Optimisation of a generalised square-law detector

In many radar applications, knowing a target's present position (angle and range) relative to the radar is not enough. Often one must be able to predict the target's position at some future time. For that, we must also know the target's angular rate and its range rate. Range rate may be determined by one of two general methods. In the first, called range differentiation, the rate is computed on the basis of the change in the measured range with time. In the second and generally superior method, the radar measures the target's doppler frequency - which is directly proportional to the range rate. In this chapter, we will look at both methods briefly. We will then take stock of potential doppler ambiguities and see how they may be resolved.

Attentions have been focused on the moving target detection in a high-resolution sea clutter. This study commences with a proposal of median-based estimator to estimate the power spectrum of high-resolution sea clutter by the time series observed in adjacent range cells and time intervals. The estimator provides a robust estimation when just a few aberrant time series happen in observation. Based on the estimator, a block-adaptive clutter suppression filter (BACSF) is designed to suppress the clutter prior to the pulse integration. Then, the residual clutter, the output of the BACSF, is modelled as spherically invariant random vector. Upon applying an adaptive normalised matched filter (ANMF) to the residual clutter, a residual clutter's ANMF detector is derived. Moreover, in high-resolution radar background, considering that the approximately stationary intervals of sea clutter and residual clutter are much shorter than the coherent processing interval, another heuristic block-ANMF detector is proposed. It can integrate more pulses and can achieve better performance than the ANMF detector does. This study concludes with the experiments of simulated target against the real sea clutter. The experimental results demonstrate that, when target's Doppler frequency is beyond strong clutter region, the ANMF detector and block-ANMF detector perform better in the residual clutter than in the clutter.

For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.

In high-frequency surface wave radar (HFSWR) systems, clutter is a common phenomenon that causes objects to be submerged. Space-time adaptive processing (STAP), which uses two-dimensional data to increase the degrees of freedom, has recently become a crucial tool for clutter suppression in advanced HFSWR systems. However, in STAP, the pattern is distorted if a clutter component is contained in the main lobe, which leads to errors in estimating the target angle and Doppler frequency. To solve the main-lobe distortion problem, this study developed a clutter-suppression method based on beam reshaping (BR). In this method, clutter components were estimated and maximally suppressed in the side lobe while ensuring that the main lobe remained intact. The results of the proposed algorithm were evaluated by comparison with those of standard STAP and sparse-representation STAP (SR-STAP). Among the tested algorithms, the proposed BR algorithm had the best suppression performance and the most accurate main-lobe peak response, thereby preserving the target angle and Doppler frequency information. The BR algorithm can assist with target detection and tracking despite a background with ionospheric clutter.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

Radar imaging of noncooperative targets is an interesting application of all-weather high-resolution coherent radars. However, these images are usually blurred when using the standard range-Doppler algorithm, if a long coherent processing interval (CPI) is used, and motion compensation techniques are hence necessary to improve imaging quality. If the CPI is reduced enough, target scatterers do not migrate of resolution cells and their corresponding Doppler frequencies are constant. Hence, for a short CPI, motion compensation is not longer necessary, but Doppler resolution gets degraded. In that case, superresolution algorithms may be applied. Here, we compare the superresolution-based focusing techniques with motion compensation-based methods. Our conclusion is that imaging quality after employing the superresolution approaches is not improved and, consequently, the use of motion compensation-based approaches to focus the radar images cannot be circumvented. Simulated and real data from high-resolution radars have been used to make the comparisons.

Long-time coherent integration can remarkably improve the detection and motion parameter estimation ability of radar for maneuvering targets. Unfortunately, the linear range migration, quadratic range migration (QRM), and Doppler frequency migration (DFM) within the coherent processing interval seriously degrade the target detection and parameter estimation performance. In this regard, a novel coherent integration approach based on frequency autocorrelation function (FAF) and Lv’s distribution (LVD), i.e., FAF–LVD, is proposed in this paper. The QRM is firstly removed by the second-order keystone transform. Then, the FAF with a variable delay is introduced to remove the DFM, followed by the scaled Fourier transform to achieve coherent integration. Finally, the acceleration and velocity are estimated simultaneously by LVD. Analysis demonstrates that FAF–LVD could be efficiently implemented via complex multiplications, the fast Fourier transform (FFT) and inverse FFT. Detailed comparisons with several representative methods indicate that FAF–LVD achieves a better balance among computational complexity, detection ability, and parameter estimation performance. Extensive simulations are presented to validate the effectiveness of FAF–LVD method.

