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By using uniformly distributed phases for each bit, one can generate a waveform with an almost ideal aperiodic ambiguity function (thumbtack). Moreover,a set of such codes will potentially exhibit low cross-correlation, making them a family of codes suitable for multiple and diverse applications, ranging from 1) MIMO radars, 2) addressing the joint radar-communication problems, 3) mitigating the interference issues of coexistence of multiple radar systems operating in the same region, and 4) increasing the robustness of the system to jamming. This article presents the full analysis of the probability density function of the peak sidelobe level of such codes, as well as a full treatment of the ambiguity function to show mathematically what could be obtained by generating random phase codes. A simple and effective greedy algorithm for generating multiple codes with improved performance is devised increasing the practicality of this waveform family, and numerical results are provided to support the theoretical ones. [ABSTRACT FROM AUTHOR]

This article proposes an accurate clutter synthesis method for a dynamic radar under various environmental conditions. Previous clutter synthesis methods mostly rely on empirical clutter models fitting the measurement results obtained from the limited conditions of observations. Inherently, these models have difficulties synthesizing clutter for unexplored environments and operational conditions. The method presented in this article overcomes this limitation by creating and summing the clutter patch by patch. The clutters of individual patches are synthesized by the clutter model corresponding to each patch's specific conditions, such as the grazing angle, ground texture, and wind velocity. It enables one to synthesize clutter signals under various observation scenarios accurately. For example, this work demonstrates the synthesis of clutter signals from heterogeneous textures for an aircraft radar on a mission. Also, the clutter signals for a monopulse radar are synthesized and included in the radar simulation to evaluate the performance of a detection algorithm. Potentially, this method can be utilized to design a robust radar algorithm against the clutter environment of interests. For a given condition of measurement, it is shown that the synthesized clutters agree well with the actual measured data in their statistical characteristics. The program is freely available through the open code. [ABSTRACT FROM AUTHOR]

Target masking is a pervasive problem in radar signal processing: the range sidelobes of the waveform’s matched filter response may cause a strong target to prevent the detection of nearby weaker targets. Common solutions to sidelobe level reduction for frequency-modulated waveforms result in a signal-to-noise ratio (SNR) loss. In this correspondence, we propose a novel method using pulse diversity to reduce the range sidelobes while avoiding an SNR loss. The proposed approach is based on shaping the power spectrum of the summation of a train of constant-amplitude linearly frequency-modulated pulses to resemble a Gaussian function. We present a numerical example demonstrating a drastic sidelobe level reduction. Importantly, our approach avoids any processing SNR loss. [ABSTRACT FROM AUTHOR]

Bistatic radar signal processing often assumes a constant velocity for target detection. The bistatic geometry alters, however, the effectively measurable velocity in relation to the target position and motion even if the target has a constant ground speed. Hence, the bistatic geometry induces instantaneous bistatic target acceleration. This is especially the case if a straight-line target trajectory cuts across several closely spaced velocity isolines. The letter tries to quantify this geometry-induced instantaneous bistatic acceleration, in general, for straight-line target trajectories. Thereby, position-dependent acceleration values are derived for a practical use case. [ABSTRACT FROM AUTHOR]

Radar specific emitter identification (SEI) is the process of uniquely identifying an individual emitter from the same class of radars by their individual properties that arise from hardware imperfections. However, it is challenging or perhaps impossible to generate globally unique emitter identifiers by using SEI techniques alone, due to the increasing number of radars and the subtle differences in their signal properties. We therefore introduce a multitier radar emitter identification concept that includes radar function identification, model identification, and SEI. The combination of function identifiers, model identifiers, and specific emitter identifiers could generate globally unique radar emitter identifiers. In this article, we propose to use the radio frequency (RF) features extracted from time domain transient signals for radar model identification. The RF features include the duration, maximum derivative, skewness, kurtosis, mean, variance, fractal dimension, Shannon entropy, and polynomial coefficients of the normalized energy trajectory of a transient signal, as well as the area under the trajectory curve. We propose three RF fingerprints for radar model identification, each consisting of a predetermined subset of the features. The performance of the RF fingerprints was evaluated by using five classification algorithms with two radar datasets. Our results show attractive performance with respect to hetero-model radar identification. In particular, for the hetero-model radar dataset, the pair of the all-features (AF) fingerprint and gradient boosting algorithm achieved 91.9, 90.25, and 91.1% classification accuracy for three, four, and five emitters, respectively. On this basis, we conclude that the proposed AF fingerprint could be applied directly to radar model identification by training the gradient boosting algorithm using a dataset with many radars per model. [ABSTRACT FROM AUTHOR]

