, and saliency maps. A principle-component analysis-based kinematic-sifting algorithm is introduced to ensure that synthetic signatures are consistent with physically possible human motions. The synthetic dataset is used to train a 19-layer deep convolutional neural network to classify micro-Doppler signatures acquired from an environment different from that of the dataset supplied to the adversarial network. An overall accuracy of 93% is achieved on a dataset that contains multiple aspect angles (0$^{\circ }$, 30$^{\circ }$, and 45$^{\circ }$ as well as 60$^{\circ }$), with 9% improvement as a result of kinematic sifting.

As radar signals propagate above the ocean surface to determine the trajectory of a target, the signals that are reflected directly from the target arrive at the receiver along with indirect signals reflected from the ocean surface. These unwanted signals must be properly filtered; otherwise, their interference may mislead the signal receiver and significantly degrade the tracking performance of the radar. To this end, we propose a low-elevation target tracking mechanism considering the specular and diffuse reflection effects of multipath propagation over the ocean surface simultaneously. The proposed mechanism consists of a state-space model and a particle filtering algorithm and promises considerable improvements in the capacity and accuracy of the radar tracking systems. The efficiency and accuracy of the developed target tracking method are tested and compared with an unscented Kalman filtering method in two- and three-dimensional space using a series of simulation experiments.

The recognition of the interference environment for adaptive radar detection is addressed in this article. Typically, detectors are designed in one specific scenario which may not be appropriate for the varying interference environment, especially for the airborne and space-based radar system. In this article, the considered recognition task is cast in terms of multiple hypothesis tests and the theory of model order selection (MOS) techniques are exploited to devise suitable decision rules. The interference environments are divided into homogeneity, partial homogeneity, and spherically invariant random process. Three MOS techniques, namely, the Akaike information criterion (AIC), generalized information criterion, and corrected AIC, are adopted. At the analysis stage, illustrating examples for the influence of the environment model parameters on the recognition accuracy of the MOS rules are presented. Numerical experiments show the AIC rule has the most robust recognition performance.

Over-the-horizon radars (OTHRs) often operate in the presence of interference that is nonstationary over the coherent processing interval (CPI). In this case, effective adaptive beamforming requires intra-CPI updates to the spatial weight vector to track changes in the interference spatial structure. However, these weight vector changes must be constrained to prevent modulating the clutter signal and drastically reducing subclutter visibility. These additional constraints require a model of the clutter signal. Existing adaptive beamforming algorithms either explicitly or implicitly rely on a scalar multivariate autoregressive (MVAR) model, which is based on typical observations made by mid-latitude OTHRs. However, high-latitude OTHRs often observe angle-Doppler coupled clutter, for which the scalar MVAR model does not apply. This article investigates the ramifications of angle-Doppler coupling for spatial adaptive processing (SAP), which results in the proposal of a new SAP algorithm. The efficacy of the new algorithm is compared to existing methods through simulation.

For an unmanned aerial vehicle (UAV) carrying out a maritime radar surveillance mission, there is a tradeoff between maximizing information obtained from the search area and minimizing fuel consumption. This article presents an approach for the optimization of a UAV's trajectory for maritime radar wide area persistent surveillance to simultaneously minimize fuel consumption, maximize mean probability of detection, and minimize mean revisit time. Quintic polynomials are used to generate UAV trajectories due to their ability to provide complete and complex solutions while requiring few inputs. Furthermore, the UAV dynamics and surveillance mission requirements are used to ensure that a trajectory is realistic and mission compatible. A wide area search radar model is used within this article in conjunction with a discretized grid in order to determine the search area's mean probability of detection and mean revisit time. The trajectory generation method is then used in conjunction with a multiobjective particle swarm optimization algorithm to obtain a global optimum in terms of path, airspeed (and thus time), and altitude. The performance of the approach is then tested over two common maritime surveillance scenarios and compared to an industry recommended baseline.

This article considers the general problem of tracking a noncooperative target in the presence of specular multipath. We revisit a novel posterior Cramer–Rao bound (PCRB) methodology for this problem [1] and develop a robust interacting multiple model (IMM) extended Kalman filter (EKF) implementation. Both the PCRB and the IMM-EKF augment the target state to include the multipath parameters. The approaches are demonstrated via a simulated scenario, in which an airborne radar tracks a low-altitude airborne target. It is shown that the PCRB increases rapidly subsequent to the multipath effect occurring, indicating that tracking performance is significantly degraded at such times. Furthermore, it is shown that the IMM-EKF offers near-optimal performance, with target state estimation errors consistently within 15% of the PCRB. The IMM-EKF also consistently provides credible estimates. It is concluded that the PCRB methodology and the IMM-EKF tracker provide powerful tools for performance assessment and target state estimation in challenging multipath scenarios.

