We derive a detector function for quantum two-mode squeezing (QTMS) radars and noise radars that is based on the use of a likelihood ratio (LR) test for distinguishing between the presence and absence of a target. In addition to an explicit expression for the LR detector, we derive a detector function which approximates the LR detector in the limit where the target is small, far away, or otherwise difficult to detect. When the number of integrated samples is large, we derive a theoretical expression for the receiver operating characteristic (ROC) curve of the radar when the LR detector is used. When the number of samples is small, we use simulations to understand the ROC curve behavior of the detector. One interesting finding is there exists a parameter regime in which a previously-studied detector outperforms the LR detector, contrary to the intuition that LR tests are optimal. This is because neither the Neyman-Pearson lemma, nor the Karlin-Rubin theorem which generalizes the lemma, hold in this particular problem. However, the LR detector remains a good choice for target detection.
Quantum two-mode squeezing (QTMS) radars and noise radars detect targets by correlating the received signal with an internally stored recording. A covariance matrix can be calculated between the two which, in theory, is a function of a single correlation coefficient. This coefficient can be used to decide whether a target is present or absent. We can estimate the correlation coefficient by minimizing the Frobenius norm between the sample covariance matrix and the theoretically expected form of the matrix. Using simulated data, we show that the estimates follow a Rice distribution whose paramet…
We have built and evaluated a prototype quantum radar, which we call a quantum two-mode squeezing radar (QTMS radar), in the laboratory. It operates solely at microwave frequencies; there is no downconversion from optical frequencies. Because the signal generation process relies on quantum mechanical principles, the system is considered to contain a quantum-enhanced radar transmitter. This transmitter generates a pair of entangled microwave signals and transmits one of them through free space, where the signal is measured using a simple and rudimentary receiver. At the heart of the transmitter…
The spherically invariant random process (SIRP) clutter model is commonly used in scenarios where the radar clutter cannot be correctly modeled as a Gaussian process. In this short communication, we devise a novel Maximum-Likelihood (ML)-based iterative estimator for direction-of-departure and direction-of-arrival estimation in the Multiple-input multiple-output (MIMO) radar context in the presence of SIRP clutter. The proposed estimator employs a stepwise numerical concentration approach w.r.t. the objective function related to the marginal likelihood of the observation data. Our estimator le…
Standard noise radars, as well as noise-type radars such as quantum two-mode squeezing radar, are characterized by a covariance matrix with a very specific structure. This matrix has four independent parameters: the amplitude of the received signal, the amplitude of the internal signal used for matched filtering, the correlation between the two signals, and the relative phase between them. In this paper, we derive estimators for these four parameters using two techniques. The first is based on minimizing the Frobenius norm between the structured covariance matrix and the sample covariance matr…
In this paper, we address the problem of target detection in passive multiple-input-multiple-output (MIMO) radar networks. A generalized likelihood ratio test is derived, assuming prior knowledge of the signal format used in the non-cooperative transmit stations. We consider scenarios in which the unknown transmitted signal uses either a linear digital modulation scheme or the Orthogonal Frequency-Division Multiplexing (OFDM) modulation scheme. These digital modulation schemes are used in popular standards including Code-Division Multiple Access (CDMA), Digital Video Broadcasting-Terrestrial (…
Structured covariance matrix estimation in the presence of missing data is addressed in this paper with emphasis on radar signal processing applications. After a motivation of the study, the array model is specified and the problem of computing the maximum likelihood estimate of a structured covariance matrix is formulated. A general procedure to optimize the observed-data likelihood function is developed resorting to the expectation-maximization algorithm. The corresponding convergence properties are thoroughly established and the rate of convergence is analyzed. The estimation technique is c…
In this paper, we propose a new solution for the detection problem of a coherent target in heterogeneous environments. Specifically, we first assume that clutter returns from different range bins share the same covariance structure but different power levels. This model meets the experimental evidence related to non-Gaussian and non-homogeneous scenarios. Then, unlike existing solutions that are based upon estimate and plug methods, we propose an approximation of the generalized likelihood ratio test where the maximizers of the likelihoods are obtained through an alternating estimation procedu…
Joint RADAR communication (JRC) systems that use orthogonal frequency division multiplexing (OFDM) can be compromised by an adversary that re-produces the received OFDM signal creating thus false RADAR targets. This paper presents a set of algorithms that can be deployed at the JRC system and can detect the presence of false targets. The presence of a false target is detected depending on whether there is residual carrier frequency offset (CFO) beyond Doppler in the received signal, with a Generalized Likelihood Ratio Test (GLRT). To evaluate the performance of our approach we measure the dete…
