AdaRadar: Rate Adaptive Spectral Compression for Radar-based Perception

Abstract

Radar is a critical perception modality in autonomous driving systems due to its all-weather characteristics and ability to measure range and Doppler velocity. However, the sheer volume of high-dimensional raw radar data saturates the communication link to the computing engine (e.g., an NPU), which is often a low-bandwidth interface with data rate provisioned only for a few low-resolution range-Doppler frames. A generalized codec for utilizing high-dimensional radar data is notably absent, while existing image-domain approaches are unsuitable, as they typically operate at fixed compression ratios and fail to adapt to varying or adversarial conditions. In light of this, we propose radar data compression with adaptive feedback. It dynamically adjusts the compression ratio by performing gradient descent from the proxy gradient of detection confidence with respect to the compression rate. We employ a zeroth-order gradient approximation as it enables gradient computation even with non-differentiable core operations--pruning and quantization. This also avoids transmitting the gradient tensors over the band-limited link, which, if estimated, would be as large as the original radar data. In addition, we have found that radar feature maps are heavily concentrated on a few frequency components. Thus, we apply the discrete cosine transform to the radar data cubes and selectively prune out the coefficients effectively. We preserve the dynamic range of each radar patch through scaled quantization. Combining those techniques, our proposed online adaptive compression scheme achieves over 100x feature size reduction at minimal performance drop (~1%p). We validate our results on the RADIal, CARRADA, and Radatron datasets.

Figures (21)

• Figure 1: Adaptive codec with task-guided feedback control. We propose an adaptive codec that compresses high-dimensional range-Doppler data by pruning spectral domain coefficients. A feedback loop adaptively regulates the compression ratio, guided by the performance of downstream perception tasks.
• Figure 2: AdaRadar: Online rate-adaptive radar compression framework. Our proposed method introduces a feedback loop in which the proxy gradient is computed from the detection outputs to update the compression ratio adaptively. This avoids the need for backpropagation through the communication channel. The radar tensor is compressed using DCT, adaptive spectral pruning, and scaled quantization, then transmitted to the compute side. In an object-detection setting, the neural network produces detection results from decompressed radar data cubes. The loop uses proposed bounding boxes to estimate the proxy gradient, thereby updating the pruning rate.
• Figure 3: Radar-based perception system overview. An FMCW radar transmits linearly swept‑frequency chirps. The incoming echo is mixed with a copy of the transmitted chirp at the receiver, yielding a de‑chirped intermediate‑frequency signal that the ADC digitizes to form a raw radar tensor. Successive FFTs along the fast‑time and slow‑time axes convert this tensor into a range–Doppler cube. The large, raw radar tensor is transferred to the NPU over the power-hungry sensor-to-compute link for network inference.
• Figure 4: Motivation for spectral pruning and quantization. (a) The DCT coefficient magnitudes are clustered in the high-frequency bins. (b) Their histogram is sharply peaked, highlighting strong sparsity and clear opportunities for compression.
• Figure 5: Motivation for surrogate objective choice. (a) AP and (b) AR vs. confidence score yield a high correlation.