We consider multitarget detection and tracking problem for a class of multipath detection system where one target may generate multiple measurements via multiple propagation paths, and the association relationship among targets, measurements and propagation paths is unknown. In order to effectively utilize multipath measurements from one target to improve detection and tracking performance, a tracker has to handle high-dimensional estimation of latent variables including target active/dormant meta-state, target kinematic state, and multipath data association. Based on variational Bayesian inference, we propose a novel joint detection and tracking algorithm that incorporates multipath data association, target detection and target state estimation in a unified Bayesian framework. The posterior probabilities of these latent variables are derived in a closed-form iterative manner, which is effective for reducing the performance deterioration caused by the coupling between estimation errors and identification errors. Loopy belief propagation is exploited to approximately calculate the probability of multipath data association, saving the computational cost significantly. Simulation results of over-the-horizon radar multitarget tracking show that the proposed algorithm outperforms multihypothesis multipath track fusion and multi-detection (hypothesis-oriented) multiple hypothesis tracker, especially under low signal-to-noise ratio circumstance.

The positioning of a target with radar measurements could be inaccurate because of the atmospheric refraction effects. We present a technique for determining the refractivity parameters using known position information of cooperative targets. The estimated refractivity parameters are then used to find the position of a noncooperative target, along with its radar range and angle measurements. The performance of the proposed algorithm is assessed by comparing the mean squared error of the estimate with the Cramer–Rao lower bound as well as by testing the algorithm with real field data. Ground-based military surveillance radar can adopt the proposed algorithm and benefit when there are some cooperative flying aircraft while there is no external refractivity information such as radiosonde data.

Compact millimeter wavelength radars have been widely used for applications such as remote sensing of clouds, guidance avionics, and recently, automotive navigation. However, the short wavelength of these radars limits their maximum unambiguous Doppler velocity. A common solution to this problem is to subsequently unfold the Doppler velocity estimate with the staggered pulse repetition time (PRT) algorithm, which requires two different PRTs to be employed in sequence. This article investigates a potentially more rapid method to extend the Doppler velocity Nyquist interval. We estimate Doppler velocity using a pair of frequency diverse pulses separated by a time lag that is significantly shorter than the PRT. During the first PRT, two pulses with center frequencies $f_1$, followed by $f_2$, separated by a lag $\tau$ are transmitted. During the next PRT, the pulses transmitted are in the order $f_2$ followed by $f_1$. Doppler velocity is then estimated using the sum of the Doppler phases derived from $f_1$/$f_2$ and $f_2$/$f_1$ sequences. The focus of this article is the demonstration of this algorithm in a beam-filled scenario and the error canceling algorithm design. Based on Monte-Carlo simulations and data collected with the NASA Goddard Space Flight center's Cloud Radar System, the algorithm is demonstrated on nearly static surface echoes. Projected aircraft speeds and traditional pulse-pair estimates are employed as references. The algorithm was found to perform adequately on surface echoes for a low spectrum width of the order of 0.5 m/s and SNR comparable to 20 dB. Doppler velocity retrievals in a cirrus cloud layer with low velocity turbulence retained some qualitative structure, albeit with significantly degraded precision due to the lost coherence.

Low-bit quantization of echo improves storage and leads to more efficient downlink transmission of spaceborne synthetic aperture radar (SAR) systems. In this paper, a new parametric quantized iterative hard thresholding (PQIHT) algorithm is proposed to refocus the images of moving targets with low-bit quantized SAR data, based on the combination of quantized iterative hard thresholding (QIHT) and the parametric sparse representation. The blurred and quantization-error-involved subimage of the region of interest (ROI) containing the moving target is represented in a sparse fashion through an adaptive parametric dictionary. The QIHT with a pruned searching method is performed for efficiently estimating the motion-adaptive parameter inside the dictionary, refocusing the ROI image and suppressing the quantization-induced error in an iterative way. Different from the conventional QIHT algorithm with a fixed dictionary that can only represent stationary targets, the proposed method exploits a parametric dictionary with a parameter related to target motion status, which is capable of adaptively representing the radar echo from a moving target with unknown motion status and, therefore, is suitable for moving target refocusing. Simulations and experiments on real GF-3 satellite SAR data demonstrate that, compared with the conventional parametric sparse representation framework for moving target refocusing based on purely precise data, the proposed algorithm can provide satisfactory quality of moving target refocusing with remarkably reduced data volume.

We consider the problem of minimizing the disturbance power at the output of the matched filter in a single antenna cognitive radar set-up. The aforementioned disturbance power can be shown to be an expectation of the slow-time ambiguity function (STAF) of the transmitted waveform over range-Doppler bins of interest. The design problem is known to yield a nonconvex quartic function of the transmit radar waveform. This STAF shaping problem becomes even more challenging in the presence of practical constraints on the transmit waveform such as the constant modulus constraint (CMC). Most existing approaches address the aforementioned challenges by suitably modifying or relaxing the design cost function and/or the CMC. In a departure from such methods, we develop a solution that involves direct optimization over the nonconvex complex circle manifold, i.e., the CMC set. We derive a new update strategy [quartic-gradient-descent (QGD)] that computes an exact gradient of the quartic cost and invokes principles of optimization over manifolds toward an iterative procedure with guarantees of monotonic cost function decrease and convergence. Experimentally, QGD can outperform state-of-the-art approaches for shaping the ambiguity function under the CMC while being computationally less expensive.