A millimeter wave (MMW) pseudo random code continuous wave (PRC-CW) radar utilizing Doppler compensation is introduced. The novel hybrid transmitted waveform used in this radar combining single frequency pulse with pseudo random binary phase-coded signal is presented which makes consequent target detection through pulse compression independent of Doppler frequency. Doppler frequencies of targets are firstly estimated based on interpolated FFT in the time span of single frequency pulse and the Doppler compensated signal can be constructed. The effect of Doppler mismatch on the correlation detection can be eliminated by processing the compensated signal using conventional pulse compression-FFT, which makes the radar capable of short-range high-speed target detection. The theoretical analysis and simulation result demonstrate the validity of this technique.

The present study investigated and compared the results of establishing composite scores based on the five weighting schemes of the equally-weighted model, the reliability weighting model, the standard deviation (or SD) weighting model, the error of measurement weighting model, and the effective score point model. The purpose of this study was to seek optimal relative weights in forming the best composite possible, as well as to offer more information about the various weighting schemes considered on the different measurement qualities of the tests. The five tests of the Basic Competence Test (BCTEST) were employed for exploration in this research. A random sample of 5,000 examinees drawn from the data obtained from the 2005 test administration was used. To evaluate the various weighting schemes, this study examined the statistical and psychometric properties of the test scores and the weighted composite scores, the effective contributions of individual tests to the composites, as well as the impact on the admission decisions. The findings indicated that the reliability coefficients of the variously formed composite scores were all very high. However, with regard to the effective contributions, the results were very different among the various weighting schemes. Overall, the SD and the error of measurement weighting models seemed to perform better in establishing the composites than the reliability or the effective score point model, although there still remained the issue of inequality of the effective contributions for both the SD and the error of measurement models. Results from this study should advance the understanding of weighting issues while combining individual test components into composites.

This article presents a comprehensive survey on the integration of Generative Artificial Intelligence (AI) technologies in radar applications, with a focus on enhancing radar data processing and system capabilities. Generative AI techniques, particularly Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are explored for their potential to address persistent challenges in radar technology such as noise management, data augmentation, and target classification. The study investigates how GANs can generate synthetic radar datasets, aiding in model training when actual data is scarce, and how VAEs contribute to signal processing by denoising and reconstructing accurate radar signals. The analysis includes case studies on clutter suppression, radar data augmentation, beam blockage correction, and data fusion, highlighting the transformative impact of Generative AI on radar systems. This paper aims to provide insights into the current advancements and future directions of Generative AI applications in radar, suggesting that these technologies hold significant promise for improving the accuracy and efficiency of radar systems in diverse and dynamic environments.

Mechanical vibration or rotation of a target or structures on the target may induce additional frequency modulations on the returned radar signal which generate sidebands about the target's Doppler frequency, called the micro-Doppler effect. Micro-Doppler signatures enable some properties of the target to be determined. In the paper, the micro-Doppler effect in radar is introduced and the mathematics of micro-Doppler signatures is developed. Computer simulations are conducted and micro-Doppler features in the joint time - frequency domain are exploited.

Inland water ways are significant component of the nation's marine transportation systems. Congo River Basin plays an active role in the economy of Democratic Republic of Congo (DRC) and other surrounding countries such as Central Africa Republic (CAR) and Congo Republic (RC). This inter-countries waterway carries approximately two third of the national's cargo volume to and fro hinterland. Up till now there is little or no regulation on the management of marine traffic on this important inland waterway. The problem of traffic management on Congo River is becoming an increasing threat to the transportation of goods by the ships, barges and safety of passenger lives. Marine radar systems has been deemed as possible solution that will monitor and track traffic situation on this important inland water way. A radar system provides a surveillance system that increases intelligent capacity of what transpires on this great waterway. It also provides real-time information and stored captured radar images which will be applied to solve traffic problems. To investigate this, radar simulation model will be setup to depict the traffic flow and the consequences of the presence or absence of surveillance radar. The goal of this research is to improve traffic coordination, safety, management and improve intelligent capability with the use of Marine Radar monitoring system to control and alleviate traffic management bottleneck experienced on Congo River. It will further demonstrate economic efficiency of the radar surveillance system on Congo River Basin. The proposed traffic model which will be based upon research findings will describe in details the operational approach of alleviating traffic management problem on Congo River Basin.

