Adaptive Matched Filtering for Sensing With Communication Signals in Cluttered Environments
🤖 AI Summary
In cluttered environments with overlaid communication signals, adaptive matched filtering (AMF) suffers from optimization difficulties due to the high stochasticity of instantaneous signal-clutter-plus-noise ratio (SCNR) and its dependence on data payload.
Method: This paper proposes optimizing the statistically averaged SCNR to balance performance and computational complexity. Theoretically, an asymptotically exact closed-form expression is derived using random matrix theory. Methodologically, fractional programming, KKT conditions, and manifold optimization are integrated to jointly design optimal waveforms and pilot sequences—under both unconstrained and rank-one constraints.
Contribution/Results: Two novel pilot mechanisms—data-dependent and data-independent—are introduced. Analytical and simulation results reveal that PSK and OFDM achieve higher average SCNR than QAM and SC/AFDM. Simulations confirm that the proposed scheme delivers high accuracy and low computational overhead in moderate-dimensional settings, significantly enhancing the robustness and practicality of integrated sensing and communication (ISAC) systems.
Application Category
📝 Abstract
This paper investigates the performance of the adaptive matched filtering (AMF) in cluttered environments, particularly when operating with superimposed signals. Since the instantaneous signal-to-clutter-plus-noise ratio (SCNR) is a random variable dependent on the data payload, using it directly as a design objective poses severe practical challenges, such as prohibitive computational burdens and signaling overhead. To address this, we propose shifting the optimization objective from an instantaneous to a statistical metric, which focuses on maximizing the average SCNR over all possible payloads. Due to its analytical intractability, we leverage tools from random matrix theory (RMT) to derive an asymptotic approximation for the average SCNR, which remains accurate even in moderate-dimensional regimes. A key finding from our theoretical analysis is that, for a fixed modulation basis, the PSK achieves a superior average SCNR compared to QAM and the pure Gaussian constellation. Furthermore, for any given constellation, the OFDM achieves a higher average SCNR than SC and AFDM. Then, we propose two pilot design schemes to enhance system performance: a Data-Payload-Dependent (DPD) scheme and a Data-Payload-Independent (DPI) scheme. The DPD approach maximizes the instantaneous SCNR for each transmission. Conversely, the DPI scheme optimizes the average SCNR, offering a flexible trade-off between sensing performance and implementation complexity. Then, we develop two dedicated optimization algorithms for DPD and DPI schemes. In particular, for the DPD problem, we employ fractional optimization and the KKT conditions to derive a closed-form solution. For the DPI problem, we adopt a manifold optimization approach to handle the inherent rank-one constraint efficiently. Simulation results validate the accuracy of our theoretical analysis and demonstrate the effectiveness of the proposed methods.
Problem
Research questions and friction points this paper is trying to address.
Optimizes adaptive matched filtering for sensing in cluttered environments with communication signals
Proposes shifting from instantaneous to statistical metrics to overcome computational and signaling challenges
Develops pilot design schemes and algorithms to enhance sensing performance and manage complexity
Innovation
Methods, ideas, or system contributions that make the work stand out.
Optimizes average SCNR using statistical metrics
Employs random matrix theory for asymptotic approximation
Proposes DPD and DPI pilot schemes with dedicated algorithms
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