Ambiguity Function Analysis for Orthogonal-LFM Waveform Based Multistatic Radar

Multistatic radar distribution geometry effects on parameter estimation accuracy

“…where τ and ν represent the time delay and Doppler frequency shift mismatch between the input signal to the matched filter and the nominal values used to characterise the design of the matched filter. Substitution of the waveform given by ( 1) into (2) gives the classical ambiguity function for a train of K identical pulses of the LFM waveform with a pulse repetition interval (PRI) of T P , as [17].…”

“…where τ and ν represent the time delay and Doppler frequency shift mismatch between the input signal to the matched filter and the nominal values used to characterise the design of the matched filter. Substitution of the waveform given by (1) into (2) gives the classical ambiguity function for a train of K identical pulses of the LFM waveform with a pulse repetition interval (PRI) of T P , as [17]. $$${\left\vert \chi \left(\tau ,\nu \right)\right\vert }^{2}=\left\vert \left(1-\frac{\left\vert \tau \right\vert }{T}\right)\frac{\sin \left(\pi \left(\nu T-{f}_{B}\tau \right)\left(1-\frac{\left\vert \tau \right\vert }{T}\right)\right)}{\pi \left(\nu T-{f}_{B}\tau \right)\left(1-\frac{\left\vert \tau \right\vert }{T}\right)}\right.$$$ $$$\cdot {\left.\frac{\sin \left(\pi \nu K{T}_{P}\right)}{K\,\sin \left(\pi \nu {T}_{P}\right)}\right\vert }^{2},$$$ …”

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

“…AF is a secondary time-frequency distribution that depicts the internal structure of the signal. The AF AF s (τ, ξ) is the joint representation of the signal on the time delay τ and the Doppler frequency shift ξ, which can be given by [13]…”

“…In order to ensure that the loss function of the update weight is the smallest when the global update is performed, a new parameter w f is set and used to record the network weight during the last global aggregation. Calculate the corresponding loss functions of w f and w (t) according to (13), and make the weight w f corresponding to the smallest loss function be According to the deformable convolutional network and the distributed federated learning system introduced in the previous section, the specific process of the intelligent identification method of RFFI based on the deformable convolutional network is introduced as follows: Firstly, each training participant performs the intelligent representation of radio frequency fingerprint, and inputs the intelligent representation of the radio frequency fingerprint into their respective local deformable convolutional networks, and then obtain their respective network weights after partial updates based on their respective data. All participants upload the network weights to the aggregation center.…”

High-Resolution Wideband Waveform Design for Sonar Based on Multi-Parameter Modulation

“…It is an effective tool for signal analysis studies and waveform design, which is determined only by the transmit waveform, reflecting the point target response, parameter estimation performance, target discrimination ability, and clutter suppression ability of the system operating in the optimal receive mode [20]. However, when theoretically analyzing the performance of the designed waveform, most of the literature still directly uses the narrowband ambiguity definition for the approximate theoretical derivation of the wideband waveform [21][22][23][24], and does not use the wideband ambiguity function definition for derivation and analysis. An essential way to improve system performance is to analyze and design the wideband waveform that possesses an optimal time-frequency resolution and reverberation suppression capabilities based on the wideband ambiguity function and corresponding signal processing techniques.…”