Passive radar signal processing is hindered by the need to sample a reference signal from the chosen illuminator, which is subject to sensor noise and interference from clutter returns. The reference signal may be demodulated and reconstructed to remove artifacts. However, without accounting for the transmitter's clock offset, the remodulated reference signal will be a poor match for the surveillance signal. This article evaluates clock offset estimation methods in terms of the radar ambiguity surface, thereby appraising them specifically for passive radar applications. Existing offset estimation methods are shown to meet variance bounds; however, these bounds worsen with increased clock offset. Ambiguity surfaces constructed from signals reconstructed with poor clock offsets exhibit power leakage. A new clock offset estimator is proposed, which refines coarse estimates made by an existing method. The new estimator is demonstrated to achieve accurate estimation of clock offset. Reference signals reconstructed with these estimates produce ambiguity surfaces with no significant power leakage. [ABSTRACT FROM AUTHOR]

This article considers three interrelated adversarial inference problems involving cognitive radars. We first discuss inverse tracking of the radar to estimate the adversary's estimate of us based on the radar's actions and calibrate the radar's sensing accuracy. Second, using revealed preference from microeconomics, we formulate a nonparametric test to identify if the cognitive radar is a constrained utility maximizer with signal processing constraints. We consider two radar functionalities, namely, beam allocation and waveform design, with respect to which the cognitive radar is assumed to maximize its utility and construct a set-valued estimator for the radar's utility function. Finally, we discuss how to engineer interference at the physical layer level to confuse the radar that forces it to change its transmit waveform. The levels of abstraction range from smart interference design based on Wiener filters (at the pulse/waveform level), inverse Kalman filters at the tracking level, and revealed preferences for identifying utility maximization at the systems level. [ABSTRACT FROM AUTHOR]

Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-of-arrival (DOA) estimation, a large number of receiving antenna elements are needed. The high dimension of the element-level data inevitably leads to a large computational burden for digital signal processing. This problem can be overcome by transforming the element-level data into a lower dimensional beamspace. In this article, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets predefined performance criteria by exploiting an acceptable tradeoff between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. It comprises a precalculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method. [ABSTRACT FROM AUTHOR]

Fast Fourier transform (FFT) is one of the fundamental signal processing algorithms widely used in radar applications. The Radon–Fourier transform (RFT) can be seen as an FFT generalization that can overcome some of its limitations. This work derives three spectral RFT (SRFT) based approaches to address major challenges of the multiple-input multiple-output automotive radars. First, two SRFT-based approaches are derived to increase maximal target detection range by mitigation of target migration in range and direction of arrival, jointly, and by multidwell integration processing, which increases the radar coherent integration time without compromising its detection update rate. Next, SRFT-based approach is proposed to address the cluster-to-track association problem that arises in multiple distributed target tracking scenarios that characterize automotive radar operation in dense urban environments. [ABSTRACT FROM AUTHOR]

With the advancement of autonomous driving, the number of automotive radar sensors grows dramatically. As a result, interference becomes a major issue that has a vital importance for the future of automotive radar. Parallel to this development, radar concepts based on the digital signal generation and processing such as orthogonal frequency-division multiplexing (OFDM) have been studied over the past few years. The realization of the radar functionality based on digital waveforms provides a large degree of flexibility as well as adaptability. Among others, this can be used for increasing the interference robustness of digital radar systems compared to the traditional automotive radar. In this article, we propose a novel approach to mitigating automotive radar interference based on cognitive interference avoidance, which we demonstrate for the digital OFDM radar. We introduce the main building blocks of such a cognitive radar and focus specifically on waveform adaptation. The proposed waveform adaptation methods enable dynamical adjustment to interference, including adaptation during a single measurement cycle. The feasibility and performance of the proposed concepts are studied in simulations and validated using measurements with an OFDM radar prototype. [ABSTRACT FROM AUTHOR]

Radar micro-Doppler (m-D) signature, which reflects the micromotion dynamic and structural characteristics of radar target with micromotion, has received increasing attention. Most of the existing m-D signature-extraction methods operate in the time domain or the time-frequency domain. Different from these methods, in this paper, an m-D period estimation approach that operates in the ambiguity domain is proposed. Although the ambiguity function (AF) has been widely used in the field of radar signal processing, its application for m-D signal is introduced for the first time. It is proved that the AF of m-D signal exhibits periodicity along the lag axis and has the best concentration when the lag equals to multiples of the m-D period. Based on this, three AF concentration statistics are employed to capture the periodicity and to provide the m-D estimate. The most important property of the AF concentration statistics is that they are (or approximately) invariant to polynomial translations with terms not larger than second order even if the signal is Doppler ambiguous. Numeric simulation and real radar experiments are used to validate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

