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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.

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.

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.