Nonlinear Frequency Modulated Waveform Optimization for a Cooperative Radar-Communication System

Mahipathi, Ashoka Chakravarthi; Srinath, Gunnery; Srihari, Pathipati; D'Souza, John; Jena, Paramananda

doi:10.1109/conecct52877.2021.9622544

Cited by 3 publications

(3 citation statements)

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“…Furthermore, in (55) the LFM-Gaussian pulse radar waveform is constrained by the signal-to-residual-noise ratio (γ) and sweep rate (b max ). 6) N LF M − P ulse The NLFM-pulse waveform is widely used due to its special characteristics like low side-lobe ratio, and high flexibility [14]. A second-order NLFM pulse with a polynomial phase is represented as,…”

Section: ) Lf M − P Ulsementioning

confidence: 99%

“…In NLFM pulse waveform, phase coefficients (p 1 , p 2 ) and signal-to-residual noise ratio (γ) are the waveform constraints (tunable waveform parameters). Here the optimized phase coefficients (p 1 , p 2 ) are evaluated based on our previous work [14]. From (58), it is evident that the measurement noise covariance can be minimized by optimizing the phase coefficients and maximizing the (γ) value.…”

Section: ) Lf M − P Ulsementioning

confidence: 99%

“…In contrast to the previous approach, [13] selects two distinct waveforms for communication and radar systems. Nevertheless, the performance enhancement of a JRCS in the aforementioned approach heavily relies on waveform constraints, as discussed in [14]. Another communication, [15], introduced a radar-centric method where optimizing the radar waveform enhances the performance of a coexisting radar-communication system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimum Waveform Selection for Target State Estimation in the Joint Radar-Communication System

Mahipathi,

Pardhasaradhi,

Gunnery

et al. 2024

IEEE Open J. Signal Process.

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The widespread usage of the Radio Frequency (RF) spectrum for wireless and mobile communication systems generated a significant spectrum scarcity. The Joint Radar-Communication System (JRCS) provides a framework to simultaneously utilize the allocated radar spectrum for sensing and communication purposes. Generally, a Successive Interference Cancellation (SIC) based receiver is applied to mitigate mutual interference in the JRCS configuration. However, this SIC receiver model introduces a communication residual component. In response to this issue, the paper presents a novel measurement model based on communication residual components for various radar waveforms. The radar system's performance within the JRCS framework is then evaluated using the Fisher Information Matrix (FIM). The radar waveforms considered in this investigation are rectangular pulse, triangular pulse, Gaussian pulse, Linear Frequency Modulated (LFM) pulse, LFM-Gaussian pulse, and Non-Linear Frequency Modulated (NLFM) pulse. After that, the Kalman filter is deployed to estimate the target kinematics (range and range rate) of a single linearly moving target for different waveforms. Additionally, range and range rate estimation errors are quantified using the Root Mean Square Error (RMSE) metric. Furthermore, the Posterior Cramer-Rao Lower Bound (PCRLB) is derived to validate the estimation accuracy of various waveforms. The simulation results show that the range and range rate estimation errors are within the PCRLB limit at all time instants for all the designated waveforms. The results further reveal that the NLFM pulse waveform provides improved range and range rate error performance compared to all other waveforms.

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Section: ) Lf M − P Ulsementioning

confidence: 99%

Section: ) Lf M − P Ulsementioning

confidence: 99%