A radar signal processor subchain containing a pulse compression module employing binary $\pm 1$ phase codes is studied in an effort to understand the behavior of the output mean and variance as functions of phase code imbalance, in noise-limited environments. It is a relatively simple subchain consisting of an R/Theta limiter, a pulse compressor using a length- $N$ phase code for match filtering, scaling by $1/N$ , and a squarer (also known as $I^2+Q^2$). The input to this system is a sampled input string assumed to be formed of a small dc bias with added two-dimensional Gaussian noise. The output of this system is studied for a range of values of the pulse compression code imbalance. Imbalance is the difference between the number of 1 and $-1$ elements in the binary phase code. A nonzero imbalance is sometimes a cause for concern, since the pulse compression dc gain is proportional to the code imbalance. Hence, the dc gain might be expected to yield a nonzero component at the output, resulting in an increased risk of false alarms on a radar return comprised of noise alone. However, we show that for imbalance of $\pm \sqrt{N}$ , any contribution from the dc bias is nearly eliminated. When $N$ is a perfect square, dc bias is completely eliminated, in theory at least. Analytical formulae are provided, along with simulation results for pulse compression code length $N=512$. [ABSTRACT FROM AUTHOR]

We develop and present a novel minimum estimation error variance waveform design method. The method optimizes the spectral shape of a unimodular radar waveform such that the performance of a joint radar-communications system that shares spectrum is maximized. We also develop the novel spectral water-filling successive interference cancelation data rate, which employs the continuous spectral water-filling algorithm to obtain the optimal communications power spectrum. We perform a numerical study to compare the performance of the new technique with the previously derived spectral mask shaping method. The global estimation rate and the data rate capture the radar and the communications performance, respectively. [ABSTRACT FROM AUTHOR]

We apply recent theory in the high-SNR estimation of channel capacity to study how much information may be encoded in radar pulse waveforms. Waveforms are constrained to those which admit a high-gain, low-sidelobe pulse compression filter. Using simple numerical methods, we compute the high-SNR capacity of a Gaussian channel using such a waveform alphabet. The resulting tradeoff curves indicate a notably favorable theoretical tradeoff space between signal processing performance and high-SNR channel capacity. [ABSTRACT FROM AUTHOR]

Both economic incentives and policy trends motivate the study of spectrum sharing between radar and wireless communications as a means to mitigate spectrum scarcity. This paper proposes a novel, temporal signal processing algorithm for a radar to mitigate interference from cyclostationary, linearly modulated digital communications (LMDC) interference such as orthogonal frequency division multiplexing (OFDM) or code division multiple access (CDMA). The proposed algorithm has the form of a novel whitening filter followed by a matched filter, and as such it optimizes the statistical deflection among the class of all linear detection filters. The derived cyclostationary whitening filter has equivalent mathematical representations as: 1) the form of a multiuser detector followed by an interference canceler, and 2) a frequency-shift (FRESH) filter. Performance results indicate that the derived cyclostationary whitener can produce signal-to-interference-plus-noise ratio (SINR) improvements ranging from negligible to up to $\text{20 dB}$ for the parameters used in this study. The cyclostationary whitener leads to the greatest SINR improvements when the interference-plus-noise is dominated by only a few LMDC signals, such as might occur in a near-far scenario. [ABSTRACT FROM AUTHOR]

Maximum likelihood (ML) estimation of the loading factor under affine constraints on the covariance eigenvalues is addressed. Several situations of practical interest for radar are considered, and the corresponding ML solutions to the loading factor estimation problem are derived in closed form. Furthermore, it is shown that the constrained ML problem, the constrained geometric approach, and the constrained problem of mean square error minimization (with respect to the loading factor) all lead to the same solution. At the analysis stage, the effectiveness of the resulting covariance estimators is evaluated in terms of both the signal-to-interference-plus-noise ratio and the receiving beampattern shape and compared with that of other covariance estimation methods available in the literature. Finally, a receiving architecture based on the adaptive matched filter that exploits the new loaded covariance estimators is also considered to assess the benefits of the new strategies in terms of detection probability. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of censoring outliers in a class of complex multivariate elliptically contoured distributed radar data, which is a vital issue in radar signal processing applications, such as adaptive radar detection and space-time adaptive processing. The maximum likelihood (ML) estimate of the outlier subset is derived resorting to the generalized likelihood function (GLF) criterion. Since the ML estimate involves the solution of a combinatorial problem, a reduced complexity but approximate ML (AML) procedure is also considered. At the analysis stage, the performance of the AML method is evaluated in the presence of both simulated and real radar data, also in comparison with the conventional generalized inner product (GIP) and the reiterative censored GIP (RCGIP) algorithms. The results highlight that the AML technique achieves a satisfactory performance level and can outperform both GIP and RCGIP in some situations. [ABSTRACT FROM AUTHOR]