Motivated by future automotive applications, we study the joint target detection and parameter estimation problem using orthogonal time frequency space (OTFS), a digital modulation format robust to time-frequency selective channels. Assuming the transmitter is equipped with a mono-static MIMO radar, we propose an efficient maximum likelihood based approach to detect targets and estimate the corresponding delay, Doppler, and angle-of-arrival parameters. In order to reduce the computational complexity associated to the high-dimensional search, our scheme proceeds in two steps, i.e., target detec…
This paper proposes a novel approach to robust radar detection of range-spread targets embedded in Gaussian noise with unknown covariance matrix. The idea is to model the useful target echo in each range cell as the sum of a coherent signal plus a random component that makes the signal-plus-noise hypothesis more plausible in presence of mismatches. Moreover, an unknown power of the random components, to be estimated from the observables, is inserted to optimize the performance when the mismatch is absent. The generalized likelihood ratio test (GLRT) for the problem at hand is considered. In ad…
In this paper, we address the problem of target detection in the presence of coherent (or fully correlated) signals, which can be due to multipath propagation effects or electronic attacks by smart jammers. To this end, we formulate the problem at hand as a multiple-hypothesis test that, besides the conventional radar alternative hypothesis, contains additional hypotheses accounting for the presence of an unknown number of interfering signals. In this context and leveraging the classification capabilities of the Model Order Selection rules, we devise penalized likelihood-ratio-based detection …
In recent years, Bayes filter methods in the labeled random finite set formulation have become increasingly powerful in the multi-target tracking domain. One of the latest outcomes is the Generalized Labeled Multi-Bernoulli (GLMB) filter which allows for stable cardinality and target state estimation as well as target identification in a unified framework. In contrast to the initial context of the GLMB filter, this paper makes use of it in the Track-Before-Detect (TBD) scheme and thus, avoids information loss due to thresholding and other data preprocessing steps. This paper provides a TBD GLM…
In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affecte…
We propose a robust likelihood-ratio test (LRT) to detect landmines and unexploded ordnance using a forward-looking ground-penetrating radar. Instead of modeling the distributions of the target and clutter returns with parametric families, we construct a band of feasible probability densities under each hypothesis. The LRT is then devised based on the least favorable densities within the bands. This detector is designed to maximize the worst-case performance over all feasible density pairs and, hence, does not require strong assumptions about the clutter and noise distributions. The proposed t…
In this paper, we devise adaptive decision schemes to detect targets competing against clutter and smart noise-like jammers (NLJ) which illuminate the radar system from the sidelobes. Specifically, the considered class of NLJs generates a pulse of noise (noise cover pulse) that is triggered by and concurrent with the received uncompressed pulse in order to mask the skin echo and, hence, to hide the true target range. The detection problem is formulated as a binary hypothesis test and two different models for the NLJ are considered. Then, ad hoc modifications of the generalized likelihood ratio…
Inter-carrier interference (ICI) poses a significant challenge for OFDM joint radar-communications (JRC) systems in high-mobility scenarios. In this paper, we propose a novel ICI-aware sensing algorithm for MIMO-OFDM JRC systems to detect the presence of multiple targets and estimate their delay-Doppler-angle parameters. First, leveraging the observation that spatial covariance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offse…
We consider the weak target detection problem with unknown parameter in colocated multiple-input multiple-output (MIMO) radar. To cope with the sheer amount of data for large-size systems, a multi-bit quantizer is utilized in the sampling process. As a low-complexity alternative to classic generalized likelihood ratio test (GLRT) for quantized data, we propose the multi-bit detector on Rao test with a closed-form test statistic, whose theoretical asymptotic distribution is provided to generalize the actual detection performance. Besides, we refine the design of quantizer by optimized quantizat…
In this paper, we address the problem of classifying data within the radar reference window in terms of statistical properties. Specifically, we partition these data into statistically homogeneous subsets by identifying possible clutter power variations with respect to the cells under test (accounting for possible range-spread targets) and/or clutter edges. To this end, we consider different situations of practical interest and formulate the classification problem as multiple hypothesis tests comprising several models for the operating scenario. Then, we solve the hypothesis testing problems b…
