Distributed Radar Target Tracking with Low Communication Cost

Abstract

In distributed radar, most of existing radar networks operate in the tracking fusion mode which combines radar target tracks for a higher positioning accuracy. However, as the filtering covariance matrix indicating positioning accuracy often occupies many bits, the communication cost from local sensors to the fusion is not always sufficiently low for some wireless communication channels. This paper studies how to compress data for distributed tracking fusion algorithms. Based on the K-singular value decomposition (K-SVD) algorithm, a sparse coding algorithm is presented to sparsely represent the filtering covariance matrix. Then the least square quantization (LSQ) algorithm is used to quantize the data according to the statistical characteristics of the sparse coefficients. Quantized results are then coded with an arithmetic coding method which can further compress data. Numerical results indicate that this tracking data compression algorithm drops the communication bandwidth to 4% at the cost of a 16% root mean squared error (RMSE) loss.

Keywords

distributed radar / distributed tracking fusion / data compression / K-singular value decomposition(K-SVD) algorithm / sparse coding / least square quantization (LSQ)