A distributed multiple-input multiple-output (MIMO) radar system is capable of tracking multiple targets under the “defocused transmit-focused receive” (DTFR) operating mode, in which each transmitter forms a completely defocused beam to illuminate the whole surveillance region and each receiver adopts a focused beam to attain a higher resolution. However, when operating in this mode, there exists a resource optimization problem on the allocation of receive-beams. To address this problem, a receive-beam resource allocation (RBRA) strategy is proposed in this paper. The key mechanism is to implement the optimal allocation between receive-beams and targets based on the feedback information in the tracking recursion cycle, with the objective of improving the worst tracking accuracy with multiple targets. Since the posterior Cramér–Rao lower bound (PCRLB) provides a lower bound on the accuracy of target state estimates, it is derived and adopted as an optimization criterion. It is shown that the optimal RBRA is a multidimensional nonconvex assignment problem that is NP-hard. We propose an efficient convex relaxation optimization to solve it. Numerical results demonstrate the superior performance of the proposed strategy in terms of the worst case tracking root mean-square errors. [ABSTRACT FROM AUTHOR]

This paper handles the problem of optimizing the scanning policy of a surveillance system in the presence of Rayleigh targets in a mixture of K-distributed clutter and Gaussian noise. Adopting the detection rate (DR) and false alarm rate (FAR) as performance measures, we show that the said optimization amounts to solving a constrained DR maximization, wherein the free parameters are the transmit beamwidth, the dwell time, and the detection threshold, whereas the constraints concern the maximum FAR and time on target. A thorough performance assessment is offered, aimed at eliciting the influence of the signal-to-noise and clutter-to-noise ratios, the clutter shape parameter, and the rate of change of the target response. The results indicate that, although a uniformly optimum transmit policy does not exist, some trends can be devised, which appears of particular relevance especially for systems that can avail themselves of some degree of cognition. [ABSTRACT FROM AUTHOR]

The problem of centralized adaptive constant false alarm rate (CFAR) detection with registration errors in multistatic radar is considered. When the observation data from different radar sites are not properly aligned after transformed into a common coordinate system, registration errors occur. This can severely degrade the target detection performance and positioning accuracy of multistatic radar. In this paper, we focus on the design of adaptive detectors that are applicable for centralized target detection with registration errors in multistatic radar. A maximum likelihood estimation-based generalized likelihood ratio test detector and a maximum a posteriori estimation-based generalized likelihood ratio test detector are developed. Both detectors possess the CFAR property with regard to the unknown statistics of the background noise. Accordingly, the performance of the two proposed detectors are analyzed. [ABSTRACT FROM AUTHOR]

This paper presents results from experiments that utilize Global-Positioning-System-like terrestrial positioning signals in a passive radar system to track aircraft over a metropolitan area. Unlike communication signals, these positioning signals offer unique properties that are advantageous for a passive radar. Exploitation of these properties provides an opportunity to reduce hardware complexity and to enable passive radar systems with a lower cost. This paper provides a detailed description of the signal processing in the context of low-cost hardware. [ABSTRACT FROM AUTHOR]

In this study, we propose an efficient strategy to exploit cross-range resolutions (CRRs) of inverse synthetic aperture radar (ISAR) images to improve classification performance. The proposed method preprocesses an ISAR image of an unknown target using both the CRR of an unknown ISAR image, which can be obtained using the cross-range scaling algorithm, and that of the ISAR images in a training database (DB). Specifically, the proposed method adjusts the unknown ISAR image so that the CRR of the adjusted unknown ISAR image is identical to that of the ISAR images in the training DB. The adjusted unknown ISAR image is then directly used for the subsequent classification task, thus considerably improving classification performance. [ABSTRACT FROM AUTHOR]

We present a Xampling-based technology enabling interference-free operation of radar and communication systems over a common spectrum. Our system uses a recently developed cognitive radio (CRo) to sense the spectrum at low sampling and processing rates. The Xampling-based cognitive radar (CRr) then transmits and receives in the available disjoint narrow bands. Our main contribution is the unification and adaptation of two previous ideas—CRo and CRr—to address spectrum sharing. Hardware implementation shows robust performance at SNRs up to –5 dB. [ABSTRACT FROM AUTHOR]

A novel radio frequency (RF) steganography scheme is proposed to hide digital communication in linear frequency modulation (LFM) radar signals. This joint radar/communication waveform serves two purposes simultaneously: it performs as the original radar waveform, and it provides a covert communication to legitimate receivers. The proposed RF steganography scheme hides digitally modulated communication information inside an LFM radar signal to prevent enemy from detecting the existence of such hidden information via a new modulation and variable symbol duration design. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