In this study, we analyze index modulation (IM) based on circularly-shifted chirps (CSCs) for dual-function radar & communication (DFRC) systems. We develop a maximum likelihood (ML) range estimator that considers multiple scatters. To improve the correlation properties of the transmitted waveform and estimation accuracy, we propose index separation (IS) which separates the CSCs apart in time. We theoretically show that the separation can be large under certain conditions without losing the spectral efficiency (SE). Our numerical results show that the IS combined ML and linear minimum mean squ…
This paper considers the design of tunable decision schemes capable of rejecting with high probability mismatched signals embedded in Gaussian interference with unknown covariance matrix. To this end, a sparse recovery technique is exploited to enhance the resolution at which the target angle of arrival is estimated with the objective to obtain high-selective detectors. The outcomes of this estimation procedure are used to devise detection architectures relying on either the twostage design paradigm or heuristic design procedures based upon the generalized likelihood ratio test. Remarkably, th…
Motivated by future automotive applications, we study some joint radar target detection and parameter estimation problems where the transmitter, equipped with a mono-static MIMO radar, wishes to detect multiple targets and then estimate their respective parameters, while simultaneously communicating information data using orthogonal time frequency space (OTFS) modulation. Assuming that the number of radio frequency chains is smaller than the number of antennas over the mmWave frequency band, we design hybrid digital-analog beamforming at the radar transmitter adapted to different operating pha…
In this paper, we address the problem of detecting multiple Noise-Like Jammers (NLJs) through a radar system equipped with an array of sensors. To this end, we develop an elegant and systematic framework wherein two architectures are devised to jointly detect an unknown number of NLJs and to estimate their respective angles of arrival. The followed approach relies on the likelihood ratio test in conjunction with a cyclic estimation procedure which incorporates at the design stage a sparsity promoting prior. As a matter of fact, the problem at hand owns an inherent sparse nature which is suitab…
A reconfigurable intelligent surface (RIS) is a nearly-passive flat layer made of inexpensive elements that can add a tunable phase shift to the impinging electromagnetic wave and are controlled by a low-power electronic circuit. This paper considers the fundamental problem of target detection in a RIS-aided multiple-input multiple-output (MIMO) radar. At first, a general signal model is introduced, which includes the possibility of using up to two RISs (one close to the radar transmitter and one close to the radar receiver) and subsumes both a monostatic and a bistatic radar configuration wit…
This paper presents a new joint radar and communication technique based on the classical stepped frequency radar waveform. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. A new signaling scheme is proposed in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. Considering the optimum maximum likelihood (ML) detection, the union bound and the nearest neighbour approximation on the communication block error probabil…
Analog neural networks are highly effective to solve some optimization problems, and they have been used for target localization in distributed multiple-input multiple-output (MIMO) radar. In this work, we design a new relaxed energy function based neural network (RNFNN) for target localization in distributed MIMO radar. We start with the maximum likelihood (ML) target localization with a complicated objective function, which can be transformed to a tractable one with equality constraints by introducing some auxiliary variables. Different from the existing Lagrangian programming neural network…
This paper addresses the standard generalized likelihood ratio test (GLRT) detection problem of weak signals in background noise. In so doing, we consider a nonfluctuating target embedded in complex white Gaussian noise (CWGN), in which the amplitude of the target echo and the noise power are assumed to be unknown. Important works have analyzed the performance for the referred scenario and proposed GLRT-based detectors. Such detectors are projected at an early stage (i.e., prior to the formation of a post-beamforming scalar waveform), thereby imposing high demands on hardware, processing, and …
We consider the problem of monostatic radar sensing with orthogonal frequency-division multiplexing (OFDM) joint radar-communications (JRC) systems in the presence of phase noise (PN) caused by oscillator imperfections. We begin by providing a rigorous statistical characterization of PN in the radar receiver over multiple OFDM symbols for free-running oscillators (FROs) and phase-locked loops (PLLs). Based on the delay-dependent PN covariance matrix, we derive the hybrid maximum-likelihood (ML)/maximum a-posteriori (MAP) estimator of the deterministic delay-Doppler parameters and the random PN…
Multiple-stage adaptive architectures are conceived to face with the problem of target detection buried in noise, clutter, and intentional interference. First, a scenario where the radar system is under the electronic attack of noise-like interferers is considered. In this context, two sets of training samples are jointly exploited to devise a novel two-step estimation procedure of the interference covariance matrix. Then, this estimate is plugged in the adaptive matched filter to mitigate the deleterious effects of the noise-like jammers on radar sensitivity. Besides, a second scenario, which…