We consider the problem of estimating the elevation angle in the presence of multipath. The proposed method belongs to the class of maximum-likelihood-like estimators and employs a modified specular reflection model that accounts for the uncertainty of the steering vector by assuming that they are subject to unknown deterministic perturbations with bounded norms. The analysis, performed using convex optimization methods, allows us to obtain computationally efficient implementations of the approach. Real-world results and computer simulations confirm the improved behavior of the proposed robustified estimators.

This paper addresses the design and performance analysis for a preview control laser range finder (LRF) based terrain-following system for fixed-wing unmanned aerial vehicles. The complexity and performance of such systems depend predominantly on the chosen concept and the layout of its hardware components, mainly its sensors. The related estimation and control algorithms designed for the terrain-following task have to further address system uncertainties and disturbances. In this paper, a laser range sensor based terrain-following system is proposed. The various error factors are discussed and the error propagation through the system is analyzed. The resulting error model is first verified using a numerical simulation, Subsequently, it is utilized to quantify the system tracking error, outline the preferred values for various system parameters in order to achieve higher terrain-following performance, and finally suggest the optimal pointing angle for the LRF. The suggested analysis approach and results grant an effective analytical method for designing such terrain-following system and evaluating its performance.

Motivated by the problem of radar target recognition, we develop a label-aided factor analysis (LA-FA) model for statistical modeling of high-resolution range profile (HRRP) under the prerequisite that the HRRP data are Gaussian distributed. The LA-FA model is the extension of the multitask learning-based factor analysis (MTL-FA) model, which is mainly applied to the recognition problem with small training data size. Compared to the MTL-FA model, our LA-FA model introduces the discrete class labels via Sigmoid-Bernoulli hierarchy to restrict the learning of model parameters, which offers the potential to enhance the separability of statistical models from different classes, thus beneficial to the improvement of recognition capability. In addition, since the noise level of a test sample is usually different from those of the training samples in the real application, we introduce a noise-robust modification method for Gaussian-based models. The proposed modification method is implemented by updating the noise level parameter of the statistical model according to the estimated signal-to-noise ratio (SNR) of test HRRP. Experiments on measured HRRP data demonstrate the better recognition performance of the LA-FA model with limited training data and our noise-robust model modification method under low test SNR. Especially, when there are 20 training HRRP samples per frame, the recognition rate of our LA-FA model is 7% higher than that of the MTL-FA model, and moreover, the recognition accuracy of the noise-robust LA-FA model is 3% higher than that of the LA-FA model without modification under the condition of SNR = 15 dB.

A deceptive jamming method against terrain observation by progressive scans synthetic aperture radar (TOPSAR) that considers the effect of the changing relative position between a jammer and a TOPSAR platform is presented. The method can generate flexible false targets with varying Doppler centroids. The jamming problem is decomposed into simpler subproblems of the same type, according to the relative position between the jammer and the satellite. The partial deceptive jamming modulation function can be pregenerated before the radar parameter reconnaissance. The partitioned scheme produces multiple deceptive jamming batches in parallel on the TOPSAR image to protect large regions of interest.

This paper considers the localization of a moving target using a forward scatter radar consisting of a transmitter and an array antenna receiver. A direct positioning method based on maximum likelihood (ML) estimation is proposed and compared with the conventional two-step method in which the primary parameters, including Doppler shift and angle of arrival, should be determined in the first step. Moreover, closed-form expressions for Cramer–Rao lower bound of both methods are derived. The aforementioned methods are comprehensively compared in terms of positioning accuracy and computational complexity. Theoretical performance study, including determining the minimum required observation time for achieving a certain positioning error level and analyzing the best possible positioning accuracy in extreme scenarios, is carried out. The results of numerical simulations demonstrate that the proposed direct approach is more precise compared to the two-step method and can achieve higher resolution levels.

International audience; The number of nodes in sensor networks is continually increasing, and maintaining accurate track estimates inside their common surveillance region is a critical necessity. Modern sensor platforms are likely to carry a range of different sensor modalities, all providing data at differing rates, and with varying degrees of uncertainty. These factors complicate the fusion problem as multiple observation models are required, along with a dynamic prediction model. However, the problem is exacerbated when sensors are not registered correctly with respect to each other, i.e. if they are subject to a static or dynamic bias. In this case, measurements from different sensors may correspond to the same target, but do not correlate with each other when in the same Frame of Reference (FoR), which decreases track accuracy. This paper presents a method to jointly estimate the state of multiple targets in a surveillance region, and to correctly register a radar and an Infrared Search and Track (IRST) system onto the same FoR to perform sensor fusion. Previous work using this type of parent-offspring process has been successful when calibrating a pair of cameras, but has never been attempted on a heterogeneous sensor network, nor in a maritime environment. This article presents results on both simulated scenarios and a segment of real data that show a significant increase in track quality in comparison to using incorrectly calibrated sensors or single-radar only.