When a monopulse radar is subjected to main-lobe barrage interference, the target and the interference source are both located within the 3 dB beamwidth of the radar; the target returns are subsumed by the interference, which results in incorrect detection, erroneous angle measurements, and tracking error. Based on a hybrid polarimetric monopulse radar with six channels, a new approach is presented for estimating the target angle from a single snapshot in the presence of main-lobe barrage interference. By means of polarization filtering and polarization synthesis of signals from multiple channels with two orthogonal polarizations, a new pair of sum and difference signals is formed that represents the angle difference between the target and the interference source. After standard monopulse processing of these new signals and additional compensation, the estimate of the target angle is derived with low computational complexity. [ABSTRACT FROM AUTHOR]

Multiframe track-before-detect (MF-TBD) usually uses sliding-window-based batch processing, where a number $N$ of the latest data frames are jointly processed at each measurement time. The sliding window mechanism compromises the operating efficiency of MF-TBD by increasing both computational costs and memory requirements, thus, heavily restricting its application in practical problems. In this paper, an improved recursive implementation for MF-TBD is proposed. Unlike the sliding-window-based implementation, the proposed method calculates the merit function, a measure of the possibility that a state is target originated, of the current batch based on an approximated recursive relationship between the merit functions of consecutive batches. As a result, instead having to process the whole batch, at any given time only the latest frame needs to be processed. The recursive relationship is first derived for any arbitrary merit function, and then explored further with several typical merit functions that are used in MF-TBD. Both the theoretical analysis and simulation results demonstrate that the proposed method can achieve almost $N$ times reduction in computational complexity and memory requirements with negligible performance loss. [ABSTRACT FROM AUTHOR]

Access to the electromagnetic spectrum is an ever-growing challenge for radar. Future radar will be required to mitigate RF interference from other RF sources, relocate to new frequency bands while maintaining quality of service, and share frequency bands with other RF systems. The spectrum sensing, multioptimization (SS-MO) technique was recently investigated as a possible solution to these challenges. Prior results have indicated significant improvement in the signal-to-interference plus noise ratio at the cost of a high computational complexity. However, the optimization computational cost must be manageable in real time to address the dynamically changing spectral environment. In this paper, a bioinspired filtering technique is investigated to reduce the computational complexity of SS-MO. The proposed technique is analogous to the processing of the thalamus in the human brain in that the number of samples input to SS-MO is significantly decreased, thus, resulting in a reduction in computational complexity. The performance and computational complexity of SS-MO and the proposed technique are investigated. Both techniques are used to process a variety of measured spectral data. The results indicate a significant decrease in computational complexity for the proposed approach while maintaining performance of the SS-MO technique. [ABSTRACT FROM AUTHOR]

This paper is a first introduction to the concept of using global navigation satellite systems (GNSS) as illuminators of opportunity in a passive bistatic real-time radar system for maritime target indication applications. An overview of the system concept and the signal processing algorithms for moving target indication is provided. To verify the feasibility of the system implementation as well as test the developed signal processing algorithms, an experimental test bed was developed and the appropriate experimental campaign with the new Galileo satellites and a ferry as the target was carried out. The results confirm the system concept and its potential for multistatic operation, with the ferry being detected simultaneously by two satellites. [ABSTRACT FROM AUTHOR]

Sparse signal representation is used to coherently separate the main rotor blade components of a helicopter from the composite data of complex time series radar returns. Received signals of this type typically consist of returns from the rotating main and tail rotor blades, the helicopter body, and other residual components which may overlap in time and frequency, thus making it difficult for conventional time–frequency separation techniques to be applied for the signal separation. In the proposed algorithm, a sparse signal representation is applied with the use of tunable Q wavelet transform to construct the dictionary, and basis pursuit denoising is used for the signal reconstruction of the component of interest. The algorithm shows very effective separation of the main rotor blade component from the composite radar returns, which is demonstrated using both simulated and real radar data at X-band. [ABSTRACT FROM AUTHOR]

The Sparse Fourier Transform (SFT), designed for signals that contain a small number of frequencies, enjoys low complexity, and thus is ideally suited for big data applications. In this paper, we propose Robust Sparse Fourier Transform, (RSFT), which is a modification of SFT that extends the SFT advantages to real world, noisy settings. RSFT can accommodate off-grid frequencies in the data. Furthermore, by incorporating Neyman–Pearson detection in the SFT stages, frequency detection does not require knowledge of the exact sparsity of the signal, and is robust to noise. We analyze the asymptotic performance of RSFT, and study the computational complexity versus detection performance tradeoff. We show that, by appropriately choosing the detection thresholds, the optimal tradeoff can be achieved. We discuss the application of RSFT on short-range ubiquitous radar signal processing and demonstrate its feasibility via simulations. [ABSTRACT FROM AUTHOR]

