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Detection and classification of Low Probability of Interception (LPI) radar signals is one of the most important challenges in electronic warfare (EW), since there are limited methods for identifying these type of signals. In this paper, a radar waveform automatic identification system for detecting and classifying LPI radar is studied, and accordingly we propose a method based on deep learning networks to detect and classify LPI radar waveforms. To this end, the GoogLeNet architecture as one of the well-known convolutional neural networks (CNN) is utilized. We employ the Short Time Fourier Transform (STFT) for time-frequency analysis in order to construct the entry image for proposed method 1,2 (improved the GoogLeNet and AlexNet networks) to recognize offline training and online recognition. After the training procedure with the supervised data sets the proposed method 1,2 can detect and classify nine modulation types of LPI radar, including LFM, poly-phase (P1, P2, P3, P4) and poly-time (T1, T2, T3, T4) waveforms. The numerical results for proposed method 1, show considerable accuracies up to 98.7% at the SNR level of –15 dB, which outperforms the existing methods.

In this chapter, the problem of joint transmit code and receive filter design has been considered to optimize the achieved SINR of extended targets in the presence of signal -dependent interference. Due to uncertainties about the TIR, a worst case optimization framework has been proposed over two different T1R uncertainty sets. The former contains T1R's samples corresponding to some TAAs, whereas the latter is equivalent to bounding the actual T1R within a spherical set. Additionally, a PAR constraint has been imposed on the transmitted waveform so as to ensure its practical implementation. The obtained non -convex optimization problem has been divided into two sequential relaxed SDP problems. Their corresponding solutions have then been exploited to synthesize the code -filter pair via randomization methods.

It is presumed that using multiple antennas at the transmit and receive sides can improve the radar's performance. One such improvement can be achieved in the detection of extended targets. However, in such multiple-input multiple-output systems, proper design of the transmit signals is an obstacle that can have significant influence on the performance. In this study, enforcing the peak-to-average-ratio (PAR) constraint, the authors consider the problem of transmit space-time code and receive space-time filter design of such a system detecting extended targets. In addition, the power allocation among the different antennas is another degree of freedom that should be optimised under a total power constraint. Here, the authors derive a constrained optimisation procedure for transmit code, receive filter and transmit powers, which sequentially improves the signal-to-interference plus noise ratio of the system.

The authors deal with the robust design of multiple-input–multiple-output (MIMO) space–time transmit code (STTC) and space–time receive filter (STRF) for a point-like target embedded in signal-dependent interference. Specifically, they assume that the radar exploits knowledge provided by dynamic environmental database, to roughly predict the actual scattering scenario. Then, they devise an iterative method to optimise the (constrained) STTC and the (constrained) STRF which sequentially improves the worst-case (over interfering scatterers statistics) signal-to-interference-plus-noise ratio (SINR). Each iteration of the algorithm is handled via solving two (hidden) convex optimisation problems. The resulting computational complexity is linear with the number of iterations and polynomial with the sizes of the STTC and the STRF. At the analysis stage, they assess the performance of the proposed algorithm in terms of the achieved SINR. They show that properly exploiting the spatial degrees of freedom offered by the MIMO system, it is possible to obtain considerable SINR gains with respect to the conventional single-input–single-output system. Moreover, their results highlight the capability of the proposed method to robustify the performance of the designed system against possible knowledge inaccuracies.

An important tool for assessing a radar's performance is the ambiguity function. On the other hand, many different schemes have been designed for the transmit waveforms of a MIMO radar. For each one, a criterion has been chosen to improve the performace, e.g. achieving better missed detection. In this paper, considering the ambiguity function as the benchmark, we propose a waveform design scheme, which optimizes the ambiguity function of the MIMO radar system.

In a MIMO (Multiple Input Multiple Output) radar system, proper design of transmit signal and receive filter is an advantageous tool to improve the detection performance. Thus, in this paper, we consider the problem of transmit code and receive filter design, in order to maximize the Signal to Noise Ratio (SNR) at the receiver while enforcing a similarity constraint between the transmit space-time code (STC) and a reference STC which has a desirable ambiguity function. We will show that by solving such constrained optimization problem through successive iterations, our proposed method leads to satisfactory results.

Based on the sparsity idea, the recently developed technique of compressive sensing (CS) is shown to be able to reduce significantly the need for huge amount of data and high sampling rates. On the other hand, moving target indication (MTI) processing has long been known in the traditional pulse radars to be able to detect weak signals of moving targets. The main drawback of the conventional approaches is the lack of accuracy in Doppler estimation; most of them suffer from low resolution to separate multiple targets from each other with low number of pulses. In this paper, we employ the CS technique to do the MTI processing in a radar system which employs measurements (echo pulses) efficiently to detect the target and estimate its Doppler. The two stages of the proposed algorithm, calculate the measurement matrix and perform the detection and estimation procedure. The simulations verify the proposed method, indicating the effectiveness of the CS technique in various applications.

In this paper, we devise a joint code-filter design method to optimize the signal-to-interference-plus-noise ratio (SINR) associated with the colocated MIMO radars. We consider constant-modulus wavefrom design for moving target detection and propose a cyclic algorithm to deal with the design problem.

We propose a novel robust design method to jointly optimize the radar transmit code and receive filter, exploiting the signal-to-interference-plus-noise ratio (SINR) at the receiver end as design figure of merit. We confer robustness to our method against uncertainties on the target impulse response (TIR) using a worst-case optimization approach. Precisely, we model the uncertainty set as a finite collection of TIRs, obtained sampling the actual TIR at some aspect angles. We further enforce a peak-to-average power ratio (PAR) constraint to the transmit code, which is very important for radar applications, where the transmitter operates close to saturation. The design problem is tackled using a sequential optimization procedure alternating between two semi-definite programming (SDP) problems, followed by randomization steps. Our numerical results highlight the robustness and applicability of the proposed method.

In this paper, we consider the problem of transmit code design to optimize the detection probability of an extended target embedded in Gaussian signal-dependent interference. We model the target with a priori known target impulse response (TIR) structure along with an unknown reflection factor. As is known, the performance of the generalized likelihood ratio test (GLRT) detector, for the case at hand, monotonically increases with the signal-to-interference-plus-noise ratio (SINR). Hence, we deal with the code design problem via maximizing the SINR. We also enforce a peak-to-average power ratio (PAR) constraint on the sought code. We devise a cyclic method to tackle the highly non-convex design problem. Numerical results highlight the effectiveness of the proposed method to improve the detection performance of the system.

Efficient combination of multiple input multiple output (MIMO) and passive coherent location (PCL) ideas is expected to improve performance of localisation schemes. While using multiple antennas at transmit and receive sides provides spatial diversity, it is possible to obtain similar performance by using multiple transmitters, already transmitting standard signals (such as digital TV) in the environment (also known as illuminators of opportunity). However, in this case, it is not always possible to ensure that signals of different transmitters are orthogonal to each other. In such cases, resolving signals of multiple transmitters reflected from multiple objects is not a trivial problem. One such scenario arises when transmitters of a single frequency network (SFN) are used. Consequently, in order to obtain the desired diversity gain, it is necessary to develop proper techniques to assign each echo arriving at the receiver to a given transmitter and a specific object to be localised. In this paper, we propose a scheme that addresses the association problem in an efficient way. Also, we consider the case of multiple antennas only at the transmit side while using a single receiver, which is the case of a multi-input single-output (MISO) system, a subset of MIMO systems, in general.