Sharif Digital Repository

A new method for the robust estimation of target orientation using measured radar cross section is proposed. The method is based on a Generalized Regression Neural Network (GRNN) scheme. The network is trained by the FFT modulus of bistatic radar cross section data sampled at the receiver positions. The target value to be trained is the angle between a defined target orientation and the incident wave. Results based on actual measurements are presented  

High resolution range profile (HRRP) is being known as one of the most powerful tools for radar target recognition. The main problem with range profile for radar target recognition is its sensitivity to aspect angle. To overcome this problem, consecutive samples of HRRP were assumed to be identically independently distributed (IID) in small frames of aspect angles in most of the related works. Here, considering the physical circumstances of maneuver of an aerial target, we have proposed dynamic system which models the short dependency between consecutive samples of HRRP in segments of the whole HRRP sequence. Dynamic system (DS) is used to model the sequence of PCA (principal component... 

One of the main tools in radar target recognition is high resolution range profile (HRRP). However, it is very sensitive to the aspect angle. One solution to this problem is to assume the consecutive samples of HRRP identically independently distributed (IID) in small frames of aspect angles, an assumption which is not true in reality. However, based on this assumption, some models have been developed to characterize the sequential information contained in the multi-aspect radar echoes. Therefore, they only consider the short dependency between consecutive samples. Here, we propose an alternative model, the segment model, to address the shortcomings of these assumptions. In addition, using a... 

A new fast and very accurate method for target detection in PCL radars is presented. This algorithm in noisy environment with SNR up to -60dB could provide the perfect and accurate detection. Presented initiative algorithm apply a special filter bank to the target signal and clean the additive noise of the environment from it and detect the multiple targets with different RCSs. © 2006 EuMA  

The high resolution range profile (HRRP) is known as the most important tool in radar target recognition. Mainly, the sensitivity to the aspect angle limits the performance of the related methods. To overcome this problem, Gaussian mixture distribution is used to model the short-term relation of consecutive HRRPs. In this work, an alternative dynamical system based method is proposed to overcome the limitations of recent methods in the field such as the independency assumption. Here, the performance of the method is tested by the data produced in an electromagnetic simulation for the radar return from an aerial maneuvering target. The results show the performance of the proposed method... 

One of the origins of error in the angle of arrival estimation of a signal is the response of the ground to the reflected waves from the target. In low-height target situations, this phenomenon can be so harrowing that it may result in much more error than thermal noise. In this article, two methods of fixed beam and double null are used as preliminary techniques to overcome the problem of low-altitude target angle estimation, and the optimum weights for beam-forming array radar are proposed. These beam-forming methods enable the radar to estimate the target angle precisely without the exact knowledge of the surface slope, whereas they are robust against diffuse and specular ground... 

This paper investigates the problem of maneuvering target tracking by using hybrid (intelligent/classical) methods. The adaptive capability of filters is known to be increased by incorporating a neural network into the filtering procedure. The proposed algorithm is implemented with two second-order Gaussian filters based on the current statistical model and a multilayer feedforward neural network. The two filters, which use the noise corrupted measurements of the target line of sight (LOS) angle, track the same maneuvering target in parallel. The neural network automatically considers all the state information of the two filters and adaptively adjusts the process variance of one of them to... 

The average likelihood ratio detector is derived as the optimum detector for detecting a target line with unknown normal parameters in the range-time data space of a search radar, which is corrupted by Gaussian noise. The receiver operation characteristics of this optimum detector is derived to evaluate its performance improvement in comparison with the Hough detector, which uses the return signal of several successive scans to achieve a non-coherent integration improvement and get a better performance than the conventional detector. This comparison, which is done through analytic derivations and also through simulation results, shows that the average likelihood ratio detector has a better... 

In this study, a detector/estimator is proposed for compressed sensing radars, which does not need to reconstruct the radar signal, and which works directly from compressive measurements. More precisely, through direct processing of the measurements, and without the need for reconstructing the original radar signal, the system performs target detection, and then estimates range, Doppler frequency shift and radar cross section in the presence of a Gaussian clutter. It can be seen that for large compression ratios, the detection performance and estimation quality is comparable with a common radar system while having a much lower data rate and with less computational load  

A robust target recognition method proposed based on recurrence plot and recurrence quantification analysis (RQA) to generate robust features against noise, target velocity and aspect angle from micro-Doppler (m-D) signatures. The proposed method is tested on simulated data of three different targets using multiclass support vector machine (MSVM) and classification rate of about 95 % is achieved. Also, effect of noise and coherent processing time (CPT) on classification rate is investigated  

In this paper, an iterative method for block-sparse recovery is suggested for target parameters estimation in a distributed MIMO radar system. The random sampling has been used as the sensing scheme in the receivers. The simulation results prove that the proposed method is superior to the other state-of-the-art techniques in the accuracy of the target estimation task. © 2016 IEEE  

High resolution range profile (HRRP) is a widely noted tool in radar target recognition. However, its high sensitivity to the target's aspect angle makes it necessary to seek solutions for this problem. One alternative is to assume consecutive samples of HRRP identically and independently distributed in small frames of aspect angles, an assumption which is not true in reality. Based on this simplifying assumption, some models, such as the hidden Markov model, have been developed to characterise the sequential information contained in multi-aspect radar echoes. As a result, these models consider only the short dependency between consecutive samples. Considering such issues, in this study, the... 

This study presents and analyses a novel joint detection and parameters estimation of multiple targets using a co-located multiple input/multiple output radar with moving platform, in the presence of clutter and background noise. A notable feature of the proposed approach is that clutter cancellation is independent of received data. Moreover, to decrease the computational load, the authors propose to perform the target detection and parameters estimation by directly processing on the compressive measurements as opposed to Nyquist samples. © The Institution of Engineering and Technology  

Multiple input multiple output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this work, the Cram'er-Rao lower bound (CRLB) for the angle and velocity estimator is employed... 

Several target position finding methods are proposed in various papers mainly regarding sensor networks. However, the problem of position finding in passive radar systems is somewhat different from the general case of sensor networks. Generally, in a passive radar system, there are few receivers located at short distances when compared with the it distance from the target. In this case, a problem known as geometric dilution of precision (GDP) occurs, which considerably increases the error of many proposed methods. This phenomenon can even make the position finding equations non-solvable. Regarding this fact, we have developed a least mean square error (LMSE) based method, which uses both the... 

One of the agents of error in angle of arrival estimation of signal is the response of the ground to the reflected waves from the target. In low-height target situations this phenomenon can be so tormenting that may cause much more error than thermal noise. In this article two methods of Fixed Beam and Double Null are used as preliminary techniques to overcome the problem of low-altitude targets angle estimation and the optimum weights for beam-forming array radar are proposed. The beam-forming methods enable the radar to estimate the target angle precisely while it is robust against diffuse and specular ground reflection components and the exact knowledge of surface slope is not required  

Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high-energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks. On one hand, higher angle and velocity estimation accuracy is required, but on the other hand, a lower number of antennas/pulses is desirable. To achieve such a compromise, in this paper, the Cramér-Rao lower bound (CRLB) for the angle and velocity estimator is employed...