This paper presents a novel low-cost technique for digital beamforming on receive in phased array radar. The presented technique can be used to synthesize multiple beam patterns in different angular directions and can be implemented after analog summation of the received signals using only one received pulse. Since the digital beamforming in our work is implemented after summation of the received signals, a reduction of the overall cost, size, and power consumption of the phased array front-end becomes possible in comparison to standard digital beamforming. The performance and effectiveness of the here-proposed beamforming technique are assessed by means of modeling and experimental verification. [ABSTRACT FROM PUBLISHER]

This paper proposes a new scheme for improving the classification performance of inverse synthetic aperture radar (ISAR) images. The proposed scheme utilizes the trace transform to gather abundant information from an ISAR image, regardless of the spatial distribution of the target response in the ISAR image. In simulations using ISAR images with various spatial distributions, the proposed scheme substantially improved the classification performance compared with existing methods, which are highly vulnerable to spatial variation. [ABSTRACT FROM PUBLISHER]

Noise radars possess several advantageous properties, including low probability of detection and identification, especially when working in continuous-wave mode. One of the main drawbacks of such radars is the occurrence of the masking effect, when a weak target echo is masked by the sidelobes of a strong target echo. The sidelobes of the ambiguity function emerge on the level of the time–bandwidth product below the main peak and are spread in the entire range–Doppler plane. While most approaches to mitigate the masking effect focus on the processing of the received signal, it is possible to move the computation burden to the waveform design phase. This paper describes a filter-based method of creating noise-like waveforms that have very low sidelobes in the area of certain range and Doppler shifts. Both theoretical analysis and measurement results are presented and compared with the lattice filter method of masking effect cancellation. [ABSTRACT FROM PUBLISHER]

High linearity and low noise are important conditions for high-resolution imaging in wideband linear frequency modulation (LFM) radar systems, where stretch processing is commonly adopted. However, in practice, hardware may introduce amplitude and phase distortions to radar signals. The distortions will increase the range sidelobes (RSLs) of the dechirped returns and cause the decline of imaging quality. Traditional time-domain RSL correction methods are greatly influenced by time delay and can only be used in a small range window. To solve the problem, we propose a novel frequency-domain RSL correction (FDRC) method in this paper. For FDRC, we first multiply the extracted distortion and the stretched return by a baseband LFM signal and then perform the correction in the frequency domain. Both theoretical and simulation results show that the proposed method provides equivalent performances in RSLs compared to other existing distortion correction methods in a large range window. Besides, the proposed method can be much easily implemented in practical radar systems. [ABSTRACT FROM AUTHOR]

Woodward?s ambiguity function measures the ability of a radar signal to simultaneously measure the range of an object via time delay and its velocity using Doppler shift. The ambiguity function is a foundational staple in radar signal processing. Six myths concerning Woodward?s ambiguity function (AF) are addressed in this paper: 1) the AF is uniquely defined, 2) the magnitudes of the various definitions of the AF are the same, 3) the AFs of the baseband and corresponding radio frequency signal are the same, 4) the maximum of a correlation?s magnitude determines optimality, and 5) multiplying a signal by a complex linear chirp rotates the AF and 6) the AF is not invertible. Each myth is explained, analyzed, and resolved. In discussing myth 6, the formula for inversion of the ambiguity function to its spawning signal is derived. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of detecting a multichannel signal in the presence of interference. The signal lies in a known subspace but with unknown coordinate. The interference is assumed to be orthogonal to the signal in the whitened space. We propose an effective detector, according to the criterion of the generalized likelihood ratio test (GLRT). The proposed GLRT has better detection performance than the corresponding Rao andWald tests. Remarkably, it is found that the GLRT in the presence of orthogonal interference has the same form as that in the absence of interference. However, the orthogonal interference does affect the detection performance of the detector. We derive closed-form expressions of the probabilities of detection and false alarm for the proposed detector, which are verified by Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Electronic warfare support (ES) plays an important role in gathering and analyzing signals from enemy emitters to provide friendly electronic attack (EA) systems or electronic protection (EP) systems with crucial information. However, ES systems may have difficulty in gathering meaningful signals because there is no prior information about the signal in most cases, and the signal is sometimes too weak or distorted. In this paper, we propose a novel weak-signal detection method to solve these problems based on a modified sinc wavelet transform. This method exploits the frequency shifting property to change the center frequency of a band-pass filter and then applies the scaling property of the continuous-time wavelet transform to vary its bandwidth. This approach is distinguished from previous works in terms of the detection capability of weak signals with high-frequency or multifrequency components. Computer simulation results show that the proposed method can be used in practical implementation to deal with signals that have a low signal-to-noise ratio (SNR). [ABSTRACT FROM PUBLISHER]