The paper considers the features of tracking air objects in existing information networks of radar systems for monitoring airspace. It is shown that the tracking is carried out according to the information of the primary radar surveillance systems, and the secondary radar surveillance systems are used as sources of additional radar information. But the transition to automatic dependent surveillance implies the mandatory use of only request radar surveillance systems. Therefore, the problems of formulating methods and algorithms for tracking air objects based on information from secondary radar surveillance systems are relevant. The specifics of the construction and operation of secondary radar surveillance systems differ significantly from primary radar surveillance systems. The work carried out the synthesis and analysis of the structure of the tracks detector of air objects by interrogating radar systems for monitoring the airspace, namely: a comparative analysis of the quality of identifying the tracks of air objects was carried out; the quality of information support to consumers of the airspace control system with the proposed structure was improved in comparison with the used information processing structure; it is shown that the quality of information support for consumers has more preferable indicators when using the signal processing method during acquisition with subsequent information merging; the influence of the readiness factor of aircraft transponders of interrogative radar systems on the quality of information support for consumers of the airspace control system was evaluated.

OF THE DISSERTATION Adaptive OFDM Radar for Target Detection and Tracking by Satyabrata Sen Doctor of Philosophy in Electrical Engineering Washington University in St. Louis, December 2010 Research Advisor: Dr. Arye Nehorai We develop algorithms to detect and track targets by employing a wideband orthogonal frequency division multiplexing (OFDM) radar signal. The frequency diversity of the OFDM signal improves the sensing performance since the scattering centers of a target resonate variably at different frequencies. In addition, being a wideband signal, OFDM improves the range resolution and provides spectral efficiency. We first design the spectrum of the OFDM signal to improve the radar’s wideband ambiguity function. Our designed waveform enhances the range resolution and motivates us to use adaptive OFDM waveform in specific problems, such as the detection and tracking of targets. We develop methods for detecting a moving target in the presence of multipath, which exist, for example, in urban environments. We exploit the multipath reflections by utilizing different Doppler shifts. We analytically evaluate the asymptotic performance of the detector and adaptively design the OFDM waveform, by maximizing the noncentrality-parameter expression, to further improve the detection performance. ii Next, we transform the detection problem into the task of a sparse-signal estimation by making use of the sparsity of multiple paths. We propose an efficient sparse-recovery algorithm by employing a collection of multiple small Dantzig selectors, and analytically compute the reconstruction performance in terms of the `1-constrained minimal singular value. We solve a constrained multi-objective optimization algorithm to design the OFDM waveform and infer that the resultant signal-energy distribution is in proportion to the distribution of the target energy across different subcarriers. Then, we develop tracking methods for both a single and multiple targets. We propose a tracking method for a low-grazing angle target by realistically modeling different physical and statistical effects, such as the meteorological conditions in the troposphere, curved surface of the earth, and roughness of the sea-surface. To further enhance the tracking performance, we integrate a maximum mutual information based waveform design technique into the tracker. To track multiple targets, we exploit the inherent sparsity on the delay-Doppler plane to develop an computationally efficient procedure. For computational efficiency, we use more prior information to dynamically partition a small portion of the delay-Doppler plane. We utilize the block-sparsity property to propose a block version of the CoSaMP algorithm in the tracking filter.

In this paper, an intelligent algorithm for clustering, intra-pulse modulation detection and separation and identification of overlapping radar pulse train is presented. In most cases, based only on primary features of incoming radar signals, the modern electronic intelligence system cannot recognize the different devices of the same type or class. A very important role is played by Measurement and Signature Intelligence. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. The information such as Pulse Repetition Interval (PRI), Angle of Arrival (AoA), Pulse Width (PW), Radio Frequencies (RF), and Doppler shifts are not usable. In the proposed algorithm, for clustering of overlapping pulses received from self-organization neural network SOFM (due to its high accuracy in comparison with other neural networks, such as CLNN and neural networks (Fuzzy ART), and for detecting intra-pulse modulation type, matrix method, and for identifying the radar type, RBF neural network have been used. The simulation results of the proposed algorithm shows that in the presence 5% noise and 5% missing pulse, the accuracy of the clustering part of the proposed algorithm is equivalent to 91/8%, intra-pulse modulation recognition accuracy is 98%, the detection accuracy is 2/99%, and the total output of the algorithm precision is 89/244%, respectively.