Random and unwanted fluctuations, which perturb the phase of an ideal reference sinusoidal signal, may cause significant performance degradation in radar systems exploiting coherent integration techniques. To quantify the resulting performance loss, we develop a fast-time/slow-time data matrix radar signal representation, modeling the undesired phase fluctuations via multivariate circular distributions and describing the phase noise power spectral density (PSD) through a composite power-law model. Hence, we accurately predict the performance degradation experienced by moving target indication (MTI) algorithms for clutter cancellation, providing a closed form expression for the improvement factor I. The subsequent analysis shows that phase noise affects I directly through its characteristic function (CF). Additionally, I shares a robust behavior with respect to the actual phase noise multivariate circular distribution, as long as the phase noise PSD correctly represents the available measurements. [ABSTRACT FROM PUBLISHER]

Conventional inverse synthetic aperture radar observes the target from one viewing direction and only obtains partial information. By using a distributed multiple-input multiple-output (MIMO) array, the target can be observed from multiple views and a better image can be obtained. The distributed MIMO radar imaging signal model is derived in this paper. Because the strong scatterers (scattering centers) of a target are usually sparsely distributed, a sparse signal recovery algorithm using homotopy between the ℓ1 norm and ℓ0 norm applicable to complex signals is proposed to recover the strong scatterers. This ℓ1 norm and ℓ0 norm homotopy method is then extended to the block sparse signal case. The equivalence between complex ℓ1 ℓ0 norms homotopy method and block size of two real ℓ1 ℓ0 norms homotopy method is demonstrated. This proves that a complex signal-based algorithm is better than a real signal-based algorithm, which only separates the complex signal into its real part and imagery part and does not use their block property. This method is compared with other methods, i.e., block orthogonal matching pursuit (BOMP), block CoSaMp, block smoothed ℓ0 norm based method (BSL0), spectral projected gradient (SPG L1), and block sparse Bayesian learning (BSBL). The signal recovery performance of ℓ1 ℓ0 norms homotopy method is better than the other methods except for BSBL. However, BSBL costs more computationally. Imaging using distributed MIMO antennas is simulated. The simulation results show that the image quality using distributed MIMO radar is better than that using monostatic (or bistatic) MIMO radar. [ABSTRACT FROM AUTHOR]

It is known that use of a random measurement (sensing) matrix usually results in good recovery performance via orthogonal matching pursuit. This paper provides the probability of ensuring the recovery of sparse signals using orthogonal matching pursuit for the case where all entries of the measurement matrix are independently selected from a Gaussian distribution. The analysis relies on the mutual-coherence property of the sensing matrix. [ABSTRACT FROM AUTHOR]

Recently proposed, the one step localization approach for active sensor network estimates the target location directly from the sensor raw signals, as opposed to the classical decentralized two steps approach where intermediate signal processing (e.g., measurement estimation, detection) is performed on each sensor node. A composite multiple hypothesis test based on the generalized maximum likelihood (GML) rule is proposed to cope with the sidelobes of the one step cost function that may cause detection and localization of ghost targets. The proposed method has been verified both on synthetic and real data. [ABSTRACT FROM AUTHOR]

This paper proposes a method for chaotic radar signal (CRS) design with ultralow sidelobe performance. We first modulate the group delay (MGD) of a radar signal using a chaotic mapping sequence in the frequency domain, and then we transform the radar signal to its time domain form by inverse Fourier transformation. The designed CRS has ultralow sidelobe owing to its flat power spectrum density (PSD). Meanwhile, its ambiguity function is thumbtack, and the amplitudes of both in-phase and quadrature components are close to Gaussian noise. Both numerical simulation and experimental results demonstrate the ultralow sidelobe performance of the designed CRS. [ABSTRACT FROM PUBLISHER]

In sparse sensing based distributed multiple-input multiple-output radars, the problem of target estimation is formulated as a sparse vector recovery problem, where the vector to be recovered is block sparse, or equivalently, the sensing matrix is block diagonal and the sparse vector consists of equal-length blocks that have the same sparsity profile. This paper derives the theoretical requirements and performance guarantees for the application of sparse recovery techniques to this problem. The obtained theoretical results confirm previous, simulations-based observations that exploiting the block sparsity of the target vector can further reduce the amount of measurements needed for successful target estimation. For signal recovery, two low-complexity approaches are proposed. The first one is an alternating direction method of multipliers-based sparse signal recovery algorithm, which in addition to significantly reducing computations is also amenable to a parallel and semidistributed implementation. The second approach decouples the location and speed estimation into two separate stages, with each stage addressing a sparse recovery problem of lower dimension while maintaining high estimation accuracy. [ABSTRACT FROM PUBLISHER]

We consider waveform design for colocated multiple-input, multiple-output (MIMO) radar systems where the signal phase is subject to random phase perturbations during propagation. We formulate an iterative algorithm to obtain both waveform parameters and the space-time adaptive weights to maximize the signal-to-interference-plus-noise ratio (SINR). We ensure that the impact of the chosen waveforms on the interference signal is accounted for when modeling the second-order interference statistics. The waveform design technique is then extended to include peak-to-average power constraints. An example of a clutter and target model is provided to show how the optimal waveform design improves radar detection performance in the presence of the signal phase randomization. [ABSTRACT FROM PUBLISHER]

Ambiguity functions (AFs) are obtained for a radar using matched illumination (MI) transmit signals for the detection of range-spread targets in the presence of clutter. The transmit signals are adapted to target and interference spectra and are filtered optimally in the receiver; they are designed to maximize signal-to-interference-and-noise ratio (SINR) power ratios at receiver output. In this paper, expressions for processing gains, spread AFs, and Cramer-Rao bounds on range and Doppler estimates are derived based on likelihood functions. Here, for extended targets, AFs resulting from using optimal MI constant envelope waveforms obtained via phase retrieval techniques demonstrate superior resolution characteristics compared with classic linear frequency-modulated (LFM) signals employing optimal pulse compression. For various target and clutter spectral models, simulation results show that optimally filtered MI signals provide significantly enhanced SINR behavior compared with LFM radar signals. Hopefully, these results set the stage for the induction of MI signaling and receive techniques into conventional radar signal processing and pave the way for realization of one methodology for achieving cognition in radar systems. [ABSTRACT FROM PUBLISHER]

Radar signal design in a spectrally crowded environment is currently a challenge due to the increasing requests for spectrum from both military sensing applications and civilian wireless services. The goal of this paper is to improve a previously devised algorithm for the synthesis of optimized radar waveforms fulfilling spectral compatibility with overlaid licensed radiators. The new technique achieves an enhanced spectral coexistence with the surrounding electromagnetic environment through a suitable modulation of the transmitted waveform energy, which was kept fixed at the maximum level in the previously devised algorithm. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable Signal to Interference Plus Noise Ratio (SINR), spectral shape, and the resulting Autocorrelation Function (ACF), also in situations where the previous technique cannot be applied. [ABSTRACT FROM AUTHOR]

We derive the probability density function (PDF) of a received radar signal in the presence of multipath when a reflecting surface with an arbitrary reflection coefficient exists. Using the PDF, we present the detection probabilities of nonfluctuating targets for various reflection coefficients and analyze the multipath effect on radar detection. [ABSTRACT FROM AUTHOR]

Noise radar systems transmitting incoherent signal sequences have been proposed as powerful candidates for implementing compressively sampled detection and imaging systems. This paper presents an analysis of compressively sampled noise radar systems by formulating ultrawideband (UWB) compressive noise radar imaging as a problem of inverting ill-posed linear systems with circulant system matrices. The nonlinear nature of compressive signal recovery presents challenges in characterizing the performance of radar imaging systems. The suitability of noise waveforms for compressive radar is demonstrated using phase transition diagrams and transform point spread functions (TPSFs). The numerical simulations are designed to provide a compelling validation of the system. Nonidealities occurring in practical compressive noise radar systems are addressed by studying the properties of the transmit waveform. The results suggest that waveforms and system matrices that arise in practical noise radar systems are suitable for compressive signal recovery. Field imaging experiments on various target scenarios using a UWB millimeter wave noise radar validate our analytical results and the theoretical guarantees of compressive sensing. [ABSTRACT FROM AUTHOR]

Radar signal design in a spectrally crowded environment is a very challenging and topical problem due to the increasing demand for both military surveillance/remote-sensing capabilities and civilian wireless services. This paper deals with the synthesis of optimized radar waveforms ensuring spectral compatibility with the overlayed licensed electromagnetic radiators. A priori information, for instance, provided by a radio environmental map (REM), is exploited to force a spectral constraint on the radar waveform, which is thus the result of a constrained optimization process aimed at improving some radar performances (such as detection, sidelobes, resolution, tracking). The feasibility of the waveform optimization problem is extensively studied, and a solution technique leading to an optimal waveform is proposed. The procedure requires the relaxation of the original problem into a convex optimization problem and involves a polynomial computational complexity. At the analysis stage, the waveform performance is studied in terms of trade-off among the achievable signal to interference plus noise ratio (SINR), spectral shape, and the resulting autocorrelation function (ACF). [ABSTRACT FROM